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Highlights: [•] Theoretically placing strategic communication management within the DRR paradigm. [•] Defining DR4 in the South African environment. [•] Indicating the need for DR4 communication in the South African environment. [Copyright &y& Elsevier]

Highlights: [•] Input Variable Selection (IVS) helps when processing system inputs is computationally heavy. [•] A framework to accommodate high level perspective of different approaches to IVS is provided. [•] Sensitivity analysis (SA) helps IVS in heterogeneous input variables with time constraint. [•] Event-based SA proves fit by its application in an industrial drilling disaster prevention problem. [Copyright &y& Elsevier]

This paper proposes a methodology for computing, visualizing, and analyzing critical decision boundaries for the selection of shortest paths in a given environment. Decision boundaries are defined as the points in a map from which two or more different shortest paths exist towards a destination. This paper introduces the problem of visualizing their evolution, taking into account moving obstacles, moving goals, and as well multiple goals. The proposed visualizations enable analyzing which paths should be taken and at which departure times, such that a destination can be reached by the shortest possible path when taking into account a moving target or time-varying areas to be avoided. The proposed techniques are also applied to the analysis and improvement of exit placement in a given environment, in order to improve the evacuation flow in emergency situations. [Display omitted] • This research presents a unique method for detecting decision boundaries in a given environment, based on the analysis of the generator points of the Shortest Path Map (SPM) rather than employing traditional scalar field topological methods relying on cell neighborhood information which can be affected by the representation resolution. • The proposed approach introduces tools and techniques to visualize the evolution of decision boundaries when considering dynamically-changing obstacles and targets, and to design exit placement to equalize the escape flow distribution. • This novel approach supports decision-making applications related to navigation and environment modeling in emergency evacuation planning. • By analyzing and visualizing SPM decision boundaries, the lengths of globally-optimal Euclidean shortest paths are taken into account, instead of grid-based accumulated distances used in other approaches. [ABSTRACT FROM AUTHOR]

Urban areas in the US and around the globe are facing increasingly complex resilience challenges. Among the components of the "urban system," transportation networks are among the most critical facilitators that support the lives, interactions, and dynamics of urban dwellers. They are essential to the well-being of the society not only under business-as-usual conditions, but also during times of disaster for the entire response and recovery timeline. This paper introduces CRAFT (Comprehensive Resilience Assessment Framework for Transportation Systems in Urban Areas), which is designed to achieve holistic analyses of transportation disruptions by addressing the many shortcomings and research gaps in this domain. The framework couples a novel structure-specific modeling methodology with a high-fidelity metropolis-scale travel demand model based on real socioeconomic data, and produces results, which, in turn, serve as input for a state-of-the-art socioeconomic impact analysis methodology that is based on computable general equilibrium (CGE) analysis. By the virtues of its data-intensive, model-based, and cross-disciplinary nature, CRAFT aims to capture and incorporate many details that are usually neglected in traditional approaches, and generates resilience insights at 3 levels: (1) system component level (e.g., damages to bridges, tunnels and information on component recovery), (2) system level (e.g., road network disruptions, reconfiguration of traffic and network level functionality) and (3) regional economic level (e.g., impacts on regional GDP, employment, economic resilience). The objective of this paper is to introduce CRAFT and to demonstrate the workings of its first coupling between the hazard and transportation modules through a case study on the Greater Los Angeles Area. [ABSTRACT FROM AUTHOR]

Despite significant improvements in design methodologies, dams and water-retaining structures failures continue to occur. Dams' failure analysis plays a crucial role in the development of dam safety planning and emergency action. Since the Grand Ethiopian Renaissance Dam (GERD) has been built, there have been many concerns about its safety and its effects on downstream countries in case of its failure. In this paper, the GERD break was modeled by using the USACE Hydrologic Engineering Center's River Analysis System (HEC-RAS). Two dimensions model under different failure scenarios was suggested. Moreover, outflow hydrographs and flood inundation maps were presented due to dam breach. Finally, it was concluded that, in case of catastrophic failure, flow depths may vary from 3 to 10 m in some residential areas, such as Khartoum. Furthermore, the water surface elevation may reach 184 m above msl (mean sea level) in Lake Nasser in case of dam failure with a fully stored GERD reservoir. Moreover, the maximum flow would reach 325,928 m3/sec which is more than 21.5 times the total capacity of the dam spillway. Finally, the paper's outcomes may assist decision-makers in developing alternate plans to deal with the dangers of GERD break. [ABSTRACT FROM AUTHOR]

The term "crowd counting" refers to the practise of counting the number of people present in a certain area. Urban planning, medical services, emergency preparedness, public security, strategic planning, and defence all seem to be domains where this method may be used. Occlusion, size and perspective distortion, and non-uniform distribution are all problems that crowd counting approaches face. As the population density grows, so does the complexity of the calculations. Great advances in deep convolution neural networks (CNNs) and datasets are largely responsible for the tremendous development in crowd count approaches seen in the past few years. In this paper we assess recent efforts and provide a complete evaluation of modern deep learning-based crowd counting systems. This paper discusses some classic and deep learning-based crowd counting approaches. We examine detection-based, regression-based, and classic density estimation approaches briefly. For the purpose of estimating the crowd density and count for the provided crowd scene image, we have evaluated the recent 10 publications on crowd counting using deep learning. We also go through the most widely used datasets. In conclusion, these investigations demonstrated a high degree of precision and offered good illustrations of the potential of AI in crowd counting. From paper to paper, there were significant differences in the approaches and algorithms used to address the crowd counting and density mapping challenge. We also examine the possible uses of crowd counting as well as the difficulties associated with it. As a result, fresh study is being conducted in this area. [ABSTRACT FROM AUTHOR]

The identification of events from social media platforms such as Twitter (now known as X) is a hot research problem. It has applications in diverse domains such as journalism, marketing, public safety, crisis management and disaster response. The process includes the identification, monitoring, and analysis of events or incidents while they are being discussed or reported on Twitter. When it comes to identifying events from tweets (i.e. feeds from Twitter), many of the currently available event detection methods mainly rely on keyword burstiness features or structural changes in the network. However, due to the intricate characteristics of tweets and the ever-changing nature of events, they frequently fail to recognise noteworthy occurrences before they become trending. Moreover, these methods face difficulties when it comes to capturing the evolving characteristics of events with limited or insufficient contextual information. In this paper, we propose a window-based tweet-processing method called EDTBERT for detecting events and tracking the evolution of events over time. Our proposed method utilizes the structural and semantic affinities that exist among words in tweets. The method starts by generating graph of tweets, where tweets are represented as nodes, and edges are the similarities between tweets. The method utilizes overlapping hashtags and named entities to capture the structural relationship between tweets. Additionally, a pre-trained sentence transformer model, specifically BERT, is employed to collect contextual knowledge and find semantically similar tweets. Next, the graph clustering technique is employed to identify optimized event clusters. Subsequently, our method generates chain of event clusters for each event to track the evolving variation of the event over time by utilising the "Maximum-Weight Bipartite Graph Matching" (MWBGM) algorithm. The effectiveness of our approach is assessed using standard Tweet Datasets. Our evaluation demonstrates that our approach outperforms the baseline approaches. [ABSTRACT FROM AUTHOR]

This research paper presents an analytical approach to earthquake time-series forecasting using the Seasonal Autoregressive Integrated Moving Average with Exogenous Variables (SARIMAX) models. The objective of this study is to investigate the effectiveness of the SARIMAX model in earthquake forecasting by considering relevant exogenous variables, such as historical seismic activity, geological characteristics, and geodetic measurements. We start introducing the SARIMAX models, explaining its mathematical formulation and the incorporation of exogenous variables. The research methodology involves collecting earthquake time-series data from seismological databases and preprocessing the data for analysis. Various SARIMAX models are constructed and evaluated using statistical measures, such as root mean square error and mean absolute error, to assess their forecasting accuracy. Additionally, the impact of different exogenous variables on the predictive performance of the models is analyzed. The results of this research contribute to the field of earthquake prediction by demonstrating the applicability and efficacy of the SARIMAX model in capturing the temporal patterns and dynamics of seismic events. The practical reason of the results provides valuable information for decision-makers and stakeholders involved in disaster preparedness and mitigation strategies. [ABSTRACT FROM AUTHOR]

The research uniquely focuses on advancing the field of fuzzy sets, specifically proposing a novel approach using Hexagonal Type-2 Fuzzy Variable (HT2FV), to handle higher degrees of uncertainty. Flooding, a recurrent and impactful phenomenon, necessitates a robust approach to effective disaster management. The study delves into the humanitarian supply chain dynamics during movement crises and flood catastrophes, introducing a novel methodology to harness the potential of HT2FV. Central to the paper is the innovative defuzzification concept of Critical Value (CV) reduction, strategically employed to eliminate fuzzy components within HT2FV, transforming it into crisp data. The research extends its novelty by proposing a comprehensive approach to distributing relief materials, employing a Multi-objective, Multi-level, Multi-modal (MOMLMM) approach, with a primary focus on boats for inundated areas. Significantly, the study aims to optimize the allocation of resources, minimize time and cost under uncertain environments, and enhance disaster relief operations. The paper is the first to use new hexagonal fuzzy numbers (HFNs) and focuses on how they can be used to sort vulnerable areas using the fuzzy Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods of the Multi-Criteria Decision-Making (MCDM) process. A case study of the 2022 Assam flood is looked at and solved using the Weighted Sum (WS), Neutrosophic Compromise (NC), and Goal Programming (GP) systematically on the LINGO solver to see if the suggested method can be used. Further, sensitivity analysis is employed to establish optimal credibility levels to validate the proposed methods. A real-life case study lends practicality, substantiating the approach's efficacy. At the same time, comprehensive comparative analysis compares and contrasts findings from different studies to synthesize existing knowledge and identify future scope, contributing significantly to the field of fuzzy sets. [ABSTRACT FROM AUTHOR]

This paper proposes an innovative approach towards real-time earthquake forecasting in Chile by integrating intelligent technologies and machine learning methods. Earthquakes pose significant risks to communities and infrastructure in Chile, making accurate and timely forecasting crucial for disaster preparedness and mitigation. Traditional forecasting methods have limitations in providing real-time insights into seismic activity. In contrast, intelligent technologies such as artificial intelligence (AI) and machine learning offer promising avenues for enhancing prediction accuracy and speed. A new earthquake forecasting method using a modified clustering approach LMSCAN(Local Maxima-based Spatio-Cluster Analysis Network) and enhanced neural network time series analysis LSTM-IC (Long Short Term Memory Inverse Correlation) is presented in this paper. This neural network-based technology has been utilized to forecast earthquakes in the Chile region, one of the countries with the largest seismic activity. This study explores the integration of intelligent systems with machine learning algorithms to analyze seismic data and predict earthquake occurrences in Chile. By leveraging historical seismic data, sensor networks, and advanced predictive models, our approach aims to provide timely warnings and insights into seismic events, thereby improving disaster response and resilience. The proposed framework holds the potential to revolutionize earthquake forecasting by enabling real-time monitoring and proactive measures to safeguard communities and infrastructure in Chile and beyond. The remarkable 95 % accuracy achieved by this model is a testament to its exceptional learning process, which sets it apart from other models. Its ability to learn and adapt to new data is unparalleled, allowing it to forecast incredibly precisely and produce highly reliable results. [ABSTRACT FROM AUTHOR]

The evaluation of communities' vulnerability to prolonged power outages offers valuable insights for prioritizing improvements in infrastructure resilience, thereby alleviating societal consequences. This study proposes a data-driven approach aiming at developing a Social Vulnerability Index (SoVI) to prolonged power outages leveraging three county-level datasets in California including (1) demographic features, (2) power outage factors, and (3) backup power factors. Furthermore, the study conducts a sensitivity analysis on three distinct datasets under two scenarios (Scenario 1: the current SoVI in 2022, Scenario 2: the prediction of SoVI in the year 2030). The results of Scenario 1 indicate that the counties with more affected customers, the number of power outages, and less education attainment tend to be more vulnerable to power outages in 2022. Scenario 1 reveals that the number of affected customers and power outages are the primary features influencing around 29% and 18% of counties, while educational attainment, public Electric Vehicles (EVs) chargers, and homes with rooftop photovoltaic (PV) substantially impact approximately 32%, 11%, and 8% of counties, respectively. However, in Scenario 2, crucial factors affecting the anticipated SoVI in 2030 include public EV chargers, houses with rooftop PV, power outages, and adults living alone. In contrast to Scenario 1, the prevalence of adults living alone has emerged as a notable factor impacting SoVI in 2030, while both scenarios underscore the pivotal role of EV chargers in influencing SoVI concerning power outages. The proposed SoVI facilitates informed policy decisions and infrastructure improvements in energy resilience, resource allocation, and disaster preparedness, contributing valuable insights for targeted interventions in these domains. • A data-driven approach was proposed to quantify the social vulnerability index (SoVI) to power outages. • This paper applies Principal Component Analysis (PCA) to identify the most correlated features from county-level datasets. • The paper conducts a sensitivity analysis to predict SoVI in2030 and evaluate how counties' dynamics change over time. • This paper provides insights into how policymakers can reduce the social vulnerability to power outages in California. [ABSTRACT FROM AUTHOR]

Considerable research has been conducted in over a decade on traffic management systems that utilize Wireless Sensor Networks (WSNs) to mitigate congestion and prioritize emergency vehicles. The traffic management is becoming a wide interesting area for both academic and industrial researchers. The real-time traffic management is a dynamic scheme and is very challenging to provide an accurate signalling time and priority for a specific vehicle. This paper introduces a novel emergency vehicle information passing system that utilizes Radio Frequency (RF) sensors. This research study presents an innovative system for transmitting emergency vehicle information, which makes use of Radio Frequency (RF) sensors. The system effectively transmits crucial data, such as vehicle ID, approaching direction, mileage driven, and destination time. By doing so, it enables other vehicles to allocate appropriate space and facilitate smoother passage for emergency vehicles. The main intention of this approach is to improve the communication speed among the nodes and to reduce the response time. The communication among the nodes is done with different frequencies to enhance the method's effectiveness. We also propose a priority-based MAC (PMAC), which guarantees a slot allocation for emergency message transmission in the network. The effectiveness of the proposed approach is assessed through simulation using NS-2. The findings emphasize the effectiveness of the RF sensor when it comes to its ability to respond quickly and showcasing the PMAC's capability to reduce end-to-end delay. [ABSTRACT FROM AUTHOR]

As obvious, disasters cause widespread human, economic, and environmental losses throughout the world. Both natural and man-made disasters require an effective disaster management system to curtail their potential damage and ensure prompt response to affected communities. The widespread adoption of Information and Communication Technologies (ICTs) in this field has facilitated information exchange, situational awareness, and communication among various stakeholders. Within ICT, the recommendation system has been widely used to help decision makers take critical and effective decisions. In this review, we aim to unravel how recommendation systems (RS) have been used in the disaster management domain. To this end, a systematic literature review (SLR) was performed, resulting in a data set of 44 papers dealing with this topic. Selected studies were evaluated to provide an answer to five different research questions. We considered what are the most common recommendation approaches adopted in these studies, the input and output of the recommendation system, the disaster management phase involved, and the contribution of the study in the field. The results of this review present a starting point for further research in this area. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel approach for estimating the burned area of forest fires using the TabNet transformer-based architecture. Forest fires pose a significant threat to ecosystems, and accurate estimation of the affected area is essential for effective disaster management and resource allocation. We conducted a comprehensive analysis of various Machine Learning (ML) and Deep Learning (DL) methods, including Random Forest, Neural Networks, Neural Architecture Search (NAS), TabNet with Transformers, and Self-Supervised Learning with Autoencoders, to identify the most accurate and efficient model for area estimation. Our experiments employed a publicly available dataset, UCI Forest Fires, containing a combination of meteorological, geospatial, and categorical data. We implemented a thorough preprocessing pipeline that included handling categorical variables, standardization, and feature engineering. The results demonstrate that TabNet outperforms other methods, achieving state-of-the-art accuracy and generalization in predicting the target variable with a Mean Squared Error (MSE) of 2319 in training and 7781 in testing. [ABSTRACT FROM AUTHOR]

Nowcasting represents a short-term weather forecast of how the atmospheric state will evolve during the next time period, typically less than two hours. It is vital for generating society-level emergency alerts in order to take timely actions and responses to potential disasters. The objective of the paper is to improve upon current nowcasting methods by applying a Deep Learning model that uses Convolutional Long-Short Term Memory Networks on a combination of satellite data. It is proposed a model ConvS Now for short-term prediction of satellite images that would be useful for precipitation nowcasting. The proposed model was trained and evaluated on satellite imagery collected by EUMESAT's Meteosat-11 satellite utilizing the Severe Storms RGB product. The experimental results performed a subset of the Meteosat-11 data spanning Europe demonstrate that this model can enhance weather short-term forecasting, reduce costs and time, and improve the general quality of predictions, as a normalized mean of absolute errors of 1.6% was attained, outperforming every other baseline approaches considered for comparison. A relative improvement of more than 30% has been achieved by the ConvS Now compared to the baselines, our proposed model being able to capture the spatio-temporal features of the weather evolution. [ABSTRACT FROM AUTHOR]

Autonomous emergency braking (AEB) systems are able to control vehicles as needed to avoid vehicle rear-end collisions. However, these systems are ineffective in scenarios with laterally cut-in vehicles and rapidly-changing dangerous scenes. This paper proposes a novel collision-free emergency braking system (CFEBS) that can enable intelligent connected vehicles (CAVs) to plan and execute a more conservative safety trajectory for the braking process in dangerous scenes by considering the longitudinal and lateral motion intentions of the surrounding vehicles. An intention identification model for surrounding vehicles is proposed based on long–short term memory (LSTM) networks and conditional random fields (CRFs). By considering the surrounding vehicles as risk sources and quantifying the risk with the speed of the risk flow, a potential risk flow model is built to calculate the potential risk map (PRM) around the ego vehicle. The global safest trajectory is generated via the PRM using the discrete method. The output trajectory profile is regarded as the reference for a model predictive controller (MPC). Simulation results show that the proposed CFEBS can predict vehicle intention with 91.6% accuracy and control the ego vehicle to perform effective collision-free braking operations in emergency traffic environments. • More conservative safety trajectories in braking process for dangerous scenes. • A high-accuracy motion intention identification model for surrounding vehicles. • A risk flow model to assess potential risk distributions. • A deceleration-based trajectory planning methods. [ABSTRACT FROM AUTHOR]

Deep learning (DL) is becoming increasingly popular in numerous application fields within the current Fourth Industrial Revolution (4IR) era. This is mainly due to its capability for providing accurate predictions and reliable consistency in decision-making. Bridge engineering focused on structure monitoring and inspection is a crucial activity for disaster prevention. Therefore, it is an application field wherein synergies between professional knowledge and sophisticated machine-based analytics strategies can be established and even drive time-effective interventions. This paper presents a comparison of DL models used to detect defects in bridges, resorting to the following architectures: MobileNetV2, Xception, InceptionV3, NASNetMobile, Visual Geometry Group Network-16 (VGG16), and InceptionResNetV2. Different optimizers (e.g., Nadam, Adam, RMSprop, and SGD) crossed with distinct learning rates (e.g., 1, 10−1, 10−2, 10−3, 10−4, and 10−5) were employed. VGG16, Xception, and NASNetMobile showed the most stable learning curves. Moreover, Gradient-weighted Class Activation Mapping (Grad-CAM) overlapping images clarifies that InceptionResNetV2 and InceptionV3 models seek features outside the areas of interest (defects). Comparing optimizers' performance, the adaptive ones outperform SGD with decay schedulers for learning rates. [ABSTRACT FROM AUTHOR]

Water distribution systems are susceptible to contamination events, which can occur due to naturally occurring events, accidents or even malicious attacks. When a contamination event occurs, dangerous substances infiltrating the network may be consumed thereby deteriorating the consumers' health and possibly affecting the economy. Advances in sensor and actuator technologies are enabling water networks to become smarter and more resilient to these types of events. This paper provides a broad review of the theoretical, modeling, and computational developments in the area of contamination event diagnosis for water distribution systems. Research is segmented into three main tasks, summarized as "Preparedness", "Event Detection and Isolation" and "Emergency Event Management". The key research topics from each task are described within a unified systems-theoretic mathematical framework, and their open challenges are discussed. [ABSTRACT FROM AUTHOR]

• Weather and climate services (WCS) for farmers in India and Norway reveal similar governance challenges related to format, relevance, and tailoring of knowledge communicated. • A 'most different case study' comparison underscores that WCS remain supply-driven. • More deliberate approaches to institutional design is suggested to foster co-creation and improved engagement of farmers and extension agents in developing the services. • Active use of social media, such as crop-based WhatsApp groups, can facilitate integration between external science-based knowledge/models and local practice and learning. High-quality weather and climate services (WCS) can be critical for communicating knowledge about current and future weather and climate risks for adaptation and disaster risk management in the agricultural sector. This paper investigates the structure and performance of weather and climate services for farmers from a governance perspective. Empirically the paper compares the institutional design and operations of agro-meteorological services in Maharashtra/India and Norway through a 'most different case study' approach. The two cases were selected to represent great diversity in location, scale and institutional design. A governance approach based on semi-direct interviews and policy and institutional analysis was combined with local survey data of farmers' perceptions and use of the services. Despite the fact that the context for the two agromet advisory services was very different from a climate-weather, eco-agriculture and socio-institutional angle, the analysis reveals great similarities in the services structures and critical governance challenges. In both countries the agromet services communicated knowledge that was largely perceived not to be well tailored to farmers' needs for decisions in specific crops- and farm operations, spatially too coarse to address local issues, and, often unreliable or inaccurate in terms of the quality of data. Farmers did, however, respond positively to specific and locally relevant information on e.g., warnings about high rainfall and spread of pests. Observing such similarities across very diverse contexts enhances the generalization potential, precisely because they evolved under very different circumstances. Similar observations find support in the wider WCS literature. Based on the empirical findings, we propose a more deliberate approach to institutional design of WCS in order to enhance governance performance and co-creation of the services at local, district and national scales. It is suggested that greater participation of farmers and agricultural extension agents in the co-creation of these services is a necessary means of improving the services, supported by the WCS literature. However, we insist that greater participation is only likely to materialize if the deficiencies in institutional design and knowledge quality and relevance are addressed to greater extent than done today. The comparison between the two services shows that Norway can learn from India that a more ambitious scope and multiple forms of communication, including the use of social media/WhatsApp groups, can facilitate greater awareness and interest among farmers in multi-purpose agromet services for multi-way communication. India can learn from Norway that a more integrated and decentralized institutional design can strengthen the network attributes of the services, foster co-creation, and improve participation of both poor and large-scale farmers and extension agents. [ABSTRACT FROM AUTHOR]

Disaster waste management received increasing attention in recent years, but there was no review updating the evolving development after the study of Brown et al. (2011a). To explore how the topics in disaster waste management evolved in recent years and to analyze whether the gaps identified by Brown et al. (2011a) are covered, 82 papers published from 2011 to 2019 were selected from the Scopus database based on the defined process and criteria. This paper systematically examines the disaster waste management research from nine aspects of planning, waste, waste treatment options, environment, economics, social considerations, organizational aspects, legal frameworks and funding. The results suggested that there were no obvious changes or developments in the field of disaster waste management, although a few research gaps have been addressed, such as waste separation, waste quantities, case studies of incineration and waste to energy, direct economic effects, social considerations as well as application of GIS technology. Except for the comparative studies, future directions were suggested by the gaps that persist since Brown et al. (2011a) and the new gaps that were identified in this review. • A systematic review was done to review recent advances in disaster waste management. • There were no dramatic new developments in the field of disaster waste management. • Recent studies mainly focus upon technical aspects. • Planning, legislation, organizations and funding were not well addressed. • Future research directions are proposed. [ABSTRACT FROM AUTHOR]

Rescue station setup and rescue path planning are two important tasks in urban emergency rescue. The former task ensures rescue response capability and the latter task provides effective rescue solutions. When emergencies occur in cities, evacuees are distributed along the urban road network. Rescue resources refer to rescue vehicles whose available number and capacity are both limited. With the constraints of rescue resources and the number of rescues, this paper aims to simultaneously optimize the tasks of rescue station setup and rescue path planning. In the addressed scenario, the priority of each evacuee is quantified as a weight value that is used as the main optimization objective. To solve the problem, a comprehensive urban emergency rescue planning approach is proposed. The proposed approach consists of components of road network processing, road network weight calculation, rescue station setup and rescue path planning. For the setup of rescue stations, this paper employs a clustering method to provide a set of high-quality candidate rescue stations for subsequent path planning based on the locations of evacuees and the road network structure. For rescue path planning, an improved ant colony optimization algorithm is developed. The proposed method is called the planning algorithm with clustering and improved ant colony optimization (PA-C-IACO). The proposed PA-C-IACO redefines the degree of heuristic and pheromone concentration increments for transfer between intersections in the ant colony algorithm and incorporates a reward mechanism during the pheromone update process. Experimental results on six different size datasets show that PA-C-IACO outperforms state-of-the-art algorithms and shows good robustness and feasibility. • For urban emergency rescue problems, rescue response capability and the effectiveness of rescue solutions are considered simultaneously. • A hybrid clustering and improved ant colony optimization algorithm approach is proposed. • The emergency rescue problem is subdivided into multiple small problems and solved collaboratively. • A reward mechanism is devised to prevent falling into a local optimum. • An extensive experimental evaluation and comparison on a real dataset is provided. [ABSTRACT FROM AUTHOR]

This paper proposes a bi-level optimization model for resource allocation in disaster response, categorizing emergency resources into personnel and materials in consideration of the coupling relationship between them. The model addresses two levels of problems, which can be conceptualized as a Stackelberg game. The upper-level saves rescue time to determine the transportation scheduling scheme from cities that provide assistance to disaster-stricken cities. The lower-level minimizes cost to determine the optimal allocation scheme of emergency resources from cities that provide assistance to emergency resource distribution centers, which can be characterized as a Nash game among the followers. A robust optimization model with budget constraints is constructed to cope with the demand uncertainty at disaster points, disruption risk at distribution centers and transportation time uncertainty from the emergency resource distribution centers to the disaster-stricken cities. An efficient emergency resource allocation scheme under multiple uncertainties ensures rapid and effective post-disaster recovery, improves disaster supply chain sustainability and reduces environmental pollution. Since the developed model is in a bi-level nonlinear format, the Karush-Kuhn-Tucker conditions and the big-M method are applied to obtain a single-level mixed integer linear programming model. To validate the efficacy of the model, a series of numerical experiments with sensitivity analysis are conducted. Compared with the leader's perspective model and the follower's perspective model, although the computational time of the bi-level model is relatively longer, it can obtain a satisfactory point for both parties. Moreover, as the disturbance of the three uncertainties continues increasing, it can be observed that the two objectives do not necessarily have a simultaneous increasing trend in any case. In addition, under the influence of uncertainty, the costs of cities that provide similar quality assistance can remain stable. [Display omitted] • A robustness model in emergency resource coupling allocation is proposed. • Cities that provide assistance are followers; Disaster-stricken cities are leaders. • A bi-level program is developed, and converted to a mixed integer one for solution. • The characteristics of robustness under multiple uncertainties are investigated. • The outcomes provide technical support for decision-making in emergency assistance. [ABSTRACT FROM AUTHOR]

National weather forecasting agencies routinely issue a range of hazard warnings. But to our knowledge, along sandy coastlines where storm waves and storm surge can result in widespread but location-specific beach erosion and beachfront flooding, no national-scale early warning service for these hazards is presently operational. This paper outlines the scientific basis and implementation of a new framework for large area coastal storm hazards forecasting, currently being tested along the southwest (Indian Ocean) and southeast (Pacific Ocean) coasts of Australia. The system provides 7-day rolling predictions of localized beach erosion and/or coastal flooding linked to forecasted extreme weather events. Coastal setting influences the nature and occurrence of these hazards, with sandy beaches along wave-dominated coasts more prone to erosion and at surge-dominated coasts to flooding. An existing nearshore water-level forecasting system and a new inshore wave modeling capability are used to forecast beach erosion and coastal flooding at every 100 m along the shore. At the regional scale O(100–1 000 km of coastline), a threshold-based decision tree model categorises the predicted extent, location, and severity of erosion and flooding. At a more local scale O(100–1 000 m), physics-based modeling using XBeach focuses on vulnerable or high-value locations, providing specific storm hazard indicators tailored to local needs. This two-tier approach is feasible for national implementation due to the reduced computational effort, limiting intensive modeling to pre-identified critical locations. Delft-FEWS manages the data and modeling workflow, ensuring scalability and compatibility with existing forecast infrastructure. Initial evaluations of the system are promising, with a detailed 2-year evaluation in progress. Future enhancements could include the use of satellite imagery for real-time beach width and dune topography assimilation and exploring alternative modeling approaches to further improve forecast accuracy. • A framework for coastal storm hazards early warning over very large areas is developed. • The severity of hazards is predicted every 100 m alongshore by parametric modeling. • Site-specific storm hazard indicators at vulnerable sites are predicted by XBeach. • 7-day rolling forecasts of storm hazards are communicated via a web portal. [ABSTRACT FROM AUTHOR]

In an era where chemical, biological, radiological, and nuclear (CBRN) incidents present a grave threat to public safety, timely and accurate information is paramount. The complexity of the CBRN concept encompasses a range of incidents, each with unique and overlapping symptoms, related substances, and event descriptions. This study introduces an innovative approach to the development of a CBRN-specific ontology, uniting diverse data sources and domain expertise to construct a comprehensive repository of CBRN events, sub-events, their causes, symptoms, and toxic substances. Unlike prior methodologies reliant on keyword searches and predefined categories, our approach enables a holistic analysis of textual data by capturing intricate relationships between symptoms and toxic substances. We leverage this ontology in conjunction with a tailored interrogation algorithm to detect potential CBRN incidents through social media data. The algorithm was then tested on datasets of three actual CBRN incidents, one fictional incident (TV show) that simulated a nuclear incident and one non-CBRN. The interrogation algorithm was able to detect the five CBRN incidents accurately. However, the study showcased the need to extend the algorithm to distinguish between real and fictional CBRN incidents. These findings underscore the potential of this approach to deliver timely information on potential CBRN incidents. Nevertheless, the study acknowledged the inherent challenges and limitations in utilizing social media data, including the risk of misinformation, fictional events, fake news, and interference from malicious actors, all of which can affect the accuracy and reliability of the information collected. • This paper proposes a novel approach to building a comprehensive ontology of Chemical, Biological, Radiological and Nuclear (CBRN) symptoms and toxic substances. • The proposed ontology captures the relationships among CBRN events, sub-events, event type, their related symptoms and toxic substances; enabling a more comprehensive analysis of social media data. • An ontology interrogation algorithm is proposed to analyze social media data for potential CBRN incidents. • Results show the potential of this approach to provide timely and accurate information to emergency responders. [ABSTRACT FROM AUTHOR]

Wildfires have emerged as one of the most destructive natural disasters worldwide, causing catastrophic losses. These losses have underscored the urgent need to improve public knowledge and advance existing techniques in wildfire management. Recently, the use of Artificial Intelligence (AI) in wildfires, propelled by the integration of Unmanned Aerial Vehicles (UAVs) and deep learning models, has created an unprecedented momentum to implement and develop more effective wildfire management. Although existing survey papers have explored learning-based approaches in wildfire, drone use in disaster management, and wildfire risk assessment, a comprehensive review emphasizing the application of AI-enabled UAV systems and investigating the role of learning-based methods throughout the overall workflow of multi-stage wildfire management, including pre-fire (e.g., vision-based vegetation fuel measurement), active-fire (e.g., fire growth modeling), and post-fire tasks (e.g., evacuation planning) is notably lacking. This survey synthesizes and integrates state-of-the-science reviews and research at the nexus of wildfire observations and modeling, AI, and UAVs — topics at the forefront of advances in wildfire management, elucidating the role of AI in performing monitoring and actuation tasks from pre-fire, through the active-fire stage, to post-fire management. To this aim, we provide an extensive analysis of the existing remote sensing systems with a particular focus on the UAV advancements, device specifications, and sensor technologies relevant to wildfire management. We also examine the pre-fire and post-fire management approaches, including fuel monitoring, prevention strategies, as well as evacuation planning, damage assessment, and operation strategies. Additionally, we review and summarize a wide range of computer vision techniques in active-fire management, with an emphasis on Machine Learning (ML), Reinforcement Learning (RL), and Deep Learning (DL) algorithms for wildfire classification, segmentation, detection, and monitoring tasks. Ultimately, we underscore the substantial advancement in wildfire modeling through the integration of cutting-edge AI techniques and UAV-based data, providing novel insights and enhanced predictive capabilities to understand dynamic wildfire behavior. [Display omitted] • An in-depth review of AI-based state-of-the-art in multi-stage wildfire management. • A comprehensive analysis of employed UAV and sensor technologies for fire management. • An extensive review of recent ML, DL, and RL advancements in active-fire management. • Detailed exploration of existing strategies for pre-fire and post-fire management. • Highlights advanced wildfire modeling, focusing on integrating AI and UAV systems. [ABSTRACT FROM AUTHOR]

In the decision-making process, probabilistic linguistic term sets (PLTSs) could represent decision-makers' uncertain evaluation information. The combination of probabilistic and linguistic information assists decision-makers to depict their evaluation information more flexibly. In this paper, a prospect outranking decision-making method based on stochastic dominance is proposed for solving the multi-attribute risk decision-making problem under the probabilistic linguistic information environment. Firstly, to fully preserve the original probabilistic linguistic information, we give the definitions of the generalized probability distribution and the corresponding generalized cumulative distribution function of a PLTS for defining the distance measure between PLTSs. Secondly, based on the positive and negative ideal cumulative distribution functions, we propose the probabilistic linguistic prospect value functions. After that, considering the uncertainty of the relationship between alternatives, several kinds of novel prospect stochastic dominance and almost stochastic dominance relations are defined, which consider the decision makers' distinction between gains and losses to measure the subtle relationship between alternatives. The stochastic dominance approach and the almost stochastic dominance approach under the probabilistic linguistic decision-making environment are put forward. Besides, we use the superiority and inferiority ranking method to solve the dominance ranking problem of the uncertainty relationship between any two alternatives. Finally, the proposed method is applied in the evaluation of emergency plans to show the accuracy and reliability, and comparative analyses are made to illustrate its availability. [ABSTRACT FROM AUTHOR]

The paper proposes a new information set of multiple interacting fuzzy linguistic set(MIFLS) to describe the interaction of uncertain linguistic information. The property of the MIFLS and its characteristic of processing consensus information are also discussed, the introduction of MIFLS provides a new perspective to improve the consistency of decision makers' evaluation information, and the application of MIFLS can improve the accuracy of decision making. Furthermore, we propose an optimization consistency information model based on the MIFLS and TOPSIS method, an example of emergency decision plan is used to illustrate the applicability and efficiency of the proposed model. The optimal consistency adjustment is not only the highly consistent evaluation information, but also the minimal difference between the original information and the adjusted information. The proposed MIFLS is fit for dealing with the interaction of decision information, which means MIFLS is one of the effective tools to processing the interaction of uncertain fuzzy set and dealing with the emergency decision making problem. More important, the MIFLS not only provides a way to quantify the correlation of multi-attribute information, but also provides a method how to improve the consistency of decision-maker information. [ABSTRACT FROM AUTHOR]

Abstract Space capabilities play a crucial role in ensuring human security. One of the threats coming from space is the possible damage to our assets by an asteroid or comet impact. As demonstrated by the object entering the Earth's atmosphere over Chelyabinsk, Russia, in February 2013, the threat of an asteroid or comet impact is a real and global issue demanding development of an international response. Addressing such a hazard, by first identifying those objects that pose a threat to enable planning a corresponding mitigation campaign, require international coordination. The United Nations Member States, especially those with capabilities to engage in a possible planetary defence mission, already share a number of common activities in this field. This paper outlines the progress made in the implementation of recommendations for an international response to the NEO impact threat, as agreed under the auspices of the United Nations (UN) Committee on the Peaceful Uses of Outer Space (COPUOS) and welcomed by the UN General Assembly in its resolution 68/75 of December 2013. The recommendations provide for a coordinated international response to a possible NEO threat. They aim at ensuring international information-sharing in discovering, monitoring and physically characterizing potentially hazardous NEOs with a view that all countries, in particular developing countries with limited capacity in predicting and mitigating a NEO impact, are aware of potential threats. They emphasize the need for an effective emergency response and disaster management in the event of a discovered NEO impact threat. The International Asteroid Warning Network (IAWN) and the Space Mission Planning Advisory Group (SMPAG), which are the two entities established in 2014 as a result of the UN-endorsed recommendations, are important mechanisms at the global level for strengthening the coordination in the area of planetary defence. The United Nations Office for Outer Space Affairs (UNOOSA) acts as secretariat to SMPAG and works with both IAWN and SMPAG in addressing this global issue. In the event of a credible impact prediction, warnings would be issued by the IAWN, the SMPAG would propose mitigation options and implementation plans for consideration to the Member States. The goal is the global protection of the eco-system, of human beings and their properties on Earth, and of the civilization of humankind from a devastating asteroid impact. The current paper outlines the work of the IAWN and the SMPAG towards a road-map for planetary defence at the global level, including agreements on initial criteria and thresholds for impact threat response actions, consideration of mitigation mission types and technologies and mapping of threat scenarios to mission types as well as developing a plan for action in case a credible threat is discovered. The paper also reflects on how to convey information about the NEO impact warnings and associated impact probabilities to the public and governmental decision-makers as part of the agreed communications guidelines, which are another important pillar in the work of the IAWN. [ABSTRACT FROM AUTHOR]

Abstract This paper evaluates the crucial role of unmanned aerial vehicles –UAV's- (or Drones) in the case of natural disasters response and humanitarian relief aid. The primary objective of this paper is to evaluate how unmanned aerial vehicles –UAV's- (or Drones) in the present or near future can help survivors in the case of a tsunami, earthquake, flooding, and any natural disaster. Initially, we assume that in any natural disaster always exist the high possibility of damage to the infrastructure, transportation systems, telecommunications systems access, and basic services immediately. This research proposes three areas the uses of unmanned aerial vehicles –UAV's- (or Drones) in the case of natural disasters response and humanitarian relief aid. These are (i) the aerial monitoring post-natural disaster damage evaluation, (ii) the natural disaster logistic and cargo delivery, (iii) the post-natural disaster aerial assessment. [ABSTRACT FROM AUTHOR]

Abstract Contrary to most areas of Europe, the Nordic countries (Denmark, Finland, Iceland, Norway, Sweden, and the Faroe Islands) have for many years shared a regional research and development program on nuclear reactor safety and emergency preparedness - NKS. In spite of its project results having received great recognition and having been integrated in state-of-the-art emergency preparedness tools over the world, NKS as an organization does not seem well known outside the Nordic countries. Although the Fukushima accident had no health impact at all in Nordic areas, it taught a number of lessons of generic nature with respect to new R&D tasks that could further strengthen and secure future maintenance of the Nordic region's capability to effectively respond to such events. For broader inspiration, this paper briefly introduces the Nordic nuclear emergency preparedness cooperation channels and outlines the related NKS R&D project initiatives launched after the Fukushima accident, many of which should be of general interest also far outside the region. The paper is intended as an introduction to NKS with an invitation to explore its results. All project results are available cost-free on the NKS website. Highlights • For inspiration to others the special Nordic cooperation pathways in nuclear emergency preparedness are outlined. • Recent Nordic research work under NKS has produced international state-of-the art results on nuclear emergency preparedness. • Cooperation on nuclear emergency preparedness of smallish countries with similar cultural background can be cost-effective. • Internet links are provided from NKS activity discussions to all relevant NKS project reports for cost-free download. [ABSTRACT FROM AUTHOR]

Abstract This research paper is developed as a practical guide on preparedness against possible threats that are usually unconsidered or undermined in Egyptian conservation schemes and agendas. The unequivocal importance of the Karnak temple (KT) in Luxor, Egypt presented this monument as a major target for investigation. Reviews of recent conservation endeavors in the KT unveil a clear lack of awareness concerning preparedness against fast, sudden and unpredictable threats. The preparedness plan developed in this research paper for the KT tackles the relationship between sources of threats, past disaster incidents, inducible risks and the process of generating interventions to contain risks and minimize damages. Issues of scientific uncertainty are transcended for the greater aim of preparing a preparedness plan against possible and potentially harmful threats. [ABSTRACT FROM AUTHOR]

Emergency decision-making is vital for nations or communities because it increases emergency management's effectiveness and legitimacy, which in turn greatly minimizes environmental damage, fatalities, and economic loss. The evaluation of emergency judgements must take into account significant inaccuracy, fuzzyness, and ambiguity. While high risk and uncertainty are frequently characteristics of emergency decision-making (EDM) circumstances. The current EDM methodologies do not take into account the psychological behavior of the decision makers in addition to the various emergency situations and the various responses. In this paper, we presented a new method based on a Complex Probabilistic Hesitant Fuzzy Soft Set (CPHFSS) as represented by membership in 2-D with hesitant probability. The idea that is being put forth captures the ambiguity and takes into account emergency situations while also taking into account the psychological state of the decision-makers involved in the EDM procedure. The introduction of a new score function for the CPHFSS serves as the second objective of this paper, which is to address a number of comparability issues. We examine a novel family of hybrid operators for CPHFSS that utilize numerous independent variables as a solution to these unforeseen issues. The group decision-making strategy and EDAS technique are then suggested employing these operators. Later, an emergency decision-making situation involving a major fire in China is used to demonstrate the validity of the algorithms. • Concept of complex probabilistic hesitant fuzzy soft set is presented. • Introduce a new score function for the CPHFSS. • Group decision-making strategy and EDAS technique presented. • Emergency decision-making problems discussed in the study. • Case study related to industrial disaster emergency decision-making discuss. [ABSTRACT FROM AUTHOR]

• In 2005, the chemical response to oil Spills: ecological effects research forum (CROSERF) protocol was developed with existing common analytical methods that described a standard method for reproducible preparation of exposure media as well as recommended specific analytical methods and analyte lists for comparative toxicity testing where the primary purpose of the data collected was to inform oil spill response and contingency planning. • With improvements in both analytical equipment and methods, the use of toxicity data has expanded to include their integration into fate and effect models that aim to extend the applicability of lab-based study results to make predictions for field system-level impacts. • This paper provides a summary of current chemical analyses for the characterization of oil and exposure media used during aquatic toxicity testing and makes recommendations for the minimum analyses needed to allow data to be used for secondary modeling purposes. • An overview of oil and exposure media analytical methods relevant for toxicity test characterization, from simple to complex are described. • Supplemental information (SI) includes definitions of the technical terms used as well as brief descriptions of the analytical methods, and sample storage and handling procedures mentioned. During toxicity testing, chemical analyses of oil and exposure media samples are needed to allow comparison of results between different tests as well as to assist with identification of the drivers and mechanisms for the toxic effects observed. However, to maximize the ability to compare results between different laboratories and biota, it has long been recognized that guidelines for standard protocols were needed. In 2005, the Chemical Response to Oil Spills: Ecological Effects Research Forum (CROSERF) protocol was developed with existing common analytical methods that described a standard method for reproducible preparation of exposure media as well as recommended specific analytical methods and analyte lists for comparative toxicity testing. At the time, the primary purpose for the data collected was to inform oil spill response and contingency planning. Since then, with improvements in both analytical equipment and methods, the use of toxicity data has expanded to include their integration into fate and effect models that aim to extend the applicability of lab-based study results to make predictions for field system-level impacts. This paper focuses on providing a summary of current chemical analyses for characterization of oil and exposure media used during aquatic toxicity testing and makes recommendations for the minimum analyses needed to allow for interpretation and modeling purposes. [Display omitted] [ABSTRACT FROM AUTHOR]

Smartification of public services is being promoted, but smart public services are at significant risk of cyberattacks. One of the most common cyberattacks is a denial-of-service (DoS) attack on unknown vulnerabilities. Recovery from DoS requires responses that may incur several hours or days of downtime. However, it is essential to enhance the resilience of smart public services by minimizing downtime. This study assumes a smart disaster prevention service using MQTT brokers and aims to enhance resilience against cyberattacks by applying immunity-based attack detection, as proposed by the authors to the MQTT brokers. Our previous work has shown that immunity-based attack detection could detect attacks on some vulnerabilities of the Mosquitto broker with high detection accuracy, but it could not correctly detect attacks on use-after-free vulnerabilities. In this paper, we propose a method for detecting cyberattacks, and demonstrate the effectiveness of the method through performance evaluation. [ABSTRACT FROM AUTHOR]

In this paper, we developed a score-based credibility assessment model which assesses the credibility of text and map-based VGI. We proposed parameters for assessing the credibility of VGI such as distance, hazard, and clustering. Then, the model was created based on the above-mentioned parameters, and the credibility scores of reports were computed. Furthermore, we categorized the reports into high-credible and low-credible categories, and accuracy of the model was determined by comparing the model's output with the real situation. To assess the credibility of VGI, we made use of reports from the Ushahidi project in Brisbane, Australia, which was hit by a severe flood in 2013 as our first dataset. Besides the aforementioned parameters, we examined the distance between the location of the user and location of the incident as a parameter for the credibility assessment of VGI. For doing so, in a case study, we developed a VGI-based website to collect data for the analysis. Results show that our model which was created by spatial clustering and flood hazard parameters can categorize the credibility classes with 92.6% accuracy. In addition, in the second study area, the credibility model using the proximity of the volunteer to the incident site parameter can assess the credibility classes of VGI with 90% accuracy. [ABSTRACT FROM AUTHOR]

In the last 20 years, extreme weather-related events like floods, landslides, droughts, and wildfires have caused the death of 1.23 million people and a loss of 2.97 trillion dollars. Studies show that low and lower-middle income countries are the most impacted ones given the lack of investment in disaster risk management. To reduce the impact of these events, weather researchers have been developing numerical weather models that inform public agencies about the impending extreme events in advance. Despite being powerful tools, these models can suffer from several sources of uncertainty, ranging from the approximation of micro-scale physical processes to the location-dependent calibration of parameters, which is especially critical in developing countries. To minimize uncertainty effects, researchers generate several different weather scenarios to compose an ensemble of simulations that typically are inspected using manual, laborious, and error-prone approaches. In this paper, we propose an interactive visual analytics system, called X-Weather , developed in close collaboration with weather researchers from Brazil. Our system contributes a set of statistics and probability-based visualizations that allows the assessment of extreme weather events by effortlessly navigating through and comparing ensemble members. We demonstrate the effectiveness of the system through two case studies analyzing tragic events that happened in the mountain region of Rio de Janeiro in Brazil. [Display omitted] • Our visualizations help identify extreme weather scenarios in large simulation ensembles. • Our web-based system enables the investigation of individual weather ensemble members. • Two case studies highlight our system's utility by analyzing extreme weather events. [ABSTRACT FROM AUTHOR]

Situational awareness of social media user activity during disasters is essential for effective and efficient crisis communication. Social media platforms such as Twitter and Facebook have emerged as critical channels for governments, relief agencies, and volunteer organizations to disseminate important information to the public during disasters. However, existing models are often limited to a specific disaster event, lacking a comprehensive and generalized approach. This paper introduces a novel life cycle concept to investigate the dynamics of social media engagement during multiple wildfires and bombing/shooting events in the United States. We extensively analyze millions of tweets and identify significant temporal commonalities, unveiling generic diurnal and life cycle patterns within each disaster category. This study proposes two models for the Hourly Distribution of Tweets and Daily Distribution of Tweets to explore different evolutionary stages characterizing diurnal and life cycle patterns. The hourly model accurately calculates peak engagement times for wildfire and bombing/shooting events, with peak engagement occurring between 9:00 AM and 12:00 PM. The daily models reveal that the highest diffusion rate is observed between two to four days from the wildfire formation day, while the peak engagement of a bombing/shooting event comes within the first 24 h of the incident. The findings enhance the understanding of social media dynamics during disasters and assist crisis communication agencies in developing communication strategies that ensure prompt and effective dissemination of critical information to affected populations. • Introduces life cycle concept to investigate the dynamics of social media engagement. • Analyzes millions of tweets to identify generic diurnal and life cycle patterns. • Estimates peak engagement between 9 AM–12 PM for wildfire and bombing/shooting events. • Reveals that highest diffusion is observed between 2–4 days from wildfire formation. • Reveals that highest diffusion is observed within 24 h of bombing/shooting event. [ABSTRACT FROM AUTHOR]

The objective of this paper is to elucidate the patterns of evolution of regional rainstorm events (RREs) in a large-scale water resources allocation area. This is crucial for the safe operation of water transfer projects and water resources management. The Objective Identification Technique for Regional Extreme Events (OITREE) method is employed to identify all RREs in the South-to-North Water Diversion Area (SNWDA) between the years 1960 and 2022. The results indicate that among the 1397 RREs identified, the extreme RREs are significantly more severe than other classes of rainstorm events. While the extreme intensity (Im) of RREs exhibits a decreasing trend, their frequency, comprehensive index (Z), maximum area (Am), summed area (As), and summed intensity (Is) all demonstrate increasing trends. Some of these indices (Z , Am , As , and Is) exhibit a strong periodicity of approximately 20 years, while the shortening of the decline period will result in more frequent rising periods of RREs. In the second year following the water transfer operation (2015), all RRE indices exhibit abrupt changes. Furthermore, the rainy season lengthens, with more frequent and extreme RREs occurring in July, which may have implications for the Middle Route of year-round water transfers. Spatially, the occurrence frequency of regional rainstorm days (RRDs) increases by 35.9% since the 21st century, while regional rainstorm intensity (RRI) increases by 11.8%. The high-frequency zones (HFZs) of RRDs are primarily located in the southeast, with intensity increases of at least 30%. While their location remains stable, there is a clear trend of northward spread. Following the water transfer, the frequency of RREs is higher in the Eastern Route, and the high-intensity zones (HIZs) appear for the first time in the Beijing area of the Middle Route. These findings provide a scientific understanding of regional rainstorms and serve as a reference for rainstorm disaster response and optimal allocation of water resources in water transfer projects. • Regional rainstorm events are escalating in frequency and intensifying. • The year 2015 marks a key period when regular fluctuation in regional rainstorm events shifted to significant enhancement. • The rainy season shows trends of early onset and delayed conclusion within the year. • Regional rainstorms are most common in July and are their heaviest in August. • The southeast of the South-to-North Water Diversion region is the most concentrated area for regional rainstorm events. [ABSTRACT FROM AUTHOR]

In this paper, we focus on how to generate the multi-objective optimal plan in hierarchical task network (HTN) planning. Many practical HTN planning problems not only require feasible plans, but also require these plans to achieve the best performance on multiple criteria, which is the multi-objective optimization problem in HTN planning. Aiming to generate the multi-objective optimal plan in HTN planning, a novel HTN planning approach is proposed. Our initial step is to express multiple criteria by extending the concept of the HTN planning domain. The primary search process of our proposed HTN planning approach has two components. The first one is heuristic search, which assesses the quality of methods/operators and organizes them according to their dominance relations. The second one is anytime search, which reduces the search space on the basis of the dominance relations between the partial plan and the non-dominated plan set, which is updated during the planning process. After the termination of the HTN planning search, a non-dominated plan set is returned for the decision-maker to choose from. We adopt Zeno Travel domain problems and an emergency evacuation planning problem in an experimental study to demonstrate how effective and practicable the proposed approach is. • An approach is proposed to handle the multi-objective optimization in HTN planning. • An HTN planning approach combines heuristic search and anytime search. • The heuristic search based on dominance relations is proposed. • The anytime search prunes the search space according to dominance relations. • The HTN planning approach generates a non-dominated plan set. [ABSTRACT FROM AUTHOR]

One of the most valuable and promising applications for Unmanned aerial vehicles (UAVs) is in natural disaster management, where these aircraft can operate autonomously without any need for human intervention during their flights. In this paper, we foster the interface of Operational Research with computer science in general and sensor networking in particular by focusing on managing a post-disaster emergency scenario where the use of a fleet of UAVs helps rescue teams identify people needing help inside an affected area. We model this situation as an original graph theoretical problem called Multi-Depot Multi-Trip Vehicle Routing Problem with Total Completion Time minimization (MDMT-VRP-TCT). The main novelty of the MDMT-VRP-TCT is the combination of the following three features: multi-depot, multi-trip, and completion time minimization. We propose a mixed-integer linear programming (MILP) formulation, develop a matheuristic framework to address large instances, and present an extended set of experiments to test the performance of the proposed matheuristic: first, we compare the matheuristic with the MILP formulation on a set of small instances (up to 30 nodes); then, we compare our matheuristic with two heuristics from networking literature, showing that it outperforms the existing algorithms. [ABSTRACT FROM AUTHOR]

In recent years, CubeSats have considerably extended their range of possible applications, from a low cost means to train students and young researchers in space related activities up to possible complementary solutions to larger missions. Increasingly popular, whereas CubeSats are still not a solution for all types of missions, they offer the possibility of performing ambitious scientific experiments. Especially worth considering is the possibility of performing Distributed Space Missions, in which CubeSat systems can be used to increase observation sampling rates and resolutions, as well as to perform tasks that a single satellite is unable to handle. The cost of access to space for traditional Earth Observation (EO) missions is still quite high. Efficient architecture design would allow reducing mission costs by employing CubeSat systems, while maintaining a level of performance that, for some applications, could be close to that provided by larger platforms, and decreasing the time needed to design and deploy a fully functional constellation. For these reasons many countries, including developing nations, agencies and organizations are looking to CubeSat platforms to access space cheaply with, potentially, tens of remote sensing satellites. During disaster management, real-time, fast and continuous information broadcast is a fundamental requirement. In this sense, a constellation of small satellites can considerably decrease the revisit time (defined as the time elapsed between two consecutive observations of the same point on Earth by a satellite) over remote areas, by increasing the number of spacecraft properly distributed in orbit. This allows collecting as much data as possible for the use by Disaster Management Centers. This paper describes the characteristics of a constellation of CubeSats built to enable access over the most remote regions of Brazil, supporting an integrated system for mitigating environmental disasters in an attempt to prevent the catastrophic effects of natural events such as heavy rains that cause flooding. In particular, the paper defines the number of CubeSats and the orbital planes required to minimize the revisit time, depending on the application that is the mission objective. Each CubeSat is equipped with the suitable payloads and possesses the autonomy and pointing capabilities needed to meet the mission requirements. Thanks to the orbital features of the constellation, this service could be exploited by other tropical countries. Coverage of other areas of the Earth might be provided by adjusting the number and in-orbit distribution of the spacecraft. [ABSTRACT FROM AUTHOR]

Recent and historic low-frequency, high-impact events have demonstrated the flood risks faced by exposed coastal areas in Europe and beyond. These coastal zone risks are likely to increase in the future which requires a re-evaluation of coastal disaster risk reduction (DRR) strategies and a new mix of PMP (prevention, e.g., dike protection; mitigation, e.g., limiting construction in flood-prone areas and eco-system based solutions; and preparedness, e.g., Early Warning Systems, EWS) measures. In response to these challenges, the RISC-KIT project has delivered a set of open-source and open-access methods, tools and management approaches to reduce risk and increase resilience to low-frequency, high-impact hydro-meteorological events in the coastal zone (the “RISC-toolKIT”). These products enhance forecasting, prediction and early warning capabilities, improve the assessment of long-term coastal risk and optimise the mix of PMP-measures. In this paper an introduction is provided to the objectives, products, applications and lessons-learned of the RISC-KIT project, which are the subjects of this Special Issue. Subsequent papers provide details on the tools and their application on 10 case study sites in Europe. [ABSTRACT FROM AUTHOR]

This paper presents a new approach to guiding coastal stakeholders about making informed decisions about Disaster Risk Reduction (DRR) measures and alternatives. As part of the RISC-KIT project and tool box, the paper presents a holistic DRR measure approach including the biophysical environment, governance aspects and practical examples from coastal areas in Europe and elsewhere. The guide (see: www.coastal-management.eu ) is addressed to a wide variety of coastal stakeholders with a different level of knowledge about DRR measures with the aim to provide guidance and information. The paper gives an overview of the overall structure of the guide. [ABSTRACT FROM AUTHOR]

This paper introduces a participatory Multi-Criteria Assessment (MCA) methodology developed through the Resilience Increasing Strategies for Coasts – Toolkit (RISC-KIT) project and implemented in nine case studies in Europe. The purpose of the MCA was to bridge the disciplinary divide between engineering sciences and social sciences, facilitate the communication and dissemination of local coastal risk assessments and Disaster Risk Reduction (DRR) measures' evaluation to a broad range of actors. The process addressed the importance of integrating scientific knowledge with stakeholders’ knowledge to understand and assess the possible social, political and economic implications of different DRR measures, which could foster or hinder successful implementation. The paper discusses the methodological aspects and implementation of the approach which included visualizing risk reduction of DRR measures using paper-based cards to support interaction and negotiation among participants to select preferred strategic alternatives (SA), and a participatory MCA where stakeholders evaluated the SA against three (self-weighted) criteria: feasibility, acceptability and sustainability. [ABSTRACT FROM AUTHOR]

Coastal zones are under increasing risk as coastal hazards increase due to climate change and the consequences of these also increase due to on-going economic development. To effectively deal with this increased risk requires the development of validated tools to identify coastal areas of higher risk and to evaluate the effectiveness of disaster risk reduction (DRR) measures. This paper analyses the performance in the application of two tools which have been developed in the RISC-KIT project: the regional Coastal Risk Assessment Framework (CRAF) and a hotspot early warning system coupled with a decision support system (EWS/DSS). The paper discusses the main achievements of the tools as well as improvements needed to support their further use by the coastal community. The CRAF, a tool to identify and rank hotspots of coastal risk at the regional scale, provides useful results for coastal managers and stakeholders. A change over time of the hotspots location and ranking can be analysed as a function of changes on coastal occupation or climate change. This tool is highly dependent on the quality of available information and a major constraint to its application is the relatively poor availability and accessibility of high-quality data, particularly in respect to social-economic indicators, and to lesser extent the physical environment. The EWS/DSS can be used as a warning system to predict potential impacts or to test the effectiveness of risk reduction measures at a given hotspot. This tool provides high resolution results, but needs validation against impact data, which are still scarce. The EWS/DSS tool can be improved by enhancing the vulnerability relationships and detailing the receptors in each area (increasing the detail, but also model simulations). The developed EWS/DSS can be adapted and extended to include a greater range of conditions (including climate change), receptors, hazards and impacts, enhancing disaster preparedness for effective risk reduction for further events or morphological conditions. Despite these concerns, the tools assessed in this paper proved to be valuable instruments for coastal management and risk reduction that can be adopted in a wide range of coastal areas. [ABSTRACT FROM AUTHOR]

Transportation modeling and simulation play an important role in the planning and management of emergency evacuation. It is often indispensable for the preparedness and timely response to extreme events occurring in highly populated areas. Reliable and robust agent-based evacuation models are of great importance to support evacuation decision making. Nevertheless, these models rely on numerous hypothetical causal relationships between the evacuation behavior and a variety of factors including socio-economic characteristics and storm intensity. Understanding the impacts of these factors on evacuation behaviors (e.g., destination and route choices) is crucial in preparing optimal evacuation plans. This paper aims to contribute to the literature by integrating well-calibrated behavior models with an agent-based evacuation simulation model in the context of hurricane evacuation. Specifically, discrete choice models were developed to estimate the evacuation behaviors based on large-scale survey data in Northern New Jersey. Monte-Carlo Markov Chain (MCMC) sampling method was used to estimate evacuation propensity and destination choices for the whole population. Finally, evacuation of over a million residents in the study area was simulated using agent-based simulation built in MATSim. The agent-based modeling framework proposed in this paper provides an integrated methodology for evacuation simulation with specific consideration of agents’ behaviors. The simulation results need to be further validated and verified using real-world evacuation data. [ABSTRACT FROM AUTHOR]

Aim at the emergency collision avoidance scenarios caused by the close-range space debris, a learning-based spacecraft rapid trajectory planning method, which can adapt to complex constraints and satisfy the requirements of the business service and real-time replanning, is proposed in this paper. First, the emergency collision avoidance scenarios are initialized and the optimal multi-constraint avoidance trajectories are generated based on the Gauss pseudo-spectral method with corresponding feasibility checks. Then, taking the generated collocation points as the initial guess, the new trajectories are regenerated by finetuning scenarios. When the trajectory data is collected to some extent, the scenarios will be reset. The "state-action" data set can be established and extended by the above "plan-check-finetune-reset" loops. On this basis, two types of "state-action" neural networks and the corresponding supervised training method are specially designed based on ideas of multi-layer and bidirectional long short-term memory networks by considering continuous-discrete hybrid control characteristics of spacecraft actuators and interdependent temporal logical relationships in the data generated by dynamic differential equations. The designed networks are trained based on the established data set. Finally, spacecraft attitude-orbit maneuvering instructions can be resolved in real-time by trained networks according to the perceptive information for space debris. Simulation results show that the runtime of the proposed method in each step can be maintained within 10.4 ms, and the overall avoidance success rate can reach 87.6 % in Monte Carlo test conditions. [ABSTRACT FROM AUTHOR]

Enhancing the reliability of energy networks and minimizing downtime is crucial, making self-healing smart grids indispensable for ensuring a continuous power supply and fortifying resilience. As smart grids increasingly incorporate decentralized prosumers, innovative coordination strategies are essential to fully exploit their potential and improve system self-healing capabilities. To address this need, this paper presents a novel bi-level strategy for managing the self-healing process within a smart grid influenced by Hydrogen Refueling Stations (HRSs), Electric Vehicle Charging Stations (EVCSs), and energy hubs. This approach taps into the combined potential of these prosumers to boost system self-healing speed and reliability. In the initial stage, the Smart Grid Operator (SGO) conducts self-healing planning during emergencies, communicating required nodal capacities to prevent forced load shedding and outlining incentives for smart prosumers. Subsequently, prosumers schedule their activities and contribute flexible capacities to the SGO. Bridging the first and second stages, an adaptive Alternating Direction Method of Multipliers (ADMM) algorithm ensures convergence between the SGO and prosumer schedules within a decentralized framework. This strategy underwent implementation on a 118-node distribution system using GAMS. Results demonstrate that the proposed concept reduces Forced Load Shedding (FLS) by 32.04% and self-healing costs by 17.48% through effective utilization of smart prosumers' flexible capacities. Furthermore, outcomes indicate that the SGO reduces FLS by 6.69% by deploying Mobile Electrical Energy Storages (MEESs) and Mobile Fuel Cell Trucks (MFCTs) to critical nodes. • Introducing a bi-level self-healing strategy for smart grids with smart prosumers • Developing an adaptive ADMM for decentralized coordination among SGO and prosumers • Utilizing the flexible capacities of energy hubs, EVCSs and HRSs to reduce FLS • Embedding a robust strategy into the bi-level methodology to address uncertainties [ABSTRACT FROM AUTHOR]