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Flood disaster risk assessment based on random forest algorithm

  • Special Issue on Multi-modal Information Learning and Analytics on Big Data
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Abstract

With the frequent occurrence of natural disasters, timely warning of flood disasters has become an issue of concern. This research mainly discusses flood disaster risk assessment based on random forest algorithm. This study uses the special functions of GIS to collect, manage, and analyze data to propose a method of flood disaster risk assessment based on GIS. This method is based on the characteristics of natural disaster-causing factors in the study area, selects an appropriate grid size, and finally realizes the function of visual expression of regional disaster risk. First, use ArcGIS10.1 to analyze and integrate each hazard factor into the flood disaster report index model. Second, the random forest algorithm is used as the weight of each parameter of the flood disaster index model. Finally, use ArcGIS spatial analysis tool map algebra function to model, carry out flood risk assessment in different periods, and use spatial analysis function to extract the median value to point function to extract the flood inundation depth of the study area in a specific scenario. In the experimental part, this research uses layer overlay to determine the number and types of affected areas. Using the natural break point method of ArcGIS 10.1 platform, the study area is divided according to the magnitude of the flood disaster risk value. At the same time, there are a total of 85 samples that have experienced flood disasters, of which only six have been misjudged as no flood disasters. Generally speaking, the model prediction accuracy is high. The research results show that the combination of random forest algorithm and GIS technology is convenient for analyzing the spatial pattern and internal laws of flood risk, and has good applicability.

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Acknowledgements

This work was supported by the Special Projects in Key Areas (New Generation of Information Technology) of Colleges and Universities in Guangdong Province (2020ZDZX3046), the Characteristics innovation project of colleges and universities of Guangdong Province (Natural Science, No. 2019KTSCX235, 2019), and the Higher Education of the Ministry of Education of the People’s Republic of China has the first batch of “industry-academic cooperation, collaborative education” projects in 2019 (No. 201901070016), Characteristic Innovation Projects of Guangdong Province Education Program (2018KTSCX209, 2019GKTSCX092); Science and Technology Program of Guangdong Province (2020B121201013); Science and Technology Special Fund Program of Guangdong Province (2020A0102009); Rural Science and Technology Commissioner Program of Guangdong Province (KTP20200278); Collaborative Innovation Center of Big Data Research and Application, JYU and GMIP (130B0310); Research Achievement Award Cultivation Project, Jiaying University. The Special Projects in Key Areas (New Generation of Information Technology) of Colleges and Universities in Guangdong Province (CN) (Grant No. 2020ZDZX3046).

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Authors and Affiliations

  1. School of Information Science and Technology, South China Business College, Guangdong University of Foreign Studies, Guangzhou, 510545, Guangdong, China

    Zijiang Zhu

  2. Institute of Intelligent Information Processing, South China Business College, Guangdong University of Foreign Studies, Guangzhou, 510545, Guangdong, China

    Zijiang Zhu

  3. School of Geographic Science and Tourism, Jiaying University, Meizhou, 514015, Guangdong, China

    Yu Zhang

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  1. Zijiang Zhu
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  2. Yu Zhang
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Correspondence to Yu Zhang.

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Zhu, Z., Zhang, Y. Flood disaster risk assessment based on random forest algorithm. Neural Comput & Applic 34, 3443–3455 (2022). https://doi.org/10.1007/s00521-021-05757-6

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