Modeling rules of regional flash flood susceptibility prediction using different machine learning models
The prediction performance of several machine learning models for regional flash flood susceptibility is characterized by variability and regionality. Four typical machine learning models, including multilayer perceptron (MLP), logistic regression (LR), support vector machine (SVM), and random fores...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-01-01
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| Series: | Frontiers in Earth Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/feart.2023.1117004/full |
| _version_ | 1797951444436910080 |
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| author | Yuguo Chen Xinyi Zhang Kejun Yang Shiyi Zeng Anyu Hong |
| author_facet | Yuguo Chen Xinyi Zhang Kejun Yang Shiyi Zeng Anyu Hong |
| author_sort | Yuguo Chen |
| collection | DOAJ |
| description | The prediction performance of several machine learning models for regional flash flood susceptibility is characterized by variability and regionality. Four typical machine learning models, including multilayer perceptron (MLP), logistic regression (LR), support vector machine (SVM), and random forest (RF), are proposed to carry out flash flood susceptibility modeling in order to investigate the modeling rules of different machine learning models in predicting flash flood susceptibility. The original data of 14 environmental factors, such as elevation, slope, aspect, gully density, and highway density, are chosen as input variables for the MLP, LR, SVM, and RF models in order to estimate and map the distribution of the flash flood susceptibility index in Longnan County, Jiangxi Province, China. Finally, the prediction performance of various models and modeling rules is evaluated using the ROC curve and the susceptibility index distribution features. The findings show that: 1) Machine learning models can accurately assess the region’s vulnerability to flash floods. The MLP, LR, SVM, and RF models all predict susceptibility very well. 2) The MLP (AUC=0.973, MV=0.1017, SD=0.2627) model has the best prediction performance for flash flood susceptibility, followed by the SVM (AUC=0.964, MV=0.1090, SD=0.2561) and RF (AUC=0.975, MV=0.2041, SD=0.1943) models, and the LR (AUC=0.882, MV=0.2613, SD=0.2913) model. 3) To a large extent, environmental factors such as elevation, gully density, and population density influence flash flood susceptibility. |
| first_indexed | 2024-04-10T22:30:42Z |
| format | Article |
| id | doaj.art-591c08f6cd5149b0899c7dd169db499d |
| institution | Directory Open Access Journal |
| issn | 2296-6463 |
| language | English |
| last_indexed | 2024-04-10T22:30:42Z |
| publishDate | 2023-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Earth Science |
| spelling | doaj.art-591c08f6cd5149b0899c7dd169db499d2023-01-17T05:26:50ZengFrontiers Media S.A.Frontiers in Earth Science2296-64632023-01-011110.3389/feart.2023.11170041117004Modeling rules of regional flash flood susceptibility prediction using different machine learning modelsYuguo ChenXinyi ZhangKejun YangShiyi ZengAnyu HongThe prediction performance of several machine learning models for regional flash flood susceptibility is characterized by variability and regionality. Four typical machine learning models, including multilayer perceptron (MLP), logistic regression (LR), support vector machine (SVM), and random forest (RF), are proposed to carry out flash flood susceptibility modeling in order to investigate the modeling rules of different machine learning models in predicting flash flood susceptibility. The original data of 14 environmental factors, such as elevation, slope, aspect, gully density, and highway density, are chosen as input variables for the MLP, LR, SVM, and RF models in order to estimate and map the distribution of the flash flood susceptibility index in Longnan County, Jiangxi Province, China. Finally, the prediction performance of various models and modeling rules is evaluated using the ROC curve and the susceptibility index distribution features. The findings show that: 1) Machine learning models can accurately assess the region’s vulnerability to flash floods. The MLP, LR, SVM, and RF models all predict susceptibility very well. 2) The MLP (AUC=0.973, MV=0.1017, SD=0.2627) model has the best prediction performance for flash flood susceptibility, followed by the SVM (AUC=0.964, MV=0.1090, SD=0.2561) and RF (AUC=0.975, MV=0.2041, SD=0.1943) models, and the LR (AUC=0.882, MV=0.2613, SD=0.2913) model. 3) To a large extent, environmental factors such as elevation, gully density, and population density influence flash flood susceptibility.https://www.frontiersin.org/articles/10.3389/feart.2023.1117004/fullflash flood susceptibility predictionuncertainty analysismachine learningmultilayer perceptronsupport vector machinerandom forest |
| spellingShingle | Yuguo Chen Xinyi Zhang Kejun Yang Shiyi Zeng Anyu Hong Modeling rules of regional flash flood susceptibility prediction using different machine learning models Frontiers in Earth Science flash flood susceptibility prediction uncertainty analysis machine learning multilayer perceptron support vector machine random forest |
| title | Modeling rules of regional flash flood susceptibility prediction using different machine learning models |
| title_full | Modeling rules of regional flash flood susceptibility prediction using different machine learning models |
| title_fullStr | Modeling rules of regional flash flood susceptibility prediction using different machine learning models |
| title_full_unstemmed | Modeling rules of regional flash flood susceptibility prediction using different machine learning models |
| title_short | Modeling rules of regional flash flood susceptibility prediction using different machine learning models |
| title_sort | modeling rules of regional flash flood susceptibility prediction using different machine learning models |
| topic | flash flood susceptibility prediction uncertainty analysis machine learning multilayer perceptron support vector machine random forest |
| url | https://www.frontiersin.org/articles/10.3389/feart.2023.1117004/full |
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