Embedded Feature Selection and Machine Learning Methods for Flash Flood Susceptibility-Mapping in the Mainstream Songhua River Basin, China

Mapping flash flood susceptibility is effective for mitigating the negative impacts of flash floods. However, a variety of conditioning factors have been used to generate susceptibility maps in various studies. In this study, we proposed combining logistic regression (LR) and random forest (RF) mode...

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Published inRemote sensing (Basel, Switzerland) Vol. 14; no. 21; p. 5523
Main Authors Li, Jianuo, Zhang, Hongyan, Zhao, Jianjun, Guo, Xiaoyi, Rihan, Wu, Deng, Guorong
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.11.2022
Subjects
Algorithms
Basins
Climate change
Decision making
Emergency preparedness
Feature selection
flash flood
Flash floods
Flood control
Flood mapping
Floods
logistic regression
Machine learning
Mapping
Methods
Model accuracy
Precipitation
random forest
Regression analysis
River basins
Rivers
Susceptibility
susceptibility mapping
Vegetation cover
Songhua River
China
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Summary:Mapping flash flood susceptibility is effective for mitigating the negative impacts of flash floods. However, a variety of conditioning factors have been used to generate susceptibility maps in various studies. In this study, we proposed combining logistic regression (LR) and random forest (RF) models with embedded feature selection (EFS) to filter specific feature sets for the two models and map flash flood susceptibility in the mainstream basin of the Songhua River. According to the EFS results, the optimized feature sets included 32 and 28 features for the LR and RF models, respectively, and the composition of the two optimal feature sets was similar and distinct. Overall, the relevant vegetation cover and river features exhibit relatively high effects overall for flash floods in the study area. The LR and RF models provided accurate and reliable flash flood susceptibility maps (FFSMs). The RF model (accuracy = 0.8834, area under the curve (AUC) = 0.9486) provided a better prediction capacity than the LR model (accuracy = 0.8634, AUC = 0.9277). Flash flood-prone areas are mainly distributed in the south and southwest and areas close to rivers. The results obtained in this study is useful for flash flood prevention and control projects.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs14215523