Medium- Long-Term Runoff Forecasting Using Interpretable Hybrid Machine Learning Model for Data-Scarce Regions
[Objectives] This study aims to analyze the applicability of existing precipitation, temperature, and runoff data in data-scarce regions, and to develop and evaluate a deep learning hybrid model driven by multi-source information for improving the accuracy of monthly runoff forecasting. [Methods] Ba...
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Editorial Office of Journal of Changjiang River Scientific Research Institute
2025-07-01
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| Series: | 长江科学院院报 |
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| Online Access: | http://ckyyb.crsri.cn/fileup/1001-5485/PDF/1001-5485(2025)07-0052-08.pdf |
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| author | YOU Yu-jun, BAI Yun-gang, LU Zhen-lin, ZHANG Jiang-hui, CAO Biao, LI Wen-zhong, YU Qi-ying |
| author_facet | YOU Yu-jun, BAI Yun-gang, LU Zhen-lin, ZHANG Jiang-hui, CAO Biao, LI Wen-zhong, YU Qi-ying |
| author_sort | YOU Yu-jun, BAI Yun-gang, LU Zhen-lin, ZHANG Jiang-hui, CAO Biao, LI Wen-zhong, YU Qi-ying |
| collection | DOAJ |
| description | [Objectives] This study aims to analyze the applicability of existing precipitation, temperature, and runoff data in data-scarce regions, and to develop and evaluate a deep learning hybrid model driven by multi-source information for improving the accuracy of monthly runoff forecasting. [Methods] Based on historical precipitation, temperature, and runoff sequences from the Yulongkashi River, a Convolutional Neural Network-Bidirectional Gated Recurrent Unit-Attention (CNN-BiGRU-Attention) model was developed. An Improved Particle Swarm Optimization (IPSO) algorithm was used to optimize this model, forming the IPSO-CNN-BiGRU-Attention hybrid model. The performance of this model was compared with that of the Gated Recurrent Unit (GRU) model and the ABCD water balance model. [Results] The IPSO-CNN-BiGRU-Attention model that incorporated precipitation and temperature data overall outperformed the CNN-BiGRU-Attention and GRU models, showing better agreement with the observed values. As the predication period increased, the proposed model achieved a root mean square error (RMSE) of 2.11 m3/s, a mean absolute error (MAE) of 1.32 m3/s, a mean absolute percentage error (MAPE) of 73.76%, and a Nash-Sutcliffe efficiency (NSE) coefficient of 0.94. The highest forecast accuracy was observed in the first three months. [Conclusions] The IPSO-CNN-BiGRU-Attention model effectively integrates precipitation, temperature, and runoff information, significantly enhancing the accuracy of monthly runoff forecasts in data-scarce regions. The model demonstrates robust performance across different forecast horizons, particularly suitable for short-term predictions of 1-3 months. This approach offers a practical and reliable tool for hydrological forecasting and flood control/drought management in data-scarce basins. |
| format | Article |
| id | doaj-art-fabd95626bb9455491316b89ba8acdf7 |
| institution | Matheson Library |
| issn | 1001-5485 |
| language | zho |
| publishDate | 2025-07-01 |
| publisher | Editorial Office of Journal of Changjiang River Scientific Research Institute |
| record_format | Article |
| series | 长江科学院院报 |
| spelling | doaj-art-fabd95626bb9455491316b89ba8acdf72025-07-22T02:36:49ZzhoEditorial Office of Journal of Changjiang River Scientific Research Institute长江科学院院报1001-54852025-07-01427525910.11988/ckyyb.20240319Medium- Long-Term Runoff Forecasting Using Interpretable Hybrid Machine Learning Model for Data-Scarce RegionsYOU Yu-jun, BAI Yun-gang, LU Zhen-lin, ZHANG Jiang-hui, CAO Biao, LI Wen-zhong, YU Qi-ying01 Shaanxi Hydrology and Water Resources Survey Center, Xi’an 710068, China;2 Xinjiang Research Institute of Water Resources and Hydropower,Urumqi 830049,China;3 School of Water Conservancy and Transportation, Zhengzhou University, Zhengzhou 450001, China[Objectives] This study aims to analyze the applicability of existing precipitation, temperature, and runoff data in data-scarce regions, and to develop and evaluate a deep learning hybrid model driven by multi-source information for improving the accuracy of monthly runoff forecasting. [Methods] Based on historical precipitation, temperature, and runoff sequences from the Yulongkashi River, a Convolutional Neural Network-Bidirectional Gated Recurrent Unit-Attention (CNN-BiGRU-Attention) model was developed. An Improved Particle Swarm Optimization (IPSO) algorithm was used to optimize this model, forming the IPSO-CNN-BiGRU-Attention hybrid model. The performance of this model was compared with that of the Gated Recurrent Unit (GRU) model and the ABCD water balance model. [Results] The IPSO-CNN-BiGRU-Attention model that incorporated precipitation and temperature data overall outperformed the CNN-BiGRU-Attention and GRU models, showing better agreement with the observed values. As the predication period increased, the proposed model achieved a root mean square error (RMSE) of 2.11 m3/s, a mean absolute error (MAE) of 1.32 m3/s, a mean absolute percentage error (MAPE) of 73.76%, and a Nash-Sutcliffe efficiency (NSE) coefficient of 0.94. The highest forecast accuracy was observed in the first three months. [Conclusions] The IPSO-CNN-BiGRU-Attention model effectively integrates precipitation, temperature, and runoff information, significantly enhancing the accuracy of monthly runoff forecasts in data-scarce regions. The model demonstrates robust performance across different forecast horizons, particularly suitable for short-term predictions of 1-3 months. This approach offers a practical and reliable tool for hydrological forecasting and flood control/drought management in data-scarce basins.http://ckyyb.crsri.cn/fileup/1001-5485/PDF/1001-5485(2025)07-0052-08.pdfrunoff forecasting|data-scarce regions|shap interpretable machine learning analysis|ipso-cnn-bigru-attention model|prediction accuracy |
| spellingShingle | YOU Yu-jun, BAI Yun-gang, LU Zhen-lin, ZHANG Jiang-hui, CAO Biao, LI Wen-zhong, YU Qi-ying Medium- Long-Term Runoff Forecasting Using Interpretable Hybrid Machine Learning Model for Data-Scarce Regions 长江科学院院报 runoff forecasting|data-scarce regions|shap interpretable machine learning analysis|ipso-cnn-bigru-attention model|prediction accuracy |
| title | Medium- Long-Term Runoff Forecasting Using Interpretable Hybrid Machine Learning Model for Data-Scarce Regions |
| title_full | Medium- Long-Term Runoff Forecasting Using Interpretable Hybrid Machine Learning Model for Data-Scarce Regions |
| title_fullStr | Medium- Long-Term Runoff Forecasting Using Interpretable Hybrid Machine Learning Model for Data-Scarce Regions |
| title_full_unstemmed | Medium- Long-Term Runoff Forecasting Using Interpretable Hybrid Machine Learning Model for Data-Scarce Regions |
| title_short | Medium- Long-Term Runoff Forecasting Using Interpretable Hybrid Machine Learning Model for Data-Scarce Regions |
| title_sort | medium long term runoff forecasting using interpretable hybrid machine learning model for data scarce regions |
| topic | runoff forecasting|data-scarce regions|shap interpretable machine learning analysis|ipso-cnn-bigru-attention model|prediction accuracy |
| url | http://ckyyb.crsri.cn/fileup/1001-5485/PDF/1001-5485(2025)07-0052-08.pdf |
| work_keys_str_mv | AT youyujunbaiyungangluzhenlinzhangjianghuicaobiaoliwenzhongyuqiying mediumlongtermrunoffforecastingusinginterpretablehybridmachinelearningmodelfordatascarceregions |