Storm Disaster Research Articles

Sudden weather disasters as triggers for ethnic protest in autocracies?

Under what conditions do disastrous storms and floods provoke anti-government protests in autocracies? While grievances, as they often occur after disastrous weather events, are necessary preconditions for protests, they are by no means sufficient. This is particularly true in undemocratic states, where protesters typically fear repressive sanctions. Therefore, this essay hypothesizes that disastrous storms and floods primarily result in anti-government mobilization in autocracies when they occur in the homelands of marginalized ethnic groups. In such scenarios, the immediate hardships caused by the disaster come on top of underlying more structural grievances. Moreover, groups with shared ethnic identities are more likely to mobilize for collective action. This argument is tested using quantitative cross-national protest-event data from the Mass Mobilization in Autocracies Database (MMAD). The results of numerous regression analyses on a sample pre-processed with Coarsened Exact Matching to address potential concerns of endogeneity yield robust support for the outlined hypothesis.

Influence of Geomagnetic Storms on Power System Static Voltage Security

The adverse effects of geomagnetic storms on power systems have been confirmed by numerous examples and observations. However, there remains a dearth of research on the quantitative assessment of the safety risks of a system under extreme geomagnetic storm conditions. Accordingly, in this study, we developed a risk assessment model for magnetic storm disasters based on the static voltage security region theory from the global perspective of power grid security. First, we determined the boundary shape of the static security region of a system under magnetic storm conditions and analyzed its influence on the voltage stability of the system from a regional perspective. Subsequently, a static safe-distance index was proposed to characterize the influence of a magnetic storm disaster on the system voltage. Considering the uncertainty in the injected power during a magnetic storm, we calculated the static voltage instability probability of the system. Through probability index analysis, a probabilistic safety assessment of the system under magnetic storm conditions was performed, and its overall static voltage safety level was measured. Finally, a simulation analysis based on the UIUC 150 node showed the proposed method to be reasonable and effective.

Assessment of the extreme sand/dust storm disaster in North China on March 26th, 1862

Case studies of extreme sand/dust storm disasters in history are crucial for better understanding of sand/dust storm, whereas the bottleneck is lack of data. This paper attempts to reconstruct and assess the spatial and temporal characteristics of an extreme sand/dust storm disaster in North China on March 26th, 1862. Through extensive examination of historical documents, abundant and detailed materials were found in the Diaries of Xianqiao Shanfang, The Diary of Weng Tonghe, Diary in Jinmen (Jinmen Wen Jian Lu), Qing History Manuscripts and 13 historical local chronicles. A large-scale sand/dust storm occurred in North China on March 26th, 1862, which lasted about three days, and its spatial range covered 34∼40°N, 116∼120°E. The storm was very strong, and the horizontal visibility reduced to <50 m in some areas, therefore can be classified as an ‘extra-strong sand/dust storm’. This disaster caused a large number of casualties, therefore can be classified as an “extremely severe sand/dust storm disaster”. Tianjin was particularly seriously hit, dozens of people died of it. At least for Tianjin, this could be an unprecedented sand/dust storm disaster in history.

Resilient planning for high‐renewable‐integrated transmission systems under the impacts of ice storm disasters

AbstractIce storm disasters pose great challenges to the safe and stable operation of transmission systems, especially in high‐renewable‐integration scenarios. However, as one of the major types of natural disasters that can have severe consequences, ice storm disasters are much less studied than hurricanes and earthquakes. Ice storm disasters are characterized by a long duration and wide scope, which affect the source, network, and load side of the power grid. It is difficult to formulate a quantitative evaluation of resilience and establish effective planning strategies. Here, a resilient planning method considering the interactive effects of ice disasters on sources, networks, and loads is therefore proposed. First, the two‐stage Monte Carlo method is used to generate multiple grid fault scenarios for different levels of ice storm disasters. Then, an index is proposed to quantify the grid resilience as the total amount of system power loss caused by ice storm disasters. Based on this approach, a two‐layer mathematical model is established with grid resilience and total investment cost objectives in each fault scenario. Hierarchical reinforcement and the protection of generating units and transmission lines are selected as investment strategies to mitigate the impact of ice storm disasters. Finally, the IEEE‐24 node transmission system is used to verify the feasibility and effectiveness of the proposed method. Optimizing the planning strategy can significantly improve system resilience with minimal investment cost.

The Transport Path and Vertical Structure of Dust Storms in East Asia and the Impacts on Cities in Northern China

Dust storm disasters have emerged as a significant environmental challenge in East Asia. However, relying on a single monitoring method to track dust storms presents limitations and can be variable. Therefore, it is necessary to use a combination of ground and remote sensing monitoring methods to explore the source and impact range of dust storms in order to fully characterize them. To achieve this, we examined the sources and impact ranges of dust storms in East Asia from 1980 to 2020 using both ground station data and remote sensing data. In addition, we focused on three specific dust storm events in the region. Our results indicate that the central source areas of dust storms are located in southern Mongolia and the Taklamakan Desert in China. Dust storms are mainly transported and spread in the northwestern region, while they are relatively rare in the southeastern region. The HYSPLIT model simulations reveal that the primary source directions of dust storms in East Asia are northwest, west, and north, the region involved includes Kazakhstan, southern Mongolia, and the Taklimakan Desert in China. The vertical structure of the dust storm layer depends on the source of the dust storm and the intensity of the dust storm event. Dust grain stratification probably occurs due to differences in dust storm sources, grain size, and regularity. These findings demonstrate that a combination of ground-based and remote sensing monitoring methods is an effective approach to fully characterize dust storms and can provide more comprehensive information for dust storm studies.

Trs-net tropical revolving storm disasters analysis and classification based on multispectral images using 2-d deep convolutional neural network

Trs-net tropical revolving storm disasters analysis and classification based on multispectral images using 2-d deep convolutional neural network

Animal Mockery in Southeast Asian and African Origin Mythology

Found in many parts of insular Southeast Asia and the adjacent mainland, a widespread complex of taboos concerns actions that are believed to result in a disastrous storm and, sometimes, petrification of offenders. The most typical offenses involve somehow mocking non-human animals, or in a few cases plants or trees. One aim of the paper is to isolate, as a distinct component of this complex, a series of myths that describe how acts of animal mockery resulted in the formation of lakes or other permanent features of local landscapes. There follows a detailed discussion of resemblances between these origin stories and several African origin of death myths that feature people mocking non-humans by giving them human funerals. The variety of societies that express disapproval of animal mockery then facilitates assessment of recent ontological pluralist theory, according to which cultures differ radically in how they conceive of human-animal relations.

Research on and application of the operational storm surge ensemble forecast model in the Bay of Bengal

The coastal region of the Bay of Bengal (BoB) is one of the most vulnerable areas to storm surges caused by tropical cyclones (TCs). Historical data indicate that the BoB experiences two seasons in the annual cycle of TC formation, namely, the pre-monsoon season (April to May) and the post-monsoon season (October–December). The frequency of post-monsoon cyclones is significantly higher than that of pre-monsoon cyclones. Very severe cyclonic storms and stronger-intensity storms can cause disastrous storm surges. Based on the accuracy of current forecasts of the path, intensity and radius of maximum wind of TCs in the BoB, a total of 153 ensemble members are automatically generated according to the subjective deterministic forecast results of the TCs, basically covering all possible TCs. A graphics processing unit (GPU)-based parallel storm surge forecast model is established and validated for the coastal areas of the BoB. The speedup ratio of the model can exceed 300, which can meet the computational time requirements of large-scale ensemble numerical forecasts. For the 2020 Amphan cyclone, the costliest cyclone ever recorded in the North Indian Ocean (NIO), the numerical ensemble forecast model can provide rapid and accurate storm surge ensemble and numerical probability forecast products for the BoB. The probability of coastal flooding risk can be evaluated for the coastal areas of the BoB, enabling reasonable and valuable recommendations to be made to evacuate people and protect property near dangerous areas.

FINER-SCALE MONITORING AND ANALYSIS OF 2016–2021 DUST STORMS DISASTERS IN CHINA BASED ON HIMAWARI-8 SATELLITE IMAGERIES

Abstract. Today, remote sensing is one of the important techniques for natural disasters monitoring. Using the Himawari-8 new generation of geostationary meteorological satellite image data, we identified and monitored 2016–2021 dust storm disasters in China at the finer scale, and compared the object-oriented classification, supervised and unsupervised classification methods. Taking the dust storm on May 5, 2016 as example, the average accuracy and kappa coefficient of the supervised classification were 0.78 and 0.68, while the object-oriented classification were 0.81, 0.73. We used the object-oriented classification method to classify the 2016-2021 dust storm incidents across China. In 2021, the largest area affected by sandstorms was about 68.33km2, the smallest area was about 2.08km2, and the average area was about 22.25km2. During 2016–2021, the most severe and wide-spreading sandstorm occurred on March 14–18 2021. The dust storm first appeared in northwestern Mongolia on March 14, and then spread southeast to China. The dust area covered Beijing on the 15th, and then spread from eastern China to the western regions of North China and Northeast China, including most of the Hexi Corridor. On the 16th and 17th, the influence of the dust storms narrowed to central and western Inner Mongolia, northern Gansu and southern Mongolia. The dust storm gradually disappeared on the 18th.

The Annual Global Incidence Rate of Extreme Weather Event Disasters Appears Positively Correlated with World GDP, 1961-2020.

This study compared the per capita annual global incidence rate of disasters caused by natural hazards with the annual world real gross domestic product, GDP (per global capita), as reported during 1961 through 2020. Sixty (60) values for the world real GDP per global capita (in constant 2015 $USD) were compared to corresponding annual values for global incidence rates for five natural disaster subgroups and then for a total of twelve individual disaster types that comprise the subgroups; each expressed as an annual global incidence rate (in terms of annual incidence per 100,000 persons). Calculations of multiple linear regression, ANOVA, and Pearson's correlation coefficient were performed for comparing population-adjusted values for GDP to corresponding values. Four out of five hydrological and meteorological disasters were found to have a positive correlation with GDP. Results of the analysis revealed a relatively high degree of correlation between world GDP and the annual incidence of flood and storm disasters (P = 6.21 × 10-10 and P = 4.23 × 10-4, respectively). The annual incidence of heat waves and cold weather disasters also appeared to correlate with GDP (P = .002 and P = .019, respectively). In comparison, wet landslides indicated no such correlation (P = .862). No significant associations were found among the seven other individual biological, climatological, and geophysical disasters and GDP. The global incidence of four extreme weather (hydrometeorological) disasters appear to be positively associated with world real GDP during 1961-2020. These findings contradict previous postulates that the risk of disaster incidence is inversely associated with the capacity of the population.

Modeling the Evolution of Major Storm-Disaster-Induced Accidents in the Offshore Oil and Gas Industry.

Storm disasters are the most common cause of accidents in offshore oil and gas industries. To prevent accidents resulting from storms, it is vital to analyze accident propagation and to learn about accident mechanism from previous accidents. In this paper, a novel risk analysis framework is proposed for systematically identifying and analyzing the evolution of accident causes. First, accident causal factors are identified and coded based on grounded theory (GT). Then, decision making trial and evaluation laboratory (DEMATEL) is integrated with interpretative structural modeling (ISM) to establish accident evolution hierarchy. Finally, complex networks (CN) are developed to analyze the evolution process of accidents. Compared to reported works, the contribution is threefold: (1) the demand for expert knowledge and personnel subjective influence are reduced through the data induction of accident cases; (2) the method of establishing influence matrix and interaction matrix is improved according to the accident frequency analysis; (3) a hybrid algorithm that can calculate multiple shortest paths of accident evolution under the same node pair is proposed. This method provides a new idea for step-by-step assessment of the accident evolution process, which weakens the subjectivity of traditional methods and achieves quantitative assessment of the importance of accident evolution nodes. The proposed method is demonstrated and validated by a case study of major offshore oil and gas industry accidents caused by storm disasters. Results show that there are five key nodes and five critical paths in the process of accident evolution. Through targeted prevention and control of these nodes and paths, the average shortest path length of the accident evolution network is increased by 35.19%, and the maximum global efficiency decreases by 20.12%. This indicates that the proposed method has broad applicability and can effectively reduce operational risk, so that it can guide actual offshore oil and gas operations during storm disasters.

Dust storm detection of a convolutional neural network and a physical algorithm based on FY-4A satellite data

Quantitative identification of a dust storm weather is key to forecasting and early warning of dust storm disasters. However, traditional visibility ground-based measurements cannot be extended to regional observations. Remote sensing of dust storms is associated with large uncertainties in dust thresholds. For accurate quantification of the dust storm region, this study proposes a dust storm mask algorithm to identify dust storm in the Tarim Basin. The dust storm mask includes two algorithms to identify the dust storm outbreak and the spatial extent by using the Advanced Geostationary Radiation Imager (AGRI) on board the FY-4A satellite. A deep learning convolutional neural network (CNN) is employed for the dust storm mask, and the AGRI bands 1–3, 5–6, and 11–13 are used as model parameters. A physical algorithm (PA) is adopted to construct a dust storm mask using three physical dust indices: the Normalized Difference Dust Index (NDDI2.25μm-0.47μm/2.25μm+0.47μm), the Dust Ratio Index (DRI7.10μm/3.75μm), and the Brightness Temperature Difference (BTD3.75μm-13.50μm). The results show that the CNN algorithm has a higher classification accuracy on dust storm detection compared to the PA. This advantage suggests that the CNN can effectively monitor large-scale dust storms. The dust storm identification results were compared and analyzed with the AGRI true color, Aerosol Optical Depth products, and Ultra Violet Aerosol Index products.

Hurricanes: A Disastrous Storm or Famous Louisiana Cocktail? A Headache Either Way

Features| April 2022 Hurricanes: A Disastrous Storm or Famous Louisiana Cocktail? A Headache Either Way Kyle P. Nester, MD, FASA; Kyle P. Nester, MD, FASA Search for other works by this author on: This Site PubMed Google Scholar Katherine S. Cox, MD, FASA Katherine S. Cox, MD, FASA Search for other works by this author on: This Site PubMed Google Scholar ASA Monitor April 2022, Vol. 86, 29. https://doi.org/10.1097/01.ASM.0000826984.50290.2f Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share MailTo Twitter LinkedIn Cite Icon Cite Get Permissions Search Site Citation Kyle P. Nester, Katherine S. Cox; Hurricanes: A Disastrous Storm or Famous Louisiana Cocktail? A Headache Either Way. ASA Monitor 2022; 86:29 doi: https://doi.org/10.1097/01.ASM.0000826984.50290.2f Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAll PublicationsASA Monitor Search Advanced Search Topics: headache, hurricanes, louisiana Living in Louisiana is great: the food, the people, the vibrant culture. But there are six months out of the year that give many of us pause. Hurricane season can lead to unpredictable events that present unique challenges for physicians. Thankfully, anesthesiologists are notorious planners. In times of impending disaster, hospitals must have emergency operation plans and activate an incident command center. Planning is essential, but each storm brings unique challenges. It was a rough year for all in 2020; no one will argue with that statement. For southwest Louisiana, and in particular the city of Lake Charles, Mother Nature brought “a little something extra,” or “lagniappe” (LAN-yap), as we say in Louisiana. However, the lagniappe was neither little nor welcomed. Two major storms, Hurricane Laura on August 27, 2020, and Hurricane Delta on October 9, 2020, delivered a devastating blow to the area. Category 4 Hurricane Laura became the... You do not currently have access to this content.

Forecasting soil erosion and sediment yields during flash floods: The disastrous case of Mandra, Greece, 2017

AbstractFlash floods, among the most destructive natural hazards, are commonly studied as to their catastrophic power in terms of fatalities, infrastructure damages and economic losses. A devastating aftermath of flash floods, which has not received much‐deserved attention in the literature, is the sizeable and permanent soil loss due to soil erosion and sediment yields. This study aims at forecasting soil erosion and sediment yields due to the disastrous storm event that occurred in Mandra town (western Attica, Greece) on 15 November 2017. Gridded hydrometeorological forecasts were conducted at 5‐min and 1‐h time steps by means of the chemical hydrological atmospheric ocean wave system (CHAOS). The forecasts of soil erosion and sediment yields were achieved by a high‐resolution geographic information system (GIS) application of the modified universal soil loss equation (MUSLE) on the basis of the forecasted surface runoff hydrographs. The entire event lasted from the afternoon of 14 November until noon of 15 November, but the bulk of the disastrous storm occurred in the morning of 15 November 2017, flooding two torrent basins. As a result of the extreme flash flood, 2195 tons and 1435 tons of sediment were forecasted to be detached from the body of the soil's surface and transported to the stream networks of the Soures and Agia Aikaterini Torrent basins which cross Mandra. Soil erosion maps were constructed for every hour and the spatial and temporal evolution of soil erosion was shown throughout the event. This study provides concrete insights on the erosion‐prone areas of the study basins, which can inform actions against erosion.

Identification of Critical Nodes and Accident Chains of Power Systems under the Effect of Geomagnetic Storms

Geomagnetically induced currents (GICs) generated by geomagnetic disturbances (GMD) during strong magnetic storms may trigger chain failures in power systems. In this paper, a critical node identification method based on the improved LeaderRank algorithm considering the effects of geomagnetic storms is proposed. A chain fault identification process based on the fragility theory of complex systems is proposed to determine the initial fault line by critical nodes and simulate the fault development process to obtain the accident chain set. The effectiveness of the method is verified by using the IEEE RTS79 node system as an example. The research results show that the method can reflect the critical nodes and accident chain set of power systems under different geomagnetic storm effects, and the research results can provide a reference for geomagnetic storm disaster quantification, assessment, and prevention.

Fractional Linear Regression Equation in Agricultural Disaster Assessment Model Based on Geographic Information System Analysis Technology

Abstract This article combines geographic information system (GIS) technology and database technology to analyse agricultural, natural disasters. The article uses a fractional linear regression equation to define the comprehensive intensity grading standard of the disaster-causing factors of torrential rain. At the same time, we use GIS to superimpose the agricultural vulnerability index into the storm disaster risk zoning to obtain the degree of agricultural impact under different levels of risk. At the end of the thesis, the model is applied to actual case analysis to verify the effectiveness of the algorithm model.

Assessment of Extreme Storm Surges over the Changjiang River Estuary from a Wave-Current Coupled Model

Disastrous storm surges and waves caused by typhoons are major marine dynamic disasters affecting the east China coast and the Changjiang River Estuary, especially when they occur coincidentally. In this study, a high-resolution wave–current coupled model consisting of ADCIRC (Advanced Circulation) and SWAN (Simulating Waves Nearshore) was established and validated. The model shows reasonable skills in reproducing the surge levels and waves. The storm surges and associated waves are then simulated for 98 typhoons affecting the Changjiang River Estuary over the past 32 years (1987–2018). Two different wind fields, the ERA reanalysis and the ERA-based synthetic wind with a theoretical typhoon model, were adopted to discern the potential uncertainties associated with winds. Model results forced by the ERA reanalysis show comparative skills with the synthetic winds, but differences may be relatively large in specific stations. The extreme surge levels with a 50-year return period are then presented based on the coupled model results and the Gumbel distribution model. Higher risk is presented in Hangzhou Bay and the nearshore region along the coast of Zhejiang. Comparative runs with and without wave effects were conducted to discern the impact of waves on the extreme surge levels. The wave setup contributes to 2–12.5% of the 50-year extreme surge level. Furthermore, the joint exceedance probabilities of high surge levels and high wave height were evaluated with the Gumbel–logistic statistic model. Given the same joint return period, the nearshore region along the coast of Zhejiang is more vulnerable with high surges and large waves than the Changjiang River Estuary with large waves and moderate surges.

Evaluating recovery strategies for the disruptions in liner shipping networks: a resilience approach

PurposeSince the start of the current century, the world at large has experienced uncertainties as a result of climate change, terrorism threats and increasing economic upheaval. These uncertainties create non-classical risks for global seaborne container trade and liner shipping networks (LSNs). The purpose of this paper is to establish a novel risk-based resilience framework to measure the effectiveness of different recovery strategies for the disruptions in LSNs in a quantitative manner.Design/methodology/approachBased on a resilience loss triangle model, an indicator of resilience–cost ratio is designed to measure the performance of LSNs during recovery. Four recovery strategies are proposed to test the rationality and feasibility of the developed indicator in aiding decision-making of LSNs from a resilience perspective.FindingsThe analysis results reveal that the superiorities of different recovery strategies vary depending on both the structures of LSNs and the specific requirements during recovery. Moreover, optimizing the sequence of ports being recovered will improve the overall recovery efficiency of the investigated LSN.Research limitations/implicationsAs an exploratory research trying to enrich the risk-based resilience evaluation of LSNs from a complex network perspective, only two attributes (e.g. port scare and economy) are considered at the current stage when estimating the time needed to fully recover the whole LSN. In future research, more attributes from the industry may be identified and incorporated into the proposed model to further extend its ability and application scopes.Practical implicationsThe findings will help to improve managerial understandings of recovery strategies to build more resilient LSNs. The proposed model has the capability to be tailored to tackle different types of risks in addition to the storm disaster condition.Originality/valueThe risk-based resilience framework and the resilience–cost ratio indicator are newly developed in this research. They can consider LSNs' structural resilience and the total costs that a recovery strategy needs to restore the whole system simultaneously.

Responding to a simulated disaster in the virtual or live classroom: Is there a difference in BSN student learning?

AimThis study aim was to investigate if prelicensure baccalaureate nursing students gained more knowledge from a live or virtual disaster simulation. The study goal was to inform the use of e-learning or traditional textbooks in undergraduate nursing population health courses. BackgroundWeather-related disasters have increased in frequency and severity in the past ten years, with 2020 being the most active storm season ever seen (National Oceanographic and Atmospheric Administration, 2021.) Even with advances in early warning systems and mitigation efforts, educating student nurses in disaster response remains a priority. Due to the impact of Covid-19 quarantine policies, many in-person student learning labs and clinical experiences were cancelled. However, virtual simulation offers an alternative to developing nursing student skills and clinical reasoning ability (Aebersold, 2018; Fogg et al., 2020). DesignA randomized quasi-experimental, repeated measures 2 × 2 crossover design (Kim, 2018) was applied, which allowed students to participate in both the live and virtual simulations. MethodsAnalysis was conducted using paired samples t-test to evaluate knowledge gains. To measure students’ self-assessment of knowledge, Unver et al. (2018) 12-item survey was administered. To explore students’ own perceptions about the disaster simulations, semi-structured interview questions were offered through private Wiki postings. The responses were analyzed using Saldaña’s in vivo coding (2015) and thematic analysis. ResultsStudents retained more empirical knowledge following the virtual assignment as compared to the disaster simulation, except in two items addressing triage. Neither age, years of education, or GPA impacted test results. However, students’ own assessment of learning did not differ between live and virtual simulations. In all but three items, students perceived a significant increase (p < .05) in their learning following the simulation, regardless whether it was live or virtual.In narrative responses, students overwhelmingly cited the benefit of an in-person simulation. However, they did not believe that they were prepared adequately for the live simulation. They also expressed that they would be more prepared if the simulation was repeated. Students expressed discomfort, even distress, regarding not being able to care adequately for everyone, even though it was a simulation (See Table 5). This highlighted that live simulations can affect students emotionally, and follow-up debriefing is essential to help in both acknowledging and processing student feelings. ConclusionThese findings, which support the use of virtual disaster training in nursing education, are especially important in the light of Covid-19 and increasing threat of storm disasters.

A Novel Fast Decision-making Method Based on Integrated Intelligent Technologies for Wind Disaster Management of Energy Manufacture

Energy manufacture is very important to all of industries. Typhoons hit the power grid in China's southeast coastal areas frequently for the past few years, seriously affecting the industries’ operation. Therefore, making-decision of wind damage management for nation's electricity grid in real time is an urgent subject to be studied. The traditional decision making method is easy to be implemented, but is not proper for dealing with nonlinear problems in complex systems. The purpose of this article is to design a fast decision making framework for accomplishing fast decision making by making combination Case-Based Reasoning (CBR) with Rule-Based Reasoning (RBR), Genetic Algorithm (GA), which is called fast decision making method based on integrated intelligent technologies (FDMMBIIT). Compared with traditional methods, FDMMBIIT completes case adaptation with BPNN after extending case base. To make the decision-making more accurate, this article updated the multi-object genetic algorithm (MOGA) with adaptive genetic operators and a selection method by using the fitness function. Likewise, BPNN is improved with adaptive simulated annealing algorithm (ASAA), which is named as BPNNASAA. More important, this paper expands the frame theory by integrating it to the D/S evidence theory, exploring a novel solution to representing cases with incomplete information. The case of Guangdong demonstrates FDMMBIIT achieves better decision-making performance for storm disaster emergency management.