Flood Classification and Improved Loss Function by Combining Deep Learning Models to Improve Water Level Prediction in a Small Mountain Watershed
ABSTRACT Floods are major natural disasters that present considerable challenges to socioeconomic and ecological systems. Flash floods are highly nonlinear and exhibit rapid spatiotemporal variability. Existing methods struggle to capture these features, leading to suboptimal long‐term and peak floo...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2025-06-01
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| Series: | Journal of Flood Risk Management |
| Subjects: | |
| Online Access: | https://doi.org/10.1111/jfr3.70022 |
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| _version_ | 1849433019044069376 |
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| author | Rukai Wang Ximin Yuan Fuchang Tian Minghui Liu Xiujie Wang Xiaobin Li Minrui Wu |
| author_facet | Rukai Wang Ximin Yuan Fuchang Tian Minghui Liu Xiujie Wang Xiaobin Li Minrui Wu |
| author_sort | Rukai Wang |
| collection | DOAJ |
| description | ABSTRACT Floods are major natural disasters that present considerable challenges to socioeconomic and ecological systems. Flash floods are highly nonlinear and exhibit rapid spatiotemporal variability. Existing methods struggle to capture these features, leading to suboptimal long‐term and peak flood prediction accuracy. This study proposes a hierarchical flood prediction model based on clustering to enhance forecasting accuracy in the Heshengxi watershed. We employ STGCN and GWN models with the spatiotemporal attention mechanism. Enhanced loss functions further refine flood prediction accuracy. Results show that the hierarchical prediction method is an effective means of extracting flood features by addressing the variability of prediction parameters for different flood magnitudes. The integration of Graph Convolutional and Time Aware models enables the model to recognize the spatiotemporal flood characteristics, overcoming limitations of prevailing methods and ensuring long‐term forecast accuracy. The optimized loss function further improves the prediction performance, resulting in a significant improvement in the accuracy of flood peak prediction, with a reduction of 0.26% in the relative error of the peak prediction by the GWN model. This framework provides an effective solution for flood warning, emergency response, and optimal scheduling. It also demonstrates the potential of deep learning models in the field of intelligent hydrological forecasting. |
| format | Article |
| id | doaj-art-72d1df7fc67e486dafb4b49df4d312ce |
| institution | Kabale University |
| issn | 1753-318X |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Flood Risk Management |
| spelling | doaj-art-72d1df7fc67e486dafb4b49df4d312ce2025-08-20T03:27:11ZengWileyJournal of Flood Risk Management1753-318X2025-06-01182n/an/a10.1111/jfr3.70022Flood Classification and Improved Loss Function by Combining Deep Learning Models to Improve Water Level Prediction in a Small Mountain WatershedRukai Wang0Ximin Yuan1Fuchang Tian2Minghui Liu3Xiujie Wang4Xiaobin Li5Minrui Wu6State Key Laboratory of Hydraulic Engineering Intelligent Construction and Operation Tianjin University Tianjin ChinaState Key Laboratory of Hydraulic Engineering Intelligent Construction and Operation Tianjin University Tianjin ChinaState Key Laboratory of Hydraulic Engineering Intelligent Construction and Operation Tianjin University Tianjin ChinaResearch Institute of Soil and Water Conservation Chengde ChinaState Key Laboratory of Hydraulic Engineering Intelligent Construction and Operation Tianjin University Tianjin ChinaWater Conservancy Workstation Chengde ChinaState Key Laboratory of Hydraulic Engineering Intelligent Construction and Operation Tianjin University Tianjin ChinaABSTRACT Floods are major natural disasters that present considerable challenges to socioeconomic and ecological systems. Flash floods are highly nonlinear and exhibit rapid spatiotemporal variability. Existing methods struggle to capture these features, leading to suboptimal long‐term and peak flood prediction accuracy. This study proposes a hierarchical flood prediction model based on clustering to enhance forecasting accuracy in the Heshengxi watershed. We employ STGCN and GWN models with the spatiotemporal attention mechanism. Enhanced loss functions further refine flood prediction accuracy. Results show that the hierarchical prediction method is an effective means of extracting flood features by addressing the variability of prediction parameters for different flood magnitudes. The integration of Graph Convolutional and Time Aware models enables the model to recognize the spatiotemporal flood characteristics, overcoming limitations of prevailing methods and ensuring long‐term forecast accuracy. The optimized loss function further improves the prediction performance, resulting in a significant improvement in the accuracy of flood peak prediction, with a reduction of 0.26% in the relative error of the peak prediction by the GWN model. This framework provides an effective solution for flood warning, emergency response, and optimal scheduling. It also demonstrates the potential of deep learning models in the field of intelligent hydrological forecasting.https://doi.org/10.1111/jfr3.70022deep learningHesheng Riverspatial‐temporal featureswater level forecasting |
| spellingShingle | Rukai Wang Ximin Yuan Fuchang Tian Minghui Liu Xiujie Wang Xiaobin Li Minrui Wu Flood Classification and Improved Loss Function by Combining Deep Learning Models to Improve Water Level Prediction in a Small Mountain Watershed Journal of Flood Risk Management deep learning Hesheng River spatial‐temporal features water level forecasting |
| title | Flood Classification and Improved Loss Function by Combining Deep Learning Models to Improve Water Level Prediction in a Small Mountain Watershed |
| title_full | Flood Classification and Improved Loss Function by Combining Deep Learning Models to Improve Water Level Prediction in a Small Mountain Watershed |
| title_fullStr | Flood Classification and Improved Loss Function by Combining Deep Learning Models to Improve Water Level Prediction in a Small Mountain Watershed |
| title_full_unstemmed | Flood Classification and Improved Loss Function by Combining Deep Learning Models to Improve Water Level Prediction in a Small Mountain Watershed |
| title_short | Flood Classification and Improved Loss Function by Combining Deep Learning Models to Improve Water Level Prediction in a Small Mountain Watershed |
| title_sort | flood classification and improved loss function by combining deep learning models to improve water level prediction in a small mountain watershed |
| topic | deep learning Hesheng River spatial‐temporal features water level forecasting |
| url | https://doi.org/10.1111/jfr3.70022 |
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