Mountain flood forecasting in small watershed based on loop multi-step machine learning regression model
Abstract Mountain flood in small watershed is widely distributed disaster, which have the characteristics of strong suddenness, great harm, and frequently. The traditional hydrodynamic and manual forecasting methods have high error rates for hourly forecasting. In order to improve the accuracy and r...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-04-01
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| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-96029-z |
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| Summary: | Abstract Mountain flood in small watershed is widely distributed disaster, which have the characteristics of strong suddenness, great harm, and frequently. The traditional hydrodynamic and manual forecasting methods have high error rates for hourly forecasting. In order to improve the accuracy and real-time of water level forecasting in small watershed, we extract effective disaster-causing information, integrate multi-dimensional disaster-causing factors (such as hydrology, meteorology, geography, etc.), use a short-term prediction window and loop multi-step input method to improve the Machine Learning (ML) regression models’ accuracy, which can reduce the ML model’s process error. The non-ensemble and ensemble ML regression models is constructed for forecasting by loop multi-step, the non-ensemble models including Linear Regression (LR), Support Vector Machine Regression (SVMR) and k-Nearest Neighbors Regression (k-NNR), and the ensemble ML models include Random Forest Regression (RFR) and Gradient Boosting Regression (GBR). The loop multi-step ensemble ML regression models have the characteristics of high accurate and low time consumption than the general ML regression models for mountain flood forecasting in small watershed. |
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| ISSN: | 2045-2322 |