A Study on Identification of Urban Waterlogging Risk Factors Based on Satellite Image Semantic Segmentation and XGBoost

Jinping Tong, Fei Gao, Hui Liu (), Jing Huang, Gaofeng Liu, Hanyue Zhang and Qiong Duan
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Jinping Tong: Business School, Changzhou University, Changzhou 213100, China
Fei Gao: Business School, Changzhou University, Changzhou 213100, China
Hui Liu: Business School, Changzhou University, Changzhou 213100, China
Jing Huang: Management Science Institute, Hohai University, Nanjing 210098, China
Gaofeng Liu: Business School, Hohai University, Nanjing 210098, China
Hanyue Zhang: Business School, Changzhou University, Changzhou 213100, China
Qiong Duan: Information Technology Center, Luoyang Institute of Science and Technology, Luoyang 471023, China

Sustainability, 2023, vol. 15, issue 8, 1-15

Abstract: As global warming exacerbates and urbanization accelerates, extreme climatic events occur frequently. Urban waterlogging is seriously spreading in China, resulting in a high level of vulnerability in urban societies and economies. It has been urgent for regional sustainable development to effectively identify and analyze the risk factors behind urban waterlogging. A novel model incorporating satellite image semantic segmentation into extreme gradient boosting (XGBoost) is employed for identifying and forecasting the urban waterlogging risk factors. Ground object features of waterlogging points are extracted by the satellite image semantic segmentation, and XGBoost is employed to predict waterlogging points and identify the primary factors affecting urban waterlogging. This paper selects the coastal cities of Haikou, Xiamen, Shanghai, and Qingdao as research areas, and obtains data from social media. According to the comprehensive performance evaluation of the semantic segmentation and XGBoost models, the semantic segmentation model could effectively identify and extract water bodies, roads, and green spaces in satellite images, and the XGBoost model is more accurate and reliable than other common machine learning methods in prediction performance and precision. Among all waterlogging risk factors, elevation is the main factor affecting waterlogging in the research areas. For Shanghai and Qingdao, the secondary factor affecting waterlogging is roads. Water bodies are the secondary factor affecting urban waterlogging in Haikou. For Xiamen, the four indicators other than the elevation are equally significant, which could all be regarded as secondary factors affecting urban waterlogging.

Keywords: satellite images; urban waterlogging; semantic segmentation; XGBoost (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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