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Abrupt air pollution accidents can endanger people's health and destroy the local ecological environment. The appropriate emergency response can minimize the harmful effects of accidents and protect people's lives and property. This paper provides an overview of the key emergency response technologies for abrupt air pollution accidents around the globe with emphasis on the major achievements that China has obtained in recent years. With decades of effort, China has made significant progress in emergency monitoring technologies and equipment, source estimation technologies, pollutant dispersion simulation technologies and others. Many effective domestic emergency monitoring instruments (e.g., portable DOAS/FT-IR systems, portable FID/PID systems, portable GC-MS systems, scanning imaging remote sensing systems, and emergency monitoring vehicles) had been developed which can meet the demands for routine emergency response activities. A monitoring layout technique combining air dispersion simulation, fuzzy comprehensive evaluation, and a post-optimality analysis was proposed to identify the optimal monitoring layout scheme under the constraints of limited monitoring resources. Multiple source estimation technologies, including the forward method and the inversion method, have been established and evaluated under various scenarios. Multi-scale dynamic pollution dispersion simulation systems with high temporal and spatial resolution were further developed. A comprehensive emergency response platform integrating database support, source estimation, monitoring schemes, fast monitoring of pollutants, pollution predictions and risk assessment was developed based on the technical idea of "source identification - model simulation - environmental monitoring" dynamic interactive feedback. It is expected that the emergency response capability for abrupt air pollution accidents will gradually improve in China. [Display omitted] [ABSTRACT FROM AUTHOR]

• HSRN is modeled as a two-layer network based on high-speed trains running in it. • Space-time failure effects of high-speed trains on HSRN robustness are investigated. • A method assessing robustness is proposed from function feature and topology feature. • Travel time and passenger flow are taken into account in the methodology. High-speed rail (HSR) has become a critical infrastructure and even has formed a network in some countries, which plays an increasingly significant part in the modern transport system and exerts increasingly obvious impacts on regional economic activity and human mobility. Although its interruption may cause serious consequences, researching its robustness doesn't attract attention until recent years. Based on previous researches on the issue, this paper proposes a comprehensive method to assess the robustness of high-speed rail network (HSRN) by combining the network function features with network topology features and modeling HSRN as a two-layer network based on the high-speed trains running in the network, in which the travel time and passenger flow are taken into account. Finally, taking China's HSRN (CHSRN) as a case study, this paper investigates the failure effects of HSR stations and high-speed trains on the robustness of CHSRN, especially the space-time failure effects of high-speed trains at three typical HSR stations. Results reveal that the failure effects of trains at the selected stations on the performance of CHSRN are limited, and the failure effects of trains at three typical stations on the performance are different as the different failure time, which is beneficial to prioritize the decisions on maintenance resource allocation and prepare emergency response plans. [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]

Small modular reactors (SMRs) are rapidly developed worldwide in recent years for their potential extensive applications. China is very active in promoting SMR design and construction. However, the current emergency preparedness (EP) guidance in China is developed based on the characteristics of large water-cooled reactors, and they are only partially applicable to SMRs. In our previous work, we have fully demonstrated from a technical perspective that it is feasible for China to establish a unified EP framework for all SMR designs based on the dose and consequence of an accident spectrum. Therefore, this paper will comprehensively discuss the costs and benefits of establishing a unified EP guidance for all SMR designs in China. Uncertainty analysis is also conducted for variables with great uncertainty. Through the study in this paper, it can be concluded that developing a new unified EP regulatory guidance for all SMR designs in China is cost beneficial in most cases. In addition, it will also bring other benefits that are hard to be quantified, such as the consistency of supervision, the clarity of the EP plan, and the improved engagement of all stakeholders during SMR development. In a word, it would always be beneficial to establish a new unified EP regulatory guidance for all SMR designs in the long term. • The fact that current emrgency preparedness guidance is only partially applicable for licensing SMR. • This paper proposes an operable process of cost-benefit analysis for regulatory guidance formulation. • Quantification approach is used to discussed cost-benifit analysis in guidance development. [ABSTRACT FROM AUTHOR]

Frequent occurrence of natural disasters has become one of the most serious challenges to human society. As rural communities are the forefront of disasters, enhancing the resilience of rural community is crucial to mitigate the impact of natural disasters and seek rapid recovery. This paper provides an analytical framework for changing trajectories of the resilience of rural communities after disasters. Taking the disaster area affected by the catastrophic debris flow in China as an example, we analyze the changing trajectories of rural community resilience after disaster in three villages with different damage levels from 2010 to 2020 based on the data from key person interviews, the disaster situation and development reports, village work records and farmers' questionnaires. We found that the changes of community resilience after disaster show obvious stages due to the difference in the intensity of effects of government intervention and community self-organization ability during disaster relief reconstruction period and self-recovery period. The resilience of communities with different disaster losses showed an increasing trend. However, during the disaster relief and reconstruction period, the resilience of the severely affected community was improved faster, followed by the most severely affected community, while the resilience of generally affected community was improved more slowly. During the self-recovery period, the resilience of the affected general community was improved faster than that of the other two communities. The evolution process of community resilience reflects the increasing role of self-organizational capacity in post-disaster community development, aligning with the overall global trend in rural community governance. In addition, there is a significant sequence in the improvement speed of different dimensions of community resilience at different stages after disasters. During the disaster relief and reconstruction period, the resilience of community management was improved faster and the resilience of environment was slower. During the self-recovery period, the economic resilience of community was improved faster and the social resilience was improved slower. Finally, some suggestions were put forward to strengthen natural disasters prevention and community resilience in disaster-prone areas. [Display omitted] • Provide an analytical framework for the changing trajectories of the resilience of rural communities after disaster. • Community self-organization ability play an important role in improving community resilience. • There are significant differences in resilience of communities with different disaster losses. • There is an obvious sequence in the improvement speed of different dimensions of community resilience after disaster. [ABSTRACT FROM AUTHOR]

Emergency supplies pre-positioning is crucial to improve the speed of response and mitigate the effects of disasters. According to the requirements of emergency preparedness for both civilian and military operations in China, the paper presents a problem of integrated civilian–military emergency supplies pre-positioning, the aim of which is to maximize both the expected military operational efficiency and the expected civilian operational efficiency while minimizing the overall cost of pre-positioning. To solve the problem, the paper proposes a solution method that uses a multiobjective algorithm to evolve a population of main location solutions and uses another single-objective algorithm to search optimal allocation sub-solutions for each main solution. Among different heuristics, we select water wave optimization (WWO) to design a multiobjective algorithm for the main problem and select biogeography-based optimization (BBO) for the subproblem, and demonstrate that they have competitive performance advantage in comparison with a set of well-known algorithms. [ABSTRACT FROM AUTHOR]

Coastal areas in China are particularly suffering from marine disasters, as they are attacked by marine disasters frequently, suddenly, catastrophically, leading to massive economic losses, even deaths. It is, therefore, necessary and urgent to build and reinforce a robust risk reduction arrangement for marine disasters in China. Marine disaster index insurance (MDII) has been recognized as an excellent emerging instrument for marine disaster risk management in coastal areas due to its dual advantages of avoiding moral hazard and adverse selection, besides immediate compensation. However, a clear understanding of its evolution, experience, and characteristics of MDII in China is remaining deficient. Taking the review and analysis of policy, this paper traces the history of evolution and investigates China's MDII pilots since 2013. It was found that China has made great progress in piloting MDII with the support of government policies in the past decade, which can be summarized in three stages: germination stage, initial development stage, and comprehensive development stage. However, some deficiencies and challenges have been identified from the investigation, including limited coverage, insufficient demand, inadequate supply, and imperfect subsidy policies. This paper proposes the following improvement strategies: improve the market ecosystem and regulatory system for MDII, enhance the ability of MDII insurers to deal with immediate huge claims, diversify the channels and forms of subsidy policies for MDII, raise public awareness of using MDII to manage marine disaster risks, and design scientific MDII products. [ABSTRACT FROM AUTHOR]

• A chance constrained stochastic programming model is proposed. • The uncertainty, dynamism and diversity of distribution system are considered. • The level assignment method is utilized to address distribution center disruptions. • The multimodal transportation system is introduced for road network disruptions. • An evolutionary algorithm is developed for large-scale cases. This paper presents a comprehensive approach to addressing the challenges of designing a reliable emergency logistics network under the dynamic uncertainty of natural disasters. The proposed multimodal transportation strategy based on level assignments can effectively mitigate the risk of facility disruptions and network uncertainty and enhance the adaptability of the system to diverse emergency supplies. The established two-stage chance constrained stochastic programming model and improved evolutionary algorithm are powerful tools for optimizing the network design and improving its resilience. Based on the real-world case of the Ya'an earthquake in China, the impacts of various practical constraints are analyzed. Several insights into the decision-making process of emergency logistics management are provided: managers should prioritize time utility over cost utility and use multiple aircraft and helicopters, supplies should be sorted into fewer and more important categories and placed in smaller packages for efficient transportation, multimodal transportation should be considered based on geographical and traffic conditions. [ABSTRACT FROM AUTHOR]

The nuclear accident that occurred in Fukushima, Japan has broadly affected the rapid development trend of the worldwide nuclear industry, causing many countries, including China, to adjust their nuclear policies. However, as the impact of the accident is fading away, nuclear policies are differentiating worldwide. Hence, analysing nuclear policies in different countries and their trends after the Fukushima accident has a practical significance of promoting the effective and safe development of the nuclear power industry. This paper focuses on the nuclear accident in Fukushima, Japan, and its wide impact. First, the changes of the developmental policies and the trends of the nuclear powers, including China, are summarised just after the accident in Fukushima occurred. Next, the nuclear power development of the nuclear powers and China in the last two years is analysed with real data. Finally, this paper systematically predicts the developmental direction of the global nuclear industry according to the actual nuclear development status in each country. [ABSTRACT FROM AUTHOR]

In China, the occurrence of a series of disasters and the tremendous loss in human lives expose the vulnerability the emergency system and the incomplete of the methodology. Faced with the needs of the practice, this paper conducted a comprehensive review both from the system and the methodology prospective. From the system prospective, this paper aims at analyzing the existing problems and the solution finding. From the methodology prospective, emergency facility location problem, emergency resource allocation problem, and emergency management technical support problem are examined. [ABSTRACT FROM AUTHOR]

Steel lattice towers are commonly used as supporting structures for overhead transmission lines. Extreme wind conditions like typhoons can cause significant damage on those steel towers and consequently jeopardize the safe operation of transmission lines. In the line sections where a river crossing is unavoidable, the steel lattice towers are designed to be much taller and stronger than regular towers owing to safety and operation considerations. In previous typhoon events in Hainan Province of China, none of the installed river-crossing towers have collapsed or experienced significant damages, except in the aftermath of super typhoon Rammasun in 2014, when two 95-m tall river-crossing suspension towers collapsed. This paper presents the structural failure analysis of these two collapsed lattice steel transmission towers: The improved genetic algorithm (IGA) was applied to solve the typhoon wind field model for improved resolution of wind records measured during the Rammasun event and based on of 10-min average wind speeds. The IGA used the field measurement data to generate time-varying typhoon key parameters that lead to a more accurate prediction of the wind field than the conventional method. Then a systematic procedure was developed to investigate the failure mechanism of steel lattice towers under fluctuating wind loads combined with rain loads with consideration of the aerodynamic characteristics of the transmission tower-line system. The results show that the root-cause of the structural failure of the two transmission towers was overstressing of the tower main leg members in the second panel above the ground. Based on the numerical simulation of typhoon wind loads and the structural failure analysis procedure applied in this study, a general reinforcement method of river-crossing towers is proposed and the implementation of a typhoon warning method is recommended to identify the potential risks in transmission lines and formulate corresponding typhoon emergency plans to reduce the losses caused by typhoons. • The failure of a river-crossing transmission line damaged by typhoon is analyzed. • Improved genetic algorithm (IGA) method was innovatively introduced to solve the typhoon wind filed. • Fluctuating characteristic and strong rainfall led to the tower collapse. • A new transmission line typhoon warning method is proposed. [ABSTRACT FROM AUTHOR]

• A method to assess the vulnerability of PTS comprising subsystem is proposed. • Critical areas of PTS are identified based on running timetable and position data. • Differences of vulnerability or critical area between subsystem and PTS are revealed. • A case of high-speed rail and air transport coupling system in China is researched. The public transport system (PTS) consists of different types of transport subsystems, such as high-speed rail (HSR), air transport (AT), conventional railway, expressway and waterway in a state, which exerts significant impacts on distribution of demographic and economic activities. And its interruption or failure may cause severe social and economic disruption. So the vulnerability analysis of the system attracts an increasing attention. However, most of previous researches mainly concentrate on the vulnerability modeling and analyzing of a single subsystem, neglecting the simultaneous interruption of components, and less considering the complementary effects of other subsystems from systematic perspective and the geographic interdependency among components. For trying to fill these gaps, this paper proposes a method to analyze the vulnerability of PTS composed of two or more transport subsystems and identity the critical area within the system based on the data of running timetable and position coordinates. Finally, taking HSR and AT coupling system in mainland China as a case study, we investigate the differences of vulnerability and critical areas between individual subsystem and overall system. Results reveal that the vulnerability variations and the critical areas distribution of the system are different from these of the subsystems. The research also finds that whether a component is critical depends not only on the system's feature, but also on the component's feature, whether an area is critical depends on economic development and population density of the area. These findings are beneficial to prioritize the plan, budget and maintenance of PTS, and prepare emergency response plans. [ABSTRACT FROM AUTHOR]

Accurate forecasting is required for the effective risk management of drought disasters. Many machine learning- and deep learning-based models have been proposed for drought forecasting, however, they cannot handle the temporal and/or spatial dependencies in the input data, causing unexpected forecasting results. In order to solve the challenging issue, in this paper we proposed the Heterogeneous Spatio-Temporal Graph (HetSPGraph), for drought forecasting. It includes three major layers: spatial aggregations including inter and intra aggregations, temporal aggregation, and a forecasting network. The main function of HetSPGraph is to learn the dynamic spatiotemporal correlations between the regions and to further predict the drought in different regions, based on which accurate drought forecasting can be achieved. Experimental forecasting results of the Standardized Precipitation Evapotranspiration Index (SPEI) in China indicated that the HetSPGraph model outperformed the traditional baseline methods including the Long Short-Term Memory model (LSTM), Convolutional Neural Network-LSTM (CNN-LSTM), Gated Recurrent Unit (GRU), Spatio-Temporal Graph Convolutional Networks (STGCN) and Geographic-Semantic-Temporal Hypergraph Convolutional Network (GST-HCN). Even for long-term forecasting (12 months), more accurate forecasting results, with the coefficient of determination R 2 higher than 0.89, can also be obtained by HetSPGraph compared to the other three models. The proposed HetSPGraph model has the potential for wider use in forecasting drought and other natural disasters. • A novel HetSPGraph approach with three layers is proposed for drought forecasting. • Dynamic spatiotemporal correlation of droughts at multi-time scales can be described by HetSPGraph. • The HetSPGraph model outperformed conventional Deep Learning models for drought forecasting. • The HetSPGraph model can keep its stable accuracy at long-term forecasting period (12 months). [ABSTRACT FROM AUTHOR]

Floods pose significant threats to farmers, and engaging in effective disaster avoidance behaviors can mitigate some of these risks for farmers in flood risk areas (FRA). However, due to farmers' bounded rationality characteristics, they often have a cognitive bias of blindly overestimating or underestimating flood risk, thus failing to perform correct disaster avoidance behaviors. Based on survey data from 180 farmers in FRA in southwest China, this study constructs an assessment method for farmers' subjective and objective flood risk and analyzes the characteristics of farmers' subjective and objective flood risk. A probit regression model explores the correlation between subjective and objective flood risk consistency and farmers' willingness to purchase natural disaster insurance (WPNDI). Results showed that of the 180 farmers surveyed, 63.89% had inconsistent subjective and objective flood risks, mainly reflected in an underestimation of objective flood risk. Farmers' subjective and objective flood risk consistency was found to be positively and significantly correlated with their WPNDI. When other conditions remain unchanged, for every unit increase in the consistency of farmers' subjective and objective flood risk, their WPNDI increased by 0.601 units on average. This paper originally proposed the hypothesis that the consistency of farmers' subjective and objective flood risk is positively and significantly correlated with their WPNDI, and proved the reliability of this hypothesis through research. These findings can provide policy references for disaster risk management and resilient disaster prevention system construction in FRA. [Display omitted] • The subjective and objective flood risk of farmers in FRA is assessed. • The correlation between farmers' subjective and objective flood risk consistency and WPNDI is analyzed by probit model. • Inconsistency of farmers' subjective and objective flood risk mainly reflected in underestimating the objective flood risk. • Farmers' subjective and objective flood risk consistency is positively and significantly correlated with their WPNDI. [ABSTRACT FROM AUTHOR]

The recent Russia-Ukraine war has reignited the world's biggest energy crisis in decades. As an important pillar of energy security, system resilience has attracted much attention. Previous research has focused on conception, composition and measurement. Few captured the dynamic features of resilience process of energy systems. In this paper, an input-output nested system dynamics (IO-SD) model is proposed to study the resilience of natural gas systems in a crisis. As a special feature, the model captures both inter-sectoral linkages and dynamic transmission of the crisis. The study highlighted and analyzed the role of emergency measures such as the release of stockpiles and emergency supplies. Later, the resilience performance of China gas system in 2017-crisis was examined in various aspects, e.g., economic loss and efficiency of emergency measures. We find that strategic storage could be beneficial to the resilience of NG systems. But expanding the storage size in isolation is not enough. Two key measures to enhance the resilience of the system are: (1) increasing the flexibility and operational efficiency of emergency facilities, e.g., storage releasing capability and gasification efficiency; and (2) building efficient infrastructure and services, enhancing connectivity of storage infrastructure and supply networks, and removing bottlenecks in regional interconnection. • Resilience plays an increasing important role in securing natural gas security. • The proposed resilience framework considers both restoring and economic performance. • The IO-SD model captures dynamic feature and intersection relations of gas system. • Enhance storage releasing rate can improve China gas system resilience effectively. • Strengthen connectivity of infrastructure can improve system resilience effectively. [ABSTRACT FROM AUTHOR]

• Built the storm surge disaster dataset with 50 indicators and 132 events. • Scientific data preprocessing methods ensure the reliability of the model. • Rank the importance of 50 indicators; hazard indicators are more important. • The indicator screening renders model simplification and workload reduction. • Satisfy machine learning models of storm surge disaster loss estimation are obtained. Storm surge is the most severe marine disaster in China, affecting the whole coastal area. Estimating storm surge disaster loss (SSDL) is significant to disaster prevention, sustainability and decision-making. Taking 11 provincial administrative regions in the coastal areas of China as the study area, this paper estimated SSDL grades based on four machine learning (ML) algorithms. A total of 132 pieces of official open-source data of storm surge disasters were collected and divided into a cross-validation set (CV set) and a test set. First, a comprehensive indicator system was constructed from three perspectives, covering the hazard (16) of disaster-causing factors, the vulnerability (22) and resilience (12) of disaster-bearing bodies, including 50 indicators. A few data preprocessing methods are implemented to improve the model performance such as normalization, SMOTE, etc. Then, Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Logistic model tree (LMT), and K-star were applied to construct the estimation model of SSDL grades. Principal component analysis (PCA) and recursive feature elimination (RFE) are adopted for an intelligent screening of the indicators. Finally, the models' performance is compared through Precision, Recall, F1 score and Kappa metrics. The results show that scientific and efficient data preparation is a strong guarantee for the reliability and stability of the models. RFE is verified more suitable for indicator selection in this paper compared with PCA. The importance ranking of RFE enhances the interpretability of the ML model, which shows that the hazard indicator is the most important, the vulnerability indicator is the second, and the resilience indicator is the least. The 27-indicator K-star model, with advantages of accurate estimation, strong generalization, and less workload, is the optimal SSDL estimation model. The number of input indicators of the optimal SSDL estimation model is 27, its CV Precision, Recall, F1 score, and Kappa are 0.838, 0.832, 0.827, and 0.776, and its Precision, Recall, F1 score, and Kappa for test set are 0.819, 0.786, 0.781, and 0.714, respectively. This paper provides a scientific basis for the government's decision-making and risk management, and it can be used as a typical demonstration case of SSDL research. [ABSTRACT FROM AUTHOR]

Highlights • Counted and analyzed the information of 47 Kilometer-Deep Mines in China. • Analyzed the situation of typical dynamic disasters in kilometer-deep mines. • Discussed the problems in the prevention and control of typical dynamic disasters. • Give out the measures for prevention and control of typical dynamic disasters. Abstract To ensure the safety of kilometer-deep mining and prevent the occurrence of typical dynamic disasters (rock burst and coal and gas outburst), this paper counted and analyzed the status quo of mining and typical dynamic disasters currently for 47 deep mines in China. The results show that the 47 deep mines are mainly distributed in 8 provinces; the proportions of mines affected by rock burst and coal and gas outburst are 53.2% and 29.79%, which are 29.23 times and 2.66 times the national average. Typical dynamic disasters in deep are more serious than that in shallow, the number of mines affected by rock burst will increase greatly in particular. The proportion of kilometer-deep mines that threatened by both coal and gas outburst and rock burst is 12.77%. Existing techniques used for prevention and control of typical dynamic disasters in kilometer-deep mines all refer to shallow mining; however, the following problems occur in deep mining: the mechanism of typical dynamic disasters and mechanism of complex disasters are not clear; the typical disaster early warning system is imperfect, and it's difficult to do precision mining in deep. Focusing on these problems, this study presents forward countermeasures from following directions: research on mechanism of deep typical dynamic disasters and coupling mechanism of complex dynamic disasters, establishing deep mine multi-parameter dynamic disaster monitoring and warning mechanisms and related equipment research, establishing deep mine typical dynamic disasters prevention and control systems, and research on the basic theory and equipment for precision mining in deep. [ABSTRACT FROM AUTHOR]

• Both models have applicability in prediction of group-occurring shallow landslides. • Combining Scoops3D and TRIGRS shows more effective landslide prediction result. • TRIGRS has the problem of over-prediction in the simulation of unstable area. • Fitting curve of rainfall threshold supports landslide warning in basin lacking data. The stability evaluation of rainfall-induced landslides using a physical determination model supports disaster prevention, but it is mostly applied to the area with few landslides, and there is a lack of quantitative study on rainfall and landslide stability. This paper combined the Scoops3D model with the TRIGRS model (3D) to predict the shallow landslide spatiotemporal distribution and compared the simulation results with those of the TRIGRS model alone (1D), aiming to obtain more accurate assessment results. At the same time, the relationship between landslide stability and accumulative rainfall was quantitatively fitted to improve the real-time landslide prediction system. We applied the 1D and 3D models to the July 21, 2013 group-occurring landslide event (976 shallow landslides) in the Niangniangba basin, China. The required geotechnical parameters of both models were acquired by field and laboratory tests. We calculated the pressure head over time using the TRIGRS model based on practical rainfall data and predicted the shallow landslide stability using the Scoops3D model according to the resulting piezometric surface. We compared the landslide stability spatial distributions of the two models under initial and saturated conditions with the landslide catalogue. The success rate of landslides predicted by 3D model is 4.20% higher than 1D model. A composite index to quantitatively evaluate both models' performances indicates over-prediction by the 1D model in the stable region, while the 3D model more effectively predicts shallow landslides with a smaller unstable region. The relationship between instability proportion and accumulative rainfall in the 1D and 3D model can be represented by y = 24.57 x 0.18 and y = 11.59 x 0.33 , respectively. The 3D model shows more conservative result, and the rainfall threshold analysis proposed in this paper can provide reference for the time of most landslides in the case of insufficient data, which is an important indicator for disaster early warning and prediction. [ABSTRACT FROM AUTHOR]

Knowledge of the spatial distribution of populations at finer spatial scales is of significant value and fundamental to many applications such as environmental change, urbanization, regional planning, public health, and disaster management. However, detailed assessment of the population distribution data of countries that have large populations (such as China) and significant variation in distribution requires improved data processing methods and spatialization models. This paper described the construction of a novel population spatialization method by combining land use/cover data and night-light data. Based on the analysis of data characteristics, the method used partial correlation analysis and geographically weighted regression to improve the distribution accuracy and reduce regional errors. China's census data for the years 1990, 2000, and 2010 were assessed. The results showed that the method was better at population spatialization than methods that use only night-light data or land use/cover data and global linear regression. Evaluation of overall accuracies revealed that the coefficient of correlation R-square was >0.90 and increased by >0.13 in the years 1990, 2000, and 2010. Moreover, the local R-square of over 90% of the samples (counties) was higher than the adjusted R-square of the general linear regression model. Furthermore, the gridded population density datasets obtained by this method can be used to analyse spatial-temporal patterns of population density and provide population distribution information with increased accuracy and precision compared to conventional models. [ABSTRACT FROM AUTHOR]

The JRODOS ( J ava-based R eal-time O nline D ecisi O n S upport) is a decision support system for off-site emergency management for releases of radioactive material into the environment. This paper documents the application of JRODOS by the Hong Kong Observatory in accident consequence assessment and emergency preparedness studies. For operational considerations, the most computational efficient dispersion model in JRODOS, ATSTEP, is adopted. Verification studies for JRODOS's ATSTEP model have been conducted. Comparison with tracer experiment results showed that under neutral atmospheric conditions and distances up to 50 km, the JRODOS simulation outputs were in general of the same order of magnitude with the tracer data. To further evaluate the capability of JRODOS in short-range simulation, a case study on the Fukushima nuclear power plant accident was also carried out. JRODOS was able to produce realistic simulation results which were comparable to the actual airborne monitoring data of the Cs-137 ground deposition from the Fukushima accident. Furthermore, the results of a comprehensive study to assess the potential consequences of accidents at a nearby nuclear power station are presented. Simulation using the French S3 source term for the Guangdong Nuclear Power Station at Daya Bay showed that the projected effective doses within Hong Kong remain far below the IAEA generic criteria of projected dose for urgent protective actions in sheltering/evacuation, while the projected equivalent dose in thyroid may meet the IAEA generic criteria for use of thyroid blocking agent at some areas in the northeastern part of Hong Kong, at distances of up to about 40 km from Daya Bay depending on the prevailing weather conditions in different seasons. [ABSTRACT FROM AUTHOR]

Port based transportations are key elements governing the operation of global maritime logistics. In China, the southeast coastline suffers from typhoons frequently every year and causes many disruptions to port operations and thus economic losses. Quantitative evaluation of coastal ports' economic losses induced by typhoons is quite demanding and can serve as guidance for port operation planning and disaster prevention. In this paper, a framework based on a hybrid wind numerical model for assessing the direct and indirect economic losses of coastline caused by typhoon-induced extreme wind is proposed. The estimation of port disruptions and their durations is realized based on the typhoon hybrid wind numerical model. For the direct economic losses, this model considers losses to the port based on the loading/unloading operations of containers. The disruption days and economic losses of 31 ports along China's coastline are selected. These include ports from the Bohai Rim Port Cluster, Yangtze River Delta Port Cluster, Southeastern Coastal Port Cluster, Pearl River Delta Port Cluster, and Southwestern Coastal Port Cluster. The analysis takes the record of typhoons from 2006 to 2020 for this investigation. The influence of time-varying variables on economic losses, such as economic growth rate, has been considered. For the indirect economic losses, the input-output models of 11 different provinces (or municipalities) are employed to analyze each sector. Features of adopting a climate model in the economic evaluations are discussed. [ABSTRACT FROM AUTHOR]

• Develop a complete problem-solving approach for group decision making. • Orient complex large-scale groups and time-sensitive emergency decision scenarios. • Design a feasible mechanism of group consensus strategies under time pressure. • Propose feedback recommendation strategies with a subgroup identification method. • A real case study of China's fight against the COVID-19 epidemic. Group Decision Making (GDM) has been well studied in the last two decades. Yet, two challenges exist: (a) how to resolve large-scale groups in GDM and achieve the consensus of preferences and (b) how to conduct GDM under risk and emergency conditions. In this paper, we develop a complete problem-solving approach for GDM that orients twofold settings of the complex large-scale group and the time-sensitive emergency decision scenarios. The crux of the matter is to design a feasible mechanism of group consensus strategies in the environment of time pressure and natural language preferences. To solve this problem, we propose a closed-loop mechanism of feedback recommendation strategies accompanied with a new subgroup identification method. This mechanism is underlain by a fourfold decomposition of complex large-scale groups, which entails multiple thresholds of group consensus, group hesitation, and time-related iteration of loops. Our mechanism and the whole GDM approach thoroughly orient the most intuitive representation of preferences - human natural language, which can be elicited and quantitatively formulated in probability linguistic preference systems. We illustrate the proposed approach through a real case study of China's fight against the COVID-19 epidemic. We verify that our mechanism can perfectly tradeoff between the effectiveness and the efficiency of complex large-scale GDM under risk and emergency. The results of this research provide proposals for mechanisms on large-scale GDM and are expected to contribute to emergency management such as epidemic controls, anti-terrorism, and other man-made or natural hazards. [ABSTRACT FROM AUTHOR]

On September 16, 2018, Typhoon Mangkhut, with record sustained winds of an estimated 110–115 mph, struck southern China, causing tremendous damage to the region. Since then, there have been many online discussions about the typhoon and how the government responded to it. In the domain of emergency response to disasters, the increasingly prominent role of social websites has been confirmed. This paper presents a case study of public attitudes and opinions regarding Typhoon Mangkhut that were collected from the Sina Weibo and WeChat websites, as well as from news websites. We then present a method for managing false online information in the wake of such natural disasters. The method consists of four phases: information extraction, sentiment recognition, opinion classification, and simulation analysis. In the last phase, a simulation experiment was conducted to assess how false information spreads. We used three scenarios in the simulation—namely, a real scenario, an isolation scenario, and an embedment scenario, each of which was based on a scale-free network based on a scale-free network. The results showed that the embedment strategy better controlled the transmission of false information than did the isolation strategy. In addition, we have provided some practical suggestions. This paper contributes to the literature on the emergency management of natural disasters. • Internet public opinions toward Typhoon Mangkhut were studied using data mining methods. • A supernetwork theory-based method for managing false online information is proposed. • Simulations regarding the real, isolation, and embedment scenarios were carried out. • Relevance of the Tobler's First Law in social media was tested. • This paper contributes to the emergency management of natural disasters. [ABSTRACT FROM AUTHOR]

It is well known that a large percentage of occupants in a building are evacuated together with their friends, families, and officemates, especially in China. Small group behaviors are therefore critical for crowd movement. This paper aims to study the crowd dynamic considering different social relations and the impacts of small groups on crowd dynamics in emergency evacuation. Three experiments are conducted in an 11-storey office building. In the first two experiments, all participants are classmates and know each other well. They are evacuated as individuals or pairs. In the third experiment, social relations among the participants are complex. Participants consist of 8 families, 6 lovers and several individuals. Space–time features, speed characteristics and density–speed relations for each experiment are analyzed and compared. Results conclude that small group behaviors can make positive impacts on crowd dynamics when evacuees know each other and are cooperative. This conclusion is also testified by four verified experiments. In the third experiment, speeds of evacuees are lowest. Small groups form automatically with the presence of intimate social relations. Small groups in this experiment slow down the average speed of the crowd and make disturbance on the crowd flow. Small groups in this case make negative impacts on the movement of the crowd. It is because that evacuees do not know each other and they are competitive to each other. Characteristics of different types of small groups are also investigated. Experimental data can provide foundational parameters for evacuation model development and are helpful for building designers. [ABSTRACT FROM AUTHOR]

• A new method for evaluating the failure of roof and floor strata under dynamic load impact is proposed. • The Energy Variation Coefficient (CV E) and Two Spatial Factors (S and α) are introduced to study the energy difference and aggregation characteristics of microseismic events respectively. • The vulnerability index model is established based on the principal component - entropy method, and the risk zoning map is determined by ArcGIS. • The different microseismic response characteristics of roof and floor are distinguished, which can be used for targeted disaster prevention and control. The disaster induced by dynamic loads in the coal mining process seriously affects the safe production of coal mines. A new method for failure evaluation of roof and floor strata under dynamic load impact is proposed in this paper. The key roof strata breaking and periodic weighting experienced by the 9212 working face of the Dongpang Coal Mine in China while passing through the abandoned roadway is taken as the research background. Firstly, the research stages are divided through theoretical analysis and microseismic monitoring, and then the plane, profile distribution and spatial position relationship of microseismic events in different stages are analyzed. After that, the energy variation coefficient ( CV E) and two spatial factors (S and α) are introduced to analyse the difference in the migration characteristics and damage degree of roof and floor strata under dynamic load impact. Then, the comprehensive weight is determined by principal component analysis and entropy weight method, and the failure evaluation model is established based on vulnerability index method; Finally, the risk grade zoning map of three stages is drawn by using ArcGIS technology. The research shows that the angle factor α can better describe the development direction of roof and floor fissures. The area factor S combined with the energy variation coefficient CV E can explain the development speed of fissures and the damage degree of coal and rock mass. According to the failure evaluation model, the corresponding risk degree of roof and floor in different stages can be comprehensively evaluated. The research results can be used as a reference for targeted disaster prevention and control in similar situations, such as large-area roof collapse and floor water inrush. [ABSTRACT FROM AUTHOR]

Rapid urbanization and exacerbated natural hazards, for example, floods, have been the big challenges for the sustainable development of many countries. Understanding the coupling coordination between urbanization and flood is important for promoting the sustainable development. However, there are few quantitative studies on the interaction between these two in China which is experiencing accelerated urbanization and climate change. In this paper, we developed an improved coupling coordination degree (CCD) model, applied Moran's I and projection pursuit model based on genetic algorithm to analyze the spatiotemporal distribution and spatial clustering effects of coupling coordination in urbanization–flood disasters system under the context of China's Five-Year Plan for National Economic and Social Development. Our results showed an upward trend of the comprehensive level of urbanization subsystem and a decreasing first and rising later trend of flood disaster subsystem in China from 2001 to 2018. There was a U-shaped relationship between the two subsystems during the research period. From 2001 to 2018, provinces with the moderate and slight uncoordinated development decreased by 9.68% and 16.13%, respectively, while provinces with the barely coordinated development increased by 16.21%. Spatially, there was a remarkable spatial dependency on CCD. The CCD between two subsystems in Northwestern China was higher than that in Southeastern China. Due to the effects of geographic position, rainfall, human activities, and economic development, the low-low (L-L) clusters concentrated in Southeastern China, and the high-high (H–H) clusters appeared in Northwestern China. Our results show that the improved CCD model, along with Moran's I analysis, can provide theoretical basis for policy makers to formulate reasonable measures for promoting the coupling coordination between urbanization and flood disasters. • Spatiotemporal distribution and dynamic evolution of nexus between urbanization and flood disasters were investigated. • Improved CCD model, Moran's I and projection pursuit model based on genetic algorithm were developed. • There was a U-shaped relationship in comprehensive level between urbanization and flood disasters. • CCD between two subsystems in Northwestern China was higher than that in Southeastern China. • There was a remarkable spatial clustering effects in CCD. [ABSTRACT FROM AUTHOR]

Eastern China, a populous and economically developed area, is severely affected by various meteorological disasters under the background of global climate change. In recent years, urban waterlogs caused by heavy rainfall has become a major concern threatening urban safety and socioeconomic development. This paper aimed to reveal the spatial pattern of waterlogging risk in 89 prefecture-level cities of Eastern China by a comprehensive analysis on extreme rainfall, socioeconomic exposure and urban adaptability. Hazard intensity index was calculated on 20-year return period value of maximum daily precipitation. Exposure index was based on the total urban population and local gross domestic product, while adaptability index was an integrated evaluation of drainage pipeline density, river network density and greening rate of urban area. Risk assessment was a synthesis judgment of hazard intensity index, exposure index and urban adaptability index, according to which the cities in Eastern China were classified into five risk levels. The six cities with the highest level of waterlogging risk were Shenzhen, Beijing, Maoming, Yangjiang, Shanwei and Dongguan, which indicates relatively stronger intensity of meteorological hazard, higher density of socioeconomic exposure and the weakness of urban adaptability in these cities. The high risk cities would suffer much more losses than other cities in Eastern China when hit by an extreme rainfall. This paper could provide a scientific basis for urban waterlogging risk identification and governance, as well as for technical measures development and capacity building of disaster prevention and reduction. • A research framework and technical methods for urban waterlogging risk assessment were proposed. • An indicator system for waterlogging risk assessment was raised based on hazard, exposure and adaptability factors. • Urban adaptability of waterlogging disaster was evaluated based on drainage pipelines, river networks and greening level. • Taking Eastern China as a pilot, the paper presented a city risk ranking for urban waterlogs. [ABSTRACT FROM AUTHOR]

• Establishing an assessment method of community resilience from multi-dimensions of social capitals, stakeholders and disaster phases. • Measuring the community resilience using SNA method from the perspective of six important disaster-adapting social capitals. • Proposing 4R implements on enhancing the community resilience in different disaster phases. • Helping to improve disaster management efforts in urban communities, especially those that are collaboratively managed by multi-stakeholders. Facing increasingly frequent disasters, the resilience concept are widely adopted to make up the limitations of traditional community disaster management. The relationships among community stakeholders along with their responsibilities and actions are important social capitals coping with community disaster, which have a significant impact on forming the community resilience. Accordingly, this paper is the first attempt to measure the community resilience using social network analysis (SNA) method from the view of social capitals of stakeholders. Firstly, six key disaster-adapting social capitals in the urban community are identified using systematic literature review (SLR). Then, the social capitals before, during and after the community disaster are calculated based on the SNA to characterize the level of community resilience. Finally, taking a waterlogging disaster in Nanjing city of China, some implements on enhancing the community resilience in different phases are proposed from four aspects: rapidity, robustness, redundancy and resourcefulness. The findings in the case study and the conclusions of this paper provides an easy method to calculate the resilience and social capitals of a community at different stages of a disaster, and also helps to improve disaster prevention and reduction efforts in urban communities, especially those that are collaboratively managed by multi-stakeholders. [ABSTRACT FROM AUTHOR]

Due to the national nutrition crisis and the adjustment of planting structure, millet has once again become a widely planted cash crop in Northern China. However, as the climate warms in recent years, drought has occurred frequently and repeatedly in Northern China, which has seriously threatened millet production safety in this area. Therefore, it is urgent to clarify the hazard of millet drought disaster and provide reference for the formulation of millet disaster prevention and reduction measures. In this paper, combined with the water demand of millet in each growth period, Standardized Precipitation Requirement Index (SPRI) was selected to identify and quantify the drought disaster of millet. Considering the impact of different degrees of drought in different growth periods, a hazard assessment model is constructed to evaluate the hazard of millet drought disaster in Northern China under the background of climate change. The results show that: (1) SPRI can well characterize the drought of millet in Northern China. (2) In the context of climate change, millet drought in Northern China shows a trend of increasing intensity and decreasing frequency, and it is possible that millet drought will be even more severe in the future. (3) Combined with the hazard assessment model, millet drought in Northern China will still be dominated by middle hazard in the future, but the areas with high hazard will gradually increase, mainly distributed in northeast China, central and western Inner Mongolia and northern Gansu. The research results can be of great practical significance for the adjustment of agricultural production structure in the future, especially for the sustainable development of agriculture and the formulation of measures to prevent and resist agricultural disasters. [Display omitted] • We apply SPRI to identify millet drought by considering actual water demand. • Future changes of millet drought evaluated by CMIP6-GCM models. • Hazard assessment model was presented combined with disaster loss coefficient. • The areas with high hazard of millet drought will gradually increase in future. [ABSTRACT FROM AUTHOR]

• We address a less studied topic: states' cultural response to disasters. • We propose a perspective based on the cultural sociology of the state and cultural theories of trauma, theodicy, and performance. • States' cultural response is " performance ": states project their definitions of situation of disasters and construct their positive self-images when interacting with the publics. • The efficacy of their performance depends on how effectively states' address the key components of the " nomos " or " meaning structure " about catastrophes and suffering and how this performance is influenced by their political structures. • Our theoretical perspective is illustrated in a comparative study of the 2008 Sichuan earthquake in China and the 2005 Hurricane Katrina in the US. • The findings also speak to an enduring debate over whether democratic or authoritarian regimes perform better in disaster responses. The sociology of disaster has paid scant attention to states' cultural response—states' effort to use meaningful narratives and symbolic actions to address issues about citizens' suffering and death to enhance their legitimacy and secure citizens' support. Our paper starts to address this gap by asking: How do states culturally respond to massive disasters? How effective are their responses? What can explain the efficacy of their cultural responses? We propose a perspective based on the cultural sociology of the state and cultural theories of trauma, theodicy, and performance. We illustrate this perspective in a comparative study of the 2008 Sichuan earthquake in China and the 2005 Hurricane Katrina in the United States. We argue that the efficacy of their cultural responses depended on whether and how effectively they addressed the key components of the meaning structure of disasters through compassionate reaction to citizens' suffering and death, convincing accounts of states' accountability, and cogent narratives about the long-term consequences of disasters on citizens. Both states struggled to address these issues at various points in their respective disasters, and their cultural responses were shaped by their political structures. The findings also speak to an enduring debate over whether democratic or authoritarian regimes perform better in disaster responses. We eschew the exclusive focus of the debate on administrative responses and its simplistic correlation between regime type and responses. [ABSTRACT FROM AUTHOR]

• We propose a new forest prediction model and use actual deforestation data for optimization and verification. • Use the XGBoost method to reconstruct deforestation intensity data. • Deforestation hotspots in China are mainly concentrated in the southern and southeastern regions. • Our model provides certain reference information for forest disaster prevention and forest management. Forests are important for the global carbon cycle, hydrothermal balance, and climate change. Human activities can exert a significant impact on forest ecosystems, thereby having the potential to alter their physical and chemical properties and thus affecting carbon, water, and heat budgets, and climate change. The historical reconstruction of the disturbance of global forests can help us understand the processes and patterns of human activities and global change. In this paper, we construct a deforestation prediction model using a Spearman correlation coefficient and implement the XGBoost method, using Python 3.6, for the reconstruction of deforestation intensity data from 2000 to 2019. Secondly, the selection of the driver indicators is done by using extreme difference regularization to unify the magnitude, and the potential deforestation area risk index is calculated in the form of equal weights. Finally, the actual deforestation data were used for optimization and validation. The model shows that the deforestation hotspots are mainly concentrated in the southern and southeastern regions of China and that there are large differences in deforestation in different provinces. In the future, the fine spatial and temporal patterns of deforestation in China during the historical period can be quantitatively reconstructed, which can provide some reference information for forest disaster prevention and forest management in China. [ABSTRACT FROM AUTHOR]

In this paper, we utilize network theory to model and study the coupled high-speed rail–aviation network (HSR–AN) in China. The HSR–AN depicting the transport services is modelled as a transfer network in P-space (TNP) in this research. The basic topology properties of the TNP and the corresponding critical components (e.g., nodes and areas) are analysed. As one type of spatial networks coupled two transportation networks, TNP of HSR–AN, counterintuitively, exhibits small-world properties which can be attributed by offering service connecting long-distance cities without transfer and complementary relationship between two transport modes. Additionally, an analysis of vital node (area) in vulnerability environment indicates that the importance of network components critically depends on the economic development level, population density, and geographical features in the corresponding city. Besides, the failure of high-speed rail station generates worse effect than that on airport in HSR–ANs. The findings are beneficial for enhancing the efficiency of HSR–ANs and developing emergency response plans for transport managers. [ABSTRACT FROM AUTHOR]

Upon the occurrence of a disaster, emergency supplies should arrive at disaster areas with different severities in the shortest time. Therefore, it is of pivotal importance to plan and schedule appropriate processes. This paper first elaborates on the principle of ordered arrival of emergency supplies. Then, it establishes a multi-objective multi-constraint emergency material scheduling (EMS) model based on the principle of ordered arrival. Subsequently, a hybrid ant colony optimization (HACO) is proposed to solve EMS. The computational results for 15 multi-depot benchmark instances are reported and the average error between HACO and the best known solution (BKS) is only 0.16% Then, real geological disasters from Hubei province in China are examined as a case study. Experimental results show that HACO performs better than non-dominated sorting genetic algorithm II (NSGA-II) and multi-objective ant colony algorithm (MOACO) in the case study and benchmark instances. • Study a large-scale real landslide disaster. • Establish a multi-objective multi-constraint EMS model. • Propose a HACO to solve the EMS model. • The HACO is tested on benchmarks and disaster network. [ABSTRACT FROM AUTHOR]

• The characteristics of residents' livelihood capital, evacuation and relocation willingness were analyzed. • The correlations between residents' livelihood capital, evacuation and relocation willingness were explored. • Livelihood capital is an important influence on residents' willingness to relocate. Evacuation and relocation are the most effective ways for residents to avoid disasters, and can ensure the property and safety of life. Therefore, it is of great significance for regional disaster risk management systems to explore the driving factors of residents' willingness to evacuate and relocate from earthquake-stricken areas. However, few scholars have quantitatively explored the relationships between residents' livelihood capital and their evacuation and relocation willingness, with consideration of residents' livelihood sustainability. According to the research objectives, stratified sampling and equal probability random sampling were used to determine the sample farmers, and the survey data of 327 households in Wenchuan and Lushan earthquake-stricken areas were obtained. This paper analyzed the characteristics of residents' livelihood capital, evacuation and relocation willingness, and used ordinary least squares (OLS) to explore correlations between residents' livelihood capital, evacuation and relocation willingness. The results show that: (1) the livelihood capital of residents is mainly comprised of human capital and social capital. Facing the threat of earthquake disaster, 40.98% and 28.75% of residents would be strongly willing to evacuate or relocate, respectively. (2) Livelihood capital is an important influence on residents' willingness to relocate; the higher the scores of natural capital, the weaker the willingness of residents to relocate. From the perspective of rural households' sustainable livelihoods, this study deepens our understanding of evacuation and relocation behaviors and decision-making by residents of the most earthquake-stricken areas. Thus, it provides useful information for regional disaster risk management. [ABSTRACT FROM AUTHOR]

• A comprehensive index evaluation system was established for rescue measures at containment vessel for China national nuclear emergency engineering rescue team. • A series of formulas was proposed to calculate the objective indicators' scores. • Fuzzy Comprehensive Evaluation method was applied to calculate subjective indicators' scores with a weight distribution estimating by Analytic Hierarchy Process method. • The proposed method based the established index system can not only evaluate the rescue teams' ability, but reveal the weaknesses and problems during the rescue process. To date, studies on nuclear emergency evaluation focus on emergency preparedness and medical rescue ability. The evaluation of Rescue Measures at containment vessel is critical for Nuclear Power Plant (NPP) emergency response under the severe accident. In this paper, a comprehensive evaluation index system was established for rescue measures of China national nuclear emergency engineering rescue team at containment vessel with the consideration of timeliness and accuracy of rescue process, rescuers' safety and rescue effects. A series of formulas was proposed to calculate the objective indicators' scores, meanwhile, Fuzzy Comprehensive Evaluation method was applied to calculate subjective indicators' scores with a weight distribution estimating by Analytic Hierarchy Process method. A case study on M310 NPP was carried out to demonstrate the evaluation method. The proposed method based the established index system can not only evaluate the rescue teams' ability, but also reveal the weaknesses and problems during the rescue process. [ABSTRACT FROM AUTHOR]

More than 5000 landslides have been identified in the Three Gorges Reservoir (TGR) area since the impoundment in 2003, which posed great threats to the residential houses, transportation on the Yangtze, and dam stability. This paper focuses on the evaluation of the coupled influence of rainfall and reservoir water on deformation characteristics and failure evolution of the Outang landslide (in China) based on field investigations, a statistical Pearson cross-correlation coefficient analysis, multi-technique monitoring data, and a numerical model. This instability is a reactivated ancient landslide with a volume of approximately 90 Mm3 located in the Three Gorges Reservoir. The slope behavior is featured with a short period of fast displacement and a relatively long period of semi-constant displacement rates per hydrological year, and displacements velocities quicken gradually upslope. Rainfall and reservoir water level are the main triggering factors of slope movement existing in the upper and the lower parts of the landslide, respectively. Based on the numerical results, the failure evolution of this landslide is a compounded push-retrogression-type failure process involving: a) a first rupture at the toe caused by the reservoir water level that will rapidly mobilize a second and subsequent collapses upwards (retrogression type); and b) the rock mass at the upper part will be pushed out and failed downslope (push type) due to precipitation. These results provide a comprehensive analysis of the Outang landslide, which is critical for its management, as well as for disaster prevention and mitigation of analogous reservoir-induced landslides. • About 6-year monitoring data of deformations and hydrological factors were analyzed. • The slope deformation characteristics were addressed. • The primarily hydrological factors for the landslide movement were revealed. • A possible slope instability evolution was proposed. [ABSTRACT FROM AUTHOR]

• Machine learning (ML) models can determine the rainfall flooding threshold as a line. projected in a plane spanned by two principal components, thereby providing a binary result (flood or no flood). • Compared to the conventional critical rainfall curve, the proposed models, especially the subspace discriminant analysis, greatly raise the accuracy (ACC) to 96.5% and lowering the false alert rate to 25%. • Rainfall threshold based flood prediction can be executed rapidly and simply, this method allows decision makers time for a high-level assessment of flood risk, providing valuable lead time for citizens in the flood-prone areas to be warned. Urban pluvial flooding is a threatening natural hazard in urban areas all over the world, especially in recent years given its increasing frequency of occurrence. In order to prevent flood occurrence and mitigate the subsequent aftermath, urban water managers aim to predict precipitation characteristics, including peak intensity, arrival time and duration, so that they can further warn inhabitants in risky areas and take emergency actions when forecasting a pluvial flood. Previous studies that dealt with the prediction of urban pluvial flooding are mainly based on hydrological or hydraulic models, requiring a large volume of data for simulation accuracy. These methods are computationally expensive. Using a rainfall threshold to predict flooding based on a data-driven approach can decrease the computational complexity to a great extent. In order to prepare cities for frequent pluvial flood events – especially in the future climate – this paper uses a rainfall threshold for classifying flood vs. non-flood events, based on machine learning (ML) approaches, applied to a case study of Shenzhen city in China. In doing so, ML models can determine several rainfall threshold lines projected in a plane spanned by two principal components, which provides a binary result (flood or no flood). Compared to the conventional critical rainfall curve, the proposed models, especially the subspace discriminant analysis, can classify flooding and non-flooding by different combinations of multiple-resolution rainfall intensities, greatly raising the accuracy to 96.5% and lowering the false alert rate to 25%. Compared to the conventional model, the critical indices of accuracy and true positive rate (TPR) were 5%-15% higher in ML models. Such models are applicable to other urban catchments as well. The results are expected to be used to assist early warning systems and provide rational information for contingency and emergency planning. [ABSTRACT FROM AUTHOR]

• A typical weathered granite tunnel which occurred five water and mud inrush disasters was presented as a case study. • A seepage-erosion coupled model was used to evaluate the safety thickness for this project. • Twenty-five grouting cycles conducted in the field were described to analysis the safety thickness. • A detailed calculated method for safety thickness under various ground conditions was developed. The Junchang tunnel is a part of a major project in the Censhui highway in Guangxi province, China. The weathered granite part of the tunnel is approximately 180 m long with two lines and four lanes. Numerous water and mud inrush disasters frequently occurred in the weathered granite area, leading to serious economic loss and challenge to the constructors. Therefore, continuous grouting was carried out for the entire weathered part of the tunnel. This paper describes its curtain grouting for disasters prevention. Detailed discussion is presented on the safety thickness after grouting and the challenges encountered under different hydraulic-geological conditions. Specifically, a water and mud inrush model for the nonlinear flow and mass transfer behavior of weathered granite was used to evaluate the safety thickness in this project. Then, field studies with twenty-five grouting cycles and excavations were conducted to investigate the effective safety thickness. Negative correlation was observed between the safety thickness and the rock classification index [BQ]. Numerical simulation and field tests demonstrated that the thickness l should be larger than 3 m, and, in most cases, 5 m was sufficient when [BQ] < 72.5. When [BQ] ≥ 72.5, l should be between 1.5 m and 3 m. Based on the field data, an empirical formula was developed to determine the thickness under different values of [BQ]. The research findings serve as an important reference for future projects in similar stratum. [ABSTRACT FROM AUTHOR]

• A new model for urban resilience considering adaptability, resistance and recovery was proposed. • Valid factual information during the Morakot was adopted to validate this model. • Critical parameters of the model such as damage intensity, experience coefficient etc. were studied. • Some optimal suggestions were provided to efficiently improve urban resilience. Due to the greater emphasis on cities' preparatory and recover ability for disaster, urban resilience is widely concerned, which is different from the traditional management mode of natural disaster and provides a new theoretical framework for urban security governance. In this paper, considering adaptability, resistance and recovery as the main characteristic factor of urban resilience, an urban resilience model was developed. This model could be used to simulate the resilience of a city under different disaster scenarios. Valid information was collected from Taiwan of China during the Morakot, and the urban resilience model was tested by the practical data, and then the resilience in Taiwan was analyzed by the constructed model. The results show that the model could reflect the resilience change of cities during disasters to some extent. Further, according to the disaster characteristics, five disaster scenarios were set to analyze various resilience characteristics. In addition, the critical parameters of the model were studied. We found that the greater intensity of disaster damage, the more serious damage of urban system function, and the earlier peak time of disaster damage intensity appears, the faster damage rate of urban system function will be. Furthermore, the larger coefficient of external disaster relief capability, the lower loss of urban system function and the better recovery capability. The higher coefficient of disaster experience, the richer the disaster response experience, the more perfect urban infrastructure of disaster response, and the smaller loss of urban system function. Based on our results, we analyze the characteristics of urban resilience and provide some optimal suggestions to efficiently improve urban resilience. [ABSTRACT FROM AUTHOR]

• Ill-considered urban planning around Niwan gully may multiply the human and economic losses caused by debris flows. • A depth-integrated model was adopted to determine the hazard zones of debris flows with different recurrence intervals. • The debris flow inundation and evolutionary trajectory are shown to be reasonably comparable with historical debris flows. • This study established a relatively complete system to implement hazard and risk assessment in the study area. The highly geologically active, structurally complex Bailongjiang river basin, Longnan City, Gansu Province hosts many debris-flow gullies. The recent Wenchuan and Jiuzhaigou earthquakes have increased the amount of source material available in these debris flow gullies, and the disturbed nature of the slopes makes them prone to failure under high rainfall, increasing the risk of large-scale debris flows. Moreover, poor urban planning in the region may multiply the human and economic losses caused by debris flows. Therefore, it is necessary to determine the potential hazard zones associated with such debris flows and to implement debris flow hazard and risk assessment. In this paper, Niwan gully is chosen as the study object, as it is characterized by repeated debris flow outbreaks and an ill-considered urban planning at the outlet of the gully. A depth-integrated continuum method is adopted to determine the potential hazard zones of debris flows with different recurrence intervals. The debris flow inundation and evolutionary trajectory are shown to be reasonably comparable with historical debris flows. A two-factor model that couples maximum flow depth with momentum is used to better classify the hazard zones. This study also provides a complete and efficient means to determine the values of debris flow parameters and to implement a hazard and risk assessment based on the solid material reserve method and a probabilistic method. This proposed approach efficiently generated a debris flow risk distribution map that can be used for effective disaster prevention in the Bailongjiang River basin. [ABSTRACT FROM AUTHOR]

The Datong Basin is part of the regionally extensive Fenwei Graben System, a major tectonic zone impacted by active tectonics and the resulting damage to life and infrastructure. The basin also contains the Shizhuang ground fissure system. In this study, the likely origin, surface extent, and fissure subsurface characteristics are explored by traditional methods: surveying, mapping, trenching, drilling, and seismic exploration. The Shizhuang ground fissure is located at the intersection of the Xiaoshikou fault and the southern Liuleng margin fault in Yingxian County. It has a length of 2.1 km, excellent continuity, and semi-elliptical surface morphology. The dislocation of strata induced by the fissure increases significantly as the depth increases. The strata bend downward and form typical traction structures near the fissure plane, indicating that the fissure has prominent synsedimentary fault characteristics. The fissure causes serious damage to houses, farmlands, roads, and public facilities, featuring large tensile cracks and vertical dislocations. The resulting data and interpretations indicate that: (1) the ground fissures connect to two major subsurface active faults, which lay the tectonic foundation for the fissure; (2) accelerated groundwater extraction has reactivated the underlying faults and exacerbated fissure development; and (3) mitigation is best achieved by cessation of excessive groundwater pumping, appropriate engineering design, and local avoidance for placement of critical structures. This paper provides a heretofore generally unknown example, promotes research on the mechanisms of fissures, and has guiding significance for disaster prevention and reduction in this area. • Various characteristics of the Shizhuang ground fissure were determined. • Trenching, drilling, and geophysical prospecting were used to examine the fissure. • Tectonic and non-tectonic factors were used to analyze the formation mechanisms. • Regional stress and fault activity controlled the development of the fissure. • Groundwater pumping and a settlement funnel accelerated the fissure extension. [ABSTRACT FROM AUTHOR]