Abstract
To reasonably evaluate and predict the loss of rainstorm and flood disaster, this study is based on the rainfall data and rainstorm and flood disaster data of 18 cities in Henan Province from 2010 to 2020, using GIS technology and weighted comprehensive evaluation method to analyze the risk of rainstorm and flood disaster factors in various regions. The four risk factors of hazard risk, hazard-pregnant environment sensitivity, hazard-bearing body vulnerability, and disaster resilience were analyzed in compartment analysis. At the same time, a new rainstorm and flood disaster prediction model was constructed in combination with the hybrid PSO-SVR algorithm. The research results show that there are many rivers in Henan Province, the terrain tends to be higher in the west and lower in the east, and most areas are low plains, making most cities in Henan Province at a moderate risk level. For the more developed cities such as Zhengzhou, Luoyang, and Nanyang, the hazard risk, sensitivity, vulnerability, and disaster resistance are high, and they are prone to heavy rains and floods. For the economically underdeveloped, the terrain is high or hills, such as Sanmenxia City; Xinyang City and other places have low hazard risk and are not prone to rainstorms and floods. By constructing a hybrid PSO-SVR model, selecting two representative cities of Zhengzhou and Luoyang, and predicting the daily rainfall, the number of disasters, and the direct economic loss, the calculated RMSE and MAPE values are both less than GA-SVR, the traditional SVR, and BPNN models, which have verified the superiority of the model proposed in this study and the practical value it brings. To further verify the prediction accuracy of the hybrid model, the average value of RMSE and MAPE of other 16 cities are calculated, and the result is still smaller than other three models, and the study can provide some decision-making references for the urban rainstorm and flood management.
Highlights
To reasonably evaluate and predict the loss of rainstorm and flood disaster, this study is based on the rainfall data and rainstorm and flood disaster data of 18 cities in Henan Province from 2010 to 2020, using geographic information system (GIS) technology and weighted comprehensive evaluation method to analyze the risk of rainstorm and flood disaster factors in various regions. e four risk factors of hazard risk, hazard-pregnant environment sensitivity, hazard-bearing body vulnerability, and disaster resilience were analyzed in compartment analysis
E support vector regression (SVR) model without parameter optimization algorithm is selected for comparison, which is mainly used to highlight the impact of parameter optimization on the prediction results. e GA-SVR model is chosen to compare and highlight that particle swarm optimization (PSO) is more applicable to this model than
With the rapid development of global climate change and urbanization, more and more cities are suffering from extreme rainstorm and flood disasters, which has caused huge losses to people’s lives and social and economic construction. erefore, it is very important to carry out risk assessment and prediction of rainstorm and flood disaster, which will help to improve the ability of regional emergency prediction, reduce losses caused by rainstorm and flood disasters. en, to expand the applicable scope of the model, the experiments of the hybrid PSO-SVR model will be tested in more cities, and better improvements will be made in the continuous experimental process, to strive to provide more accurate analysis and assessment and disaster loss prediction
Summary
E evaluation of rainstorm and flood disaster is a comprehensive evaluation process based on the selection of reasonable indicators. In this research, based on the theory of disaster risk system, referring to existing research results and considering the availability of data for rainstorms and floods, index system of rainstorm and flood disaster loss prediction is constructed from the aspects of hazard factors, hazard-pregnant environment, hazard-bearing body, disaster resilience, and disaster loss (see Table 2). E sensitivity analysis of hazard-pregnant environment reflects the impact of natural geographical environment on rainstorm and flood disaster. E vulnerability analysis of hazardbearing body mainly analyzes the influence of different rainstorm and flood intensity disasters for the distribution of population and the condition of regional economic and infrastructure. According to the significance at the 95% level, the significance value greater than 0.05 indicates that the correlation of variables is low. erefore, the impact of Z1, S2, and K2 will not be considered in this paper
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