Flash flood susceptibility mapping based on catchments using an improved Blending machine learning approach
Flash floods are a frequent and highly destructive natural hazard in China. In order to prevent and manage these disasters, it is crucial for decision-makers to create GIS-based flash flood susceptibility maps. In this study, we present an improved Blending approach, RF-Blending (Reserve Feature Ble...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IWA Publishing
2023-04-01
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| Series: | Hydrology Research |
| Subjects: | |
| Online Access: | http://hr.iwaponline.com/content/54/4/557 |
| Summary: | Flash floods are a frequent and highly destructive natural hazard in China. In order to prevent and manage these disasters, it is crucial for decision-makers to create GIS-based flash flood susceptibility maps. In this study, we present an improved Blending approach, RF-Blending (Reserve Feature Blending), which differs from the Blending approach in that it preserves the original feature dataset during meta-learner training. Our objectives were to demonstrate the performance improvement of the RF-Blending approach and to produce flash flood susceptibility maps for all catchments in Jiangxi Province using the RF-Blending approach. The Blending approach employs a double-layer structure consisting of support vector machine (SVM), K-nearest neighbor (KNN), and random forest (RF) as base learners for level-0, and the output of level-0 is utilized as the meta-feature dataset for the meta-learner in level-1, which is logistic regression (LR). RF-Blending employs the output of level-0 along with the original feature dataset for meta-learner training. To develop flood susceptibility maps, we utilized these approaches in conjunction with historical flash flood points and catchment-based factors. Our results indicate that the RF-Blending approach outperformed the other approaches. These can significantly aid catchment-based flash flood susceptibility mapping and assist managers in controlling and remediating induced damages.
HIGHLIGHTS
Catchments as basic study units.;
Producing flash flood susceptibility maps using machine learning approaches.;
An improved Blending approach.; |
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| ISSN: | 1998-9563 2224-7955 |