Rapid and Accurate Acquisition of Equivalent Economic Scale in Flood Protection Area Based on Multi-source Big Data
2025
ZHENG Yong | XU Zhangfan | ZHOU Xiaoxin | CAI Jihong | JI Hongxiang | QIN Yan
The equivalent economic scale is an important indicator for determining the flood protection standard in urban protection areas. Its accuracy depends on the estimated population and gross domestic product (GDP) of the flood protection area. This study integrates multi-source big data, including building height, urban point-of-interest (POI) data, and population census data, to build random forest algorithm models of population and GDP, forming 100-meter grid data sets of population and GDP for the Greater Bay Area and achieving the rapid acquisition of equivalent economic scale for flood protection areas. The results show that the accuracy of the population prediction model is 82%, and the accuracy of the GDP prediction model is 76%. Compared with the public population and GDP data sets, the 100-meter grid data sets of this study have higher spatial resolution and can better reflect the spatial distribution details of the data. The proposed method provides a data processing example for fine-grained evaluation, which can be extended to fine-grained spatialization of various flood risk indicators, improving the accuracy and reliability of traditional disaster risk assessment methods.
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