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To establish and improve the theoretical framework and operation mechanism of emergency information sharing, and to establish the index system and evaluation method of the efficiency of emergency information sharing are the key work to improve the disaster prevention and mitigation capacity of coastal cities in China. In this paper, the synergetic theory is introduced into the construction of emergency information sharing framework. By clarifying the operation relationship between the participants and the information transmission, the framework and operation mechanism of emergency information sharing are constructed. Around the 3 dimensions of emergency information sharing mechanism construction, resource guarantee ability and collaborative driving force of emergency information sharing, the evaluation index system of emergency information sharing collaborative efficiency in coastal cities is proposed, and the evaluation method of emergency information sharing collaborative efficiency based on coupling degree model is proposed. The empirical results show that the efficiency of emergency information sharing in coastal cities in China is generally on the rise, and the contribution rate of the construction level of emergency information sharing mechanism is higher than that of the resource guarantee ability and the collaborative driving force of emergency information sharing, but the efficiency of emergency information sharing in coastal cities in China is still at the bottom level. The research results provide theoretical basis and methods for the emergency management departments of coastal cities in China. [ABSTRACT FROM AUTHOR]

As natural disasters often cause catastrophic damages, disaster management must be considered. Relief logistics planning in disaster management is a dynamic process that includes deliberate processes taken before and after a disaster. This paper presents a robust optimization model for relief logistics planning covering before and after disaster actions to minimize the total cost. In this model, demand and traveling time between nodes are non-deterministic parameters, and scenario-based robust optimization is applied to handle uncertainty. The model is an integrated multi-echelon, multi-period, and multi-commodity vehicle routing problem considering the fair distribution of goods and services in a way that social costs are minimized. Vehicles can enter nodes and exit them several times. In this paper, a genetic algorithm (GA) is considered the solution approach. To estimate the GA parameters, an experimental design based on the Taguchi method is considered. A roulette wheel method is applied to select a child from parents for the next generation. Single-point crossover and swap mutation operators are used to generate the new child. Test problems in different scales are generated and solved using GAMS and GA. The results indicate that GA solutions have 3.75% gaps in average with optimal solutions. Finally, the model is applied with the data of a real case (Tehran). The results also indicate that it is better to consider the appropriate capacity for supply centers and distributors and select vehicles. [ABSTRACT FROM AUTHOR]

Natural disasters cause heavy casualties and financial losses annually. To reduce these damages, the rescue teams need to be planned effectively. In this regard, in this research, a mixed-integer programming model is offered to allocate and schedule rescue teams in a response phase of disaster management under uncertainty. The objective function minimizes the incident's total weighted completion times. The literature review shows that the uncertain condition and time windows have been less addressed in the previous studies. To cover these gaps, this paper investigates the problem under uncertainty and considers time windows for incidents. Besides, the fatigue effect is considered in this paper. Accordingly, within a planning horizon, incident processing times are not fixed. Since the considered problem is an NP-hard one and exact methods cannot solve it within a reasonable amount of time, this research develops a heuristic-based simulated annealing algorithm. The presented model is solved using the developed algorithm and three known meta-heuristic algorithms. Then, the results obtained by algorithms are compared and analyzed. Finally, the sensitivity analysis is carried out on some crucial parameters of the presented model, and the related results are reported. [ABSTRACT FROM AUTHOR]

Twitter is an intensely utilized platform for disaster events and emergencies. Therefore, Twitter is an important resource for providing the essential information. Named entity recognition (NER), which is the process of determining the elementary units in a text and classifying them with pre-defined categories, plays a significant role to extract essential and usefulness information. However, NER is a challenging task due to the utilized informal text in the Twitter platform such as grammatical errors and nonstandard abbreviations. In this paper, recurrent neural network (RNN)-based approaches considering diversity of activation functions and optimization functions with NER tools are utilized to extract named entities such as organization, person, and location from the tweets. Inputs for RNN models are provided via two different NER tools which are natural language toolkit (NLTK) and general architecture for text engineering (Gate). Then, pre-labeled data are trained via GloVe word embedding technique, and RNN model variants such as LSTM, BLSTM, and GRU are demonstrated. Therefore, outperforming models among RNN variants are presented for predicting named entities. Yellowbrick interpreter is used for evaluation of the proposed method and Wilcoxon signed-rank test are applied on results of two different data sets to demonstrate consistency of the proposed method. In addition, comparison is made with existing machine learning methods. The experiments by utilizing the Nepal earthquake Twitter data set show that the RNN-based approaches achieve good results in finding named entities. In emergencies, the results of this paper can help in reducing the efforts of event location detection and provide better disaster management. [ABSTRACT FROM AUTHOR]

Presently, the Internet of Things (IoT) and big data analytics technology offer enormous opportunities to disaster risk management services. Recent disaster risk management patterns involve disaster risk reduction to avoid future disaster risks, minimize current disastrous risks, resilience building, and disaster loss reduction. The main challenges in disaster management are communication within a disaster zone which is being disrupted. In this paper, the Internet of Things assisted disaster risk management framework (IOTDRMF) has been proposed for technical resources to communicate the emergency time and better visibility into reliable and prompt decision-making through observing, evaluating, and forecasting natural disasters. This IOTDRMF utilizes big data analytics to analyze disaster risk management build a kind of spatial data communication network infrastructure, making it a priority to establish rules, protocols, and knowledge sharing. The experimental results show the IOTDRMF, and big data analytics compensate for a weak communication network infrastructure and better decision-making to handle disaster risk management. [ABSTRACT FROM AUTHOR]

Nowadays the fuzzy graphs gained popularity due to their wide applications in different areas of science, engineering, social sciences, etc.In this paper, we consider covering problem in fuzzy graph. Different types of covering problems have been defined in this paper. Almost all problems are new. For each of these problems separate study is required. Also, the covering set, strong covering set, minimal covering set, etc., are defined and explained with examples. In a graph, each vertex can cover only those vertices that lie within its covering radius along shortest path and no vertex can cover itself. Here, an algorithm is designed to find the different types of covering sets on a fuzzy graph. An imprecise number, instead of a real number, namely interval number and triangular fuzzy number are considered as arc length of a fuzzy graph. To the best of our knowledge no works are available for covering problem on fuzzy graphs/networks. An application of covering set in natural disaster management is discussed to highlight the importance of the covering problem. In this application, an algorithm is designed to find all the facility vertices (or supply vertices) for given vertex and covering radius. [ABSTRACT FROM AUTHOR]

Emergency responses bear the characteristics of uncertainty and possess multi-attributes in decision making. This paper applies the interval evidential reasoning approach and the interval-valued hesitant fuzzy TODIM (IVHF-TODIM) method to tackle the dynamic emergency decision-making problem. We introduce a function to obtain the gain and loss degrees through the geometric area method. The gain and loss matrices of the interval belief degrees are found probabilistically. A new approach to obtaining the dominance degree matrix is proposed. From the IVHF-TODIM method, the overall dominance degree is established to provide the ranking of the decision alternatives. A recent case of selecting an emergency decision alternative for a large bushfire is used to validate the proposed method, followed by a comparative analysis. [ABSTRACT FROM AUTHOR]

Due to the frequent occurrence of accidental and destructive disasters, it is essential to improve the performance of emergency systems. Facing the fact that the performance of emergency system depends on various factors and it is not feasible to optimize all these factors simultaneously due to the limitation of resources. A feasible solution is to select and improve some important factors. In this paper, a novel enhanced fuzzy evidential decision-making trial and evaluation laboratory (DEMATEL) method to identifying critical success factors (CSFs) is proposed. In the proposed method, we combine Dempster-Shafer evidence theory and DEMATEL method. Firstly, direct relations between factors are evaluated by multiple domain experts with intuitionistic fuzzy numbers (IFNs). Then, IFNs are transformed to basic probability assignments (BPAs) and can be combined by Dempster combination rule. In addition, the uncertainty and fuzziness of BPAs due to the lack of knowledge are taken into consideration to make final decision. Finally, implementing DEMATEL method, we can figure out cause-effect categories of factors with the DEMATEL method. The cause factors are identified as CSFs. The proposed method can well tackle subjectivity and fuzziness of experts evaluations. Based on the proposed method, the optimization of emergency management can be significantly simplified into optimizing CSFs. Through optimizing these CSFs, the performance of the whole systems can be significantly improved. [ABSTRACT FROM AUTHOR]

Indeterminacy is an intrinsic characteristics of real-world data. Where they originate from credible experiments, probability theory is a robust tool to manipulate this type of indeterminacy. However, this is not always the case, and referring to the domain expert belief is an alternative approach. Baoding Liu initiated an axiomatic basis of uncertainty theory to answer this kind of indeterminacy. Dominating set with its different versions has a wide range of applications in many fields, while the practice suffers indeterminacy with no reliable data in most cases. In this paper, we investigate the minimum weighted dominating set with indeterministic weights in two cases. The weights in the first one have probability distribution and in the other one uncertainty distribution which they are based on the belief degree of the domain expert. In both cases, the objective function of model is not defined. To overcome this difficulty, based on probability and uncertainty theory, deterministically two different models are constructed. The first model considers an α-chance method, and the second exploits the expected value of the uncertain variables. Both models are converted to deterministic ones resulting to the so-called α-minimum weighted dominating set, and the uncertain minimum weighted dominating set, respectively. A prototype application in earthquake relief management is provided, and the performance of models is experimented in a concrete illustrative example. [ABSTRACT FROM AUTHOR]

A multi-attribute decision-making method based on heterogeneous information and set pair analysis is proposed, considering the balance of the contradictions between information representation and information processing in the decision-making process. The method is applied to decision-making problems in which the attribute weights are completely unknown and the attribute values are characterized as heterogeneous information (including exact numbers, interval numbers, intuitionistic fuzzy numbers, interval intuitionistic fuzzy numbers, neutrosophic numbers, interval neutrosophic numbers, picture fuzzy numbers, and Pythagorean fuzzy numbers). First, various transformation methods for converting heterogeneous information into the connection numbers (CNs) of set pair analysis are introduced and defined, and the heterogeneous information is uniformly converted into CNs for ease of processing. Second, the attribute weights of the heterogeneous information are obtained using the information entropy of the CNs. Thereafter, the alternatives are ranked by the VIKOR method. Moreover, we use the method of setting a threshold for the compromise evaluation value, and the clustering method to group the alternatives to provide more auxiliary decision making. Finally, the feasibility and effectiveness of the proposed method are demonstrated by a typhoon disaster evaluation example, and the advantages of the proposed approach are emphasized by performing a multi-faceted comparative analysis with other methods. Among them, the time complexity of the proposed algorithm can be simplified as O m , which demonstrates an improvement on the simplified time complexity O m 2 of the two existing methods. Particularly in the context of big data, the advantages are apparent. [ABSTRACT FROM AUTHOR]

A real-time and accurate storm surge prediction model is of great scientific value and practical significance in reducing human casualties and economic losses in coastal areas. For this purpose, a novel storm surge multi-step forecasting framework integrating time-varying filtered empirical modal decomposition (TVF-EMD), fast Fourier transform (FFT), phase space reconstruction, convolutional neural network (CNN), and long short-term memory neural network (LSTM) is proposed in this study. Among the supplementary strategies, the TVF-EMD is used to extract the fluctuation features of the storm surge data and decompose the storm surge time series into a number of IMFs; the FFT is employed to calculate the frequency values of each IMF, and the subsequences with similar frequency values are combined and reconstructed. Meanwhile, CNN is adopted to predict the preprocessed high-frequency components, while the low-frequency is predicted by LSTM. Subsequently, the ultimate prediction results of the raw storm surge are calculated by superimposing the predicted values of all components. Three datasets collected from southeastern coastal region of China and five relevant comparison models are carried out to evaluate the proposed approach, where the corresponding results demonstrate that: (1) data preprocessing strategy applying TVF-EMD and FFT can significantly improve forecasting performance; (2) the TVF-EMD decomposition method is more effective under the influence of low sampling rate and noise; (3) by observing the characteristics of the subsequence, the prediction by modules can achieve better results. In addition, in order to apply the model to engineering, the proposed model is transferred to the small data domain as a pre-trained model using a transfer learning approach. According to the prediction results of Wenzhou station in the 7821-storm surge event, it can be found the proposed model still has good robustness and generalization ability even though the new sample data is small. This also proves that the model has strong practical value in coastal storm surge warning as well as disaster prevention and mitigation. Overall, the storm surge prediction framework proposed in this study has higher prediction accuracy and transferability, and can provide scientific and reasonable theoretical guidance for the emergency management to develop disaster prevention strategies. [ABSTRACT FROM AUTHOR]

Public health events have done great harm. Emergency management requires the joint participation of multiple parties including government department, pharmaceutical enterprises, citizens and new media. Then, what are the effects of different strategy choices in participation of citizens and new media on emergency management? To answer the question, we construct a four-party evolutionary game model, considering the citizens' two participation ways consisted of true evaluation and false evaluation, and the new media's two participation ways consisted of report after verification and report without verification. This is of more practical significance than simply studying whether citizens and new media participate in emergency management or not because citizen and new media participation does not represent the completely positive behavior. Then, we conduct the evolutionary stability analysis, solve the stable equilibrium points using the Lyapunov first method and conduct the simulation analysis with MATLAB 2020b. The results show that, firstly, the greater the probability of citizens making true evaluation, the more inclined the government department is to strictly implement the emergency management system; secondly, when the probability of citizens making true evaluation decreases, new media are more inclined to report after verification, and when new media lose more pageview value or should be punished more for reporting without verification, the probability that they report without verification is smaller; thirdly, the greater the probability of citizens making false evaluation, the less enthusiasm of pharmaceutical enterprises to participate in emergency management, which indicates that false evaluation is detrimental to prompt pharmaceutical enterprises to participate; what's more, the greater the probability of new media reporting after verification, the greater the probability of pharmaceutical enterprises actively participating, which shows that new media's verification to citizens' evaluation is beneficial to emergency management. [ABSTRACT FROM AUTHOR]

Management of any uncertain state or situations allows everything to be backed up to its normal position. Disaster planning has been important for environmental planning in recent times. In every world, researchers, geographers, climatologists and regional planners are going to detect and minimize the impact of natural disasters. The recent work presents flood-prone areas in the Ajoy River basin of India, considering the "Support Vector Machine", "Extremely Randomize Trees" and "Biogeography Based Optimization" (BBO) method in GIS platform. Various geo-environmental variables are known to determine the final outcome. "Area Under Curve" (AUC) values of "Receiver Operating Characteristics" were used to validate and determine the accuracy of the predicted models. AUC shows the state or nature of the flooding. If the outcome of AUC values indicates more than 0.95, then the likelihood of flooding will be high BBO gave us the most optimal outcomes in this research out of these models (AUC = 0.98) and assured us that BBO is an influential tool for assessing flood-prone areas in eastern India and helps to take measures for strategic management and preparing for progress. [ABSTRACT FROM AUTHOR]

Unpredictable occurrence of any disaster emerges immeasurable demand in an affected society. Importance of immediate response in the aftermath of disaster is a crucial part of humanitarian logistic. Resource redistribution among the affected areas makes the optimal allocation in this chaotic situation. The research work has introduced a transportation plan considering the redistribution of resources from those areas which has already acquired relief and restored the normal condition to those areas still not being recovered from the effect of calamities. This research plan is developed to minimize the total cost of the relief operation as well as optimal allocation of the resources. The optimal allocation amidst the disruption of some resource storing points in the aftermath attack of disaster is also one of the key factors of the research. This research work has a great impact for decision-maker to derive an appropriate decision-making in such an anarchic situation of critical humanitarian supply chain. Due to the complexity of disaster, the model is considered in mixed uncertain environment. A numerical study is also performed to show the smooth functioning of the mathematical model assuming the uncertainty by trapezoidal neutrosophic number. Also, trapezoidal fuzzy number is implemented for uncertain parameters of the mathematical model and hereby compared with trapezoidal neutrosophic number. [ABSTRACT FROM AUTHOR]

Natural hazards such as tropical cyclones is a topic of wider interest and operational forecast of their landfall, maximum sustained winds, storm surge height and associated extent of inland inundation is a challenging topic having wider socio-economic implication. Recent advancements in computational power and development of sophisticated models have resulted in better understanding the dynamics of atmosphere and ocean. Forecast quality have improved to a great extent however there are still constraints in computation time and cost for real-time operations. Soft computing techniques in the broad domain of computational intelligence are widely recognized today having diverse practical applications and beneficial value across multiple disciplines. The present study is an effort on application of soft computing techniques such as Artificial Neural Networks, Genetic Algorithm, and Genetic Programming to predict storm surge and inundation characteristics resulting from tropical cyclones. The coast of Odisha adjoining the Bay of Bengal is considered for this case study. Historically, the Odisha coast is known to be highly vulnerable to strike from maximum number of high intense tropical cyclones that form over Bay of Bengal region. Recently in a separate study the authors have developed a comprehensive pre-computed dataset on storm surges and inundation scenarios from historical cyclones that made landfall over coastal Odisha State. Present study is an effort to effectively utilize the pre-computed dataset for real-time operation using soft computing techniques and that performs rapid computation to aid emergency preparedness and planning operations. Study performed several numerical experiments using various soft computing techniques and best possible configuration for real-time operation is developed. An inter-comparison exercise was also carried out to skill assess the performance of various soft computing techniques. The authors believe that this study has immense potential for real-time operations and that can be extended to other coastal regions in India. [ABSTRACT FROM AUTHOR]

Emergency decision making is still an important issue in unconventional emergency management research. Although many studies have been written on this topic, they remain political and qualitative, and it is difficult to make them operational in practice. Therefore, this article proposes a decision-theoretic rough set over two universes as an approach for solving this difficulty. The proposed approach integrates rough set theory over two universes using a Bayesian decision-making technique. In this study, emergency decision making is considered as a multiple-criteria ranking or multiple-criteria selection problem with multi-granularity linguistic assessment information. A Bayesian decision process based on linguistic description with qualitative data over two universes is first presented to construct the decision model and approach, and then the decision-theoretic rough set theory over two universes is taken to induce a set of decision rules that satisfy minimum risk of loss conditions. These rules can easily give the optimal decision results with minimum risk of loss by considering online information, realistic scenarios, and the dynamic characteristic of unconventional emergency events as they develop. Finally, the steps and the basic principle of the proposed method are illustrated by a numerical example with the background of emergency decision making. [ABSTRACT FROM AUTHOR]