GIS-based Risk Zonation of Rainstorm Disaster in Guizhou Province

[Objective] This paper aimed to study the risk zoning of rainstorm in Guizhou based on GIS. [Method] Taking Guizhou as study area, 1 km×1 km grid data as evaluation unit, and by dint of daily precipitation in meteorological station in Guizhou from 1961 to 2008, the rainstorm risk zoning system was constructed from the aspects of disaster-stricken dangers, suffering flexibility, disaster environment sensitivity and disaster prevention or mitigation; based on the level analysis method to determine factor weight, the risk assessment model based on GIS was set up to evaluate the four sub-indicators and risks and to get the rainstorm disaster in Guizhou in the end. [Result] The risk assessment and zonation results showed a general trend that the risk level decreased from the central to all around. The low risk area distributed in the northwest of Guizhou province because of less heavy rains and high capacity of rainstorm disaster resistant, while high risk area mainly distributed in the west-central of Guizhou due to concentration rainstorms, large terrain undulation and low coverage rate of forest. Especially, according to Anshun, the high risk area took up 98.02% of the city, and the Gangwu County, where a super-large geological disaster concurred in 2010 is located at the high risk area, which showed that the risk assessment coincided with the actual situation. [Conclusion] The study provided theoretical basis for the macro disaster prevention and disaster mitigation plan.

Study on Evaluation of the Rainstorm Disaster in Fujian Province Based on Spectral Clustering Model with Grey Correlation Analysis

[ Objective] The research aimed to study assessment index system of the rainstorm disaster in Fujian Province based on spectral cluste- ring model with grey correlation analysis. [Method] According to meteorological disaster yearbook in Fujian Province, by comprehensively consider- ing disaster-inducing factor, disaster-inducing environment, disaster-sustaining body and regional disaster-prevention level, evaluation index system of the regional rainstorm disaster in Fujian was established. By spectral clustering model based on grey correlation analysis, dsk zoning of the rain- storm disaster was conducted in each area of Fujian. Finally, effect and application of the clustering model were analyzed by case research. [ Re- sult] In order to dig immanent connection among regional characteristics and improve disaster-preventing linkage performance of the evaluation unit, a spectral clustering model based on grey correlation analysis was used to conduct risk zoning of the rainstorm disaster in Fujian Province. Moreo- ver, combined weight was introduced to judge each evaluation index, so as to adjust clustering model. By case study, rainstorm disaster levels in 67 counties were obtained. Internal characteristics of each type were analyzed, and main correlation factors of each type were extracted. It was compared with statistical result of the rainstorm disaster, verifying validity and feasibility of the model. [ Conclusion] The method was feasible, and its evaluated result had better differentiation and decision accuracv.

Research and Application of Rainstorm Disaster Risk Assessment along the Middle and Lower Reaches of Yangtze River

We explored and studied rainstorm disaster impact grade. Firstly,we selected average precipitation,precipitation intensity,coverage and duration during rainstorm process,and economic losses,the number of deaths and total casualties in rainstorm disaster situation as the pre-assessment indexes of rainstorm process impact grade along the middle and lower reaches of Yangtze River. Then,normalized and dimensionless processing of each index was conducted. By using gray correlation method,we established rainstorm process grade and rainstorm disaster impact grade. At last,we conducted regression analysis of relevancy degree between rainstorm process grade and rainstorm disaster situation grade,and established a linear relationship between the two,thereby getting a rainstorm disaster pre-assessment method. On this basis,using rainstorm hazard factors in independent sample,we carried out pre-assessment test of disaster impact grade. The results show that this pre-assessment method is quick and easy,and the effect is better.

Study on Risk Assessment of Summer Rainstorm Disaster and Countermeasures for Disaster Prevention and Mitigation in Shaoyang City

Using summer(June-August)precipitation observation data in 10 representative stations of Shaoyang City during 1971-2021 and disaster data caused by summer rainstorm in nine counties(cities)and four districts of Shaoyang during 1981-2021,statistical analysis on summer rainstorm and its caused disaster in Shaoyang was conducted,and spatial and temporal characteristics of summer rainstorm and spatial distribution rule of disaster were found out.The results showed that(1)the rainstorm disaster in Shaoyang City occurs almost every year and is highly seasonal.(2)Rainstorm disaster loss is the first of other meteorological disasters.(3)The summer rainstorm disaster has the characteristics of sudden and destructive.On this basis,the relative grades of rainstorm disaster risk degree and disaster loss degree were divided,and the risk assessment of rainstorm and flood disaster in Shaoyang City was made,and the disaster prevention and mitigation measures and countermeasures were put forward.The research could provide scientific decision basis for party and government departments guiding flood fighting and disaster relief.

Global Rainstorm Disaster Risk Monitoring Based on Satellite Remote Sensing

Methods of rainstorm disaster risk monitoring(RDRM)based on retrieved satellite rainfall data are studied.Due to significant regional differences,the global rainstorm disasters are not only affected by geography(such as topography and surface properties),but also by climate events.It is necessary to study rainstorm disaster-causing factors,hazard-formative environments,and hazard-affected incidents based on the climate distribution of precipitation and rainstorms worldwide.According to a global flood disaster dataset for the last 20 years,the top four flood disaster causes(accounting for 96.8%in total)related to rainstorms,from most to least influential,are heavy rain(accounting for 61.6%),brief torrential rain(16.7%),monsoonal rain(9.4%),and tropical cyclone/storm rain(9.1%).A dynamic global rainstorm disaster threshold is identified by using global climate data based on 3319 rainstorm-induced floods and rainfall data retrieved by satellites in the last 20 years.Taking the 7-day accumulated rainfall,3-and 12-h maximum rainfall,24-h rainfall,rainstorm threshold,and others as the main parameters,a rainstorm intensity index is constructed.Calculation and global mapping of hazard-formative environmental factor and hazard-affected body factor of rainstorm disasters are performed based on terrain and river data,population data,and economic data.Finally,a satellite remote sensing RDRM model is developed,incorporating the above three factors(rainstorm intensity index,hazard-formative environment factor,and hazard-affected body factor).The results show that the model can well capture the rainstorm disasters that happened in the middle and lower reaches of the Yangtze River in China and in South Asia in 2020.

Focused crawling strategies based on ontologies and simulated annealing methods for rainstorm disaster domain knowledge

At present,focused crawler is a crucial method for obtaining effective domain knowledge from massive heterogeneous networks.For most current focused crawling technologies,there are some difficulties in obtaining high-quality crawling results.The main difficulties are the establishment of topic benchmark models,the assessment of topic relevance of hyperlinks,and the design of crawling strategies.In this paper,we use domain ontology to build a topic benchmark model for a specific topic,and propose a novel multiple-filtering strategy based on local ontology and global ontology(MFSLG).A comprehensive priority evaluation method(CPEM)based on the web text and link structure is introduced to improve the computation precision of topic relevance for unvisited hyperlinks,and a simulated annealing(SA)method is used to avoid the focused crawler falling into local optima of the search.By incorporating SA into the focused crawler with MFSLG and CPEM for the first time,two novel focused crawler strategies based on ontology and SA(FCOSA),including FCOSA with only global ontology(FCOSA_G)and FCOSA with both local ontology and global ontology(FCOSA_LG),are proposed to obtain topic-relevant webpages about rainstorm disasters from the network.Experimental results show that the proposed crawlers outperform the other focused crawling strategies on different performance metric indices.

Analysis and Zoning of Rainstorm Flood Disaster Risk in Huaihe River Basin

Taking the rainstorm flood disaster of Huaihe River basin as the research object,according to the principles of risk assessment for natural disasters,starting from the fatalness of inducing factors and the vulnerability of hazard bearing body,the weight of each impact factor was calculated by using AHP. By using spatial analysis and statistical function of GIS,the integrated risk chart of rainstorm flood disaster in Huaihe River basin was obtained. The results showed that the high risk areas of rainstorm flood disaster in Huaihe River basin mainly distributed in the southern part of Henan,the central northern part of Anhui and eastern part of Jiangsu Province. Due to higher fatalness of inducing factors in southern Henan,there was high risk in the region. Central Anhui and east Jiangsu were not only high-fatalness zones but also high vulnerability zones of population and economy.

Reasoning Disaster Chains with Bayesian Network Estimated Under Expert Prior Knowledge

With the acceleration of global climate change and urbanization,disaster chains are always connected to artificial systems like critical infrastructure.The complexity and uncertainty of the disaster chain development process and the severity of the consequences have brought great challenges to emergency decision makers.The Bayesian network(BN)was applied in this study to reason about disaster chain scenarios to support the choice of appropriate response strategies.To capture the interacting relationships among different factors,a scenario representation model of disaster chains was developed,followed by the determination of the BN structure.In deriving the conditional probability tables of the BN model,we found that,due to the lack of data and the significant uncertainty of disaster chains,parameter learning methodologies based on data or expert knowledge alone are insufficient.By integrating both sample data and expert knowledge with the maximum entropy principle,we proposed a parameter estimation algorithm under expert prior knowledge(PEUK).Taking the rainstorm disaster chain as an example,we demonstrated the superiority of the PEUK-built BN model over the traditional maximum a posterior(MAP)algorithm and the direct expert opinion elicitation method.The results also demonstrate the potential of our BN scenario reasoning paradigm to assist real-world disaster decisions.