Prediction of flood sensitivity based on Logistic Regression, eXtreme Gradient Boosting, and Random Forest modeling methods.

Wu, Ying; Zhang, Zhiming; Qi, Xiaotian; Hu, Wenhan; Si, Shuai

Wu, Ying; Zhang, Zhiming; Qi, Xiaotian; Hu, Wenhan; Si, Shuai.

Afiliación

Wu Y; Department of Environment and Energy Engineering, Beijing University of Civil Engineering and Architecture, No. 1 Zhanlanguan Road, Beijing 100044, China.
Zhang Z; Beijing Climate Change Response Research and Education Center, School of Environment and Energy Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100044, China E-mail: zhangzhiming@bucea.edu.cn.
Qi X; Department of Environment and Energy Engineering, Beijing University of Civil Engineering and Architecture, No. 1 Zhanlanguan Road, Beijing 100044, China.
Hu W; Department of Environment and Energy Engineering, Beijing University of Civil Engineering and Architecture, No. 1 Zhanlanguan Road, Beijing 100044, China.
Si S; Department of Environment and Energy Engineering, Beijing University of Civil Engineering and Architecture, No. 1 Zhanlanguan Road, Beijing 100044, China.

Water Sci Technol ; 89(10): 2605-2624, 2024 May.

Article en En | MEDLINE | ID: mdl-38822603

ABSTRACT

ABSTRACT

Floods are one of the most destructive disasters that cause loss of life and property worldwide every year. In this study, the aim was to find the best-performing model in flood sensitivity assessment and analyze key characteristic factors, the spatial pattern of flood sensitivity was evaluated using three machine learning (ML) models Logistic Regression (LR), eXtreme Gradient Boosting (XGBoost), and Random Forest (RF). Suqian City in Jiangsu Province was selected as the study area, and a random sample dataset of historical flood points was constructed. Fifteen different meteorological, hydrological, and geographical spatial variables were considered in the flood sensitivity assessment, 12 variables were selected based on the multi-collinearity study. Among the results of comparing the selected ML models, the RF method had the highest AUC value, accuracy, and comprehensive evaluation effect, and is a reliable and effective flood risk assessment model. As the main output of this study, the flood sensitivity map is divided into five categories, ranging from very low to very high sensitivity. Using the RF model (i.e., the highest accuracy of the model), the high-risk area covers about 44% of the study area, mainly concentrated in the central, eastern, and southern parts of the old city area.

Asunto(s)

Inundaciones; Modelos Logísticos; Aprendizaje Automático; China; Modelos Teóricos; Bosques Aleatorios

Palabras clave

Logistic Regression (LR); Random Forest (RF); eXtreme Gradient Boosting (XGBoost); flood sensitivity assessment

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Inundaciones País/Región como asunto: Asia Idioma: En Revista: Water Sci Technol Asunto de la revista: SAUDE AMBIENTAL / TOXICOLOGIA Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Reino Unido

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