Probabilistic daily runoff forecasting in high-altitude cold regions using a hybrid model combining DBO and transformer variants
Study Area: The Tailan River Basin in the Aksu region and the Yulong Kashi River in the Hotan River Basin of Xinjiang are located at respective geographical coordinates of 80°21'44'' to 81°10'14'' E, 40°41'41'' to 42°15'13'' N, and 77.25° t...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-06-01
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| Series: | Journal of Hydrology: Regional Studies |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214581825001351 |
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| author | Qiying Yu Wenzhong Li Yungang Bai Zhenlin Lu Yingying Xu Chengshuai Liu Lu Tian Chen Shi Biao Cao Tianning Xie Jianghui Zhang Caihong Hu |
| author_facet | Qiying Yu Wenzhong Li Yungang Bai Zhenlin Lu Yingying Xu Chengshuai Liu Lu Tian Chen Shi Biao Cao Tianning Xie Jianghui Zhang Caihong Hu |
| author_sort | Qiying Yu |
| collection | DOAJ |
| description | Study Area: The Tailan River Basin in the Aksu region and the Yulong Kashi River in the Hotan River Basin of Xinjiang are located at respective geographical coordinates of 80°21'44'' to 81°10'14'' E, 40°41'41'' to 42°15'13'' N, and 77.25° to 81.75° E, 34.75° to 36.25° N. Study Focus: To tackle the complexity of runoff prediction in high-altitude cold regions, alongside the limitations of existing machine learning approaches, where nonlinear relationships, long-term dependencies, and sparse observational data pose significant challenges, previous models have consistently struggled to account for these issues. In response, we propose a hybrid runoff prediction model that combines Dung Beetle Optimization (DBO)'s optimization capabilities, Temporal Convolutional Networks (TCN)’s proficiency in extracting local temporal features, and the Transformer’s ability to capture long-term dependencies. In addition, the Bootstrap method is employed to merge point prediction outcomes for interval runoff forecasting, providing robust uncertainty estimates to address data limitations in these regions. New Hydrological Insights for the Region: The DBO-TCN-Transformer model consistently attains a Nash-Sutcliffe Efficiency (NSE) above 0.81, showcasing enhanced performance over traditional models. Across various forecast periods, the model’s NSE values are 6.9–26.9 % higher than those of the TCN and Transformer models, offering more reliable short-term and long-term predictions. Furthermore, the Bootstrap algorithm’s probabilistic approach provides valuable insights into forecast uncertainty, a crucial feature for managing water resources and mitigating flood risks in high-altitude cold regions with complex hydrological dynamics. |
| format | Article |
| id | doaj-art-001504a596df4753885d1faaeec32749 |
| institution | Kabale University |
| issn | 2214-5818 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Hydrology: Regional Studies |
| spelling | doaj-art-001504a596df4753885d1faaeec327492025-08-20T03:47:33ZengElsevierJournal of Hydrology: Regional Studies2214-58182025-06-015910231110.1016/j.ejrh.2025.102311Probabilistic daily runoff forecasting in high-altitude cold regions using a hybrid model combining DBO and transformer variantsQiying Yu0Wenzhong Li1Yungang Bai2Zhenlin Lu3Yingying Xu4Chengshuai Liu5Lu Tian6Chen Shi7Biao Cao8Tianning Xie9Jianghui Zhang10Caihong Hu11School of Water Conservancy and Transportation, Zhengzhou University, Zhengzhou 450001, China; Xinjiang Research Institute of Water Resources and Hydropower, Xinjiang 830049, ChinaSchool of Water Conservancy and Transportation, Zhengzhou University, Zhengzhou 450001, ChinaXinjiang Research Institute of Water Resources and Hydropower, Xinjiang 830049, China; Correspondence to: Xinjiang Research Institute of Water Resources and Hydropower, 830049, China.Xinjiang Research Institute of Water Resources and Hydropower, Xinjiang 830049, China; Correspondence to: Xinjiang Research Institute of Water Resources and Hydropower, 830049, China.School of Water Conservancy and Transportation, Zhengzhou University, Zhengzhou 450001, ChinaSchool of Water Conservancy and Transportation, Zhengzhou University, Zhengzhou 450001, ChinaSchool of Water Conservancy and Transportation, Zhengzhou University, Zhengzhou 450001, ChinaSchool of Water Conservancy and Transportation, Zhengzhou University, Zhengzhou 450001, ChinaXinjiang Research Institute of Water Resources and Hydropower, Xinjiang 830049, ChinaSchool of Water Conservancy and Transportation, Zhengzhou University, Zhengzhou 450001, ChinaXinjiang Research Institute of Water Resources and Hydropower, Xinjiang 830049, ChinaSchool of Water Conservancy and Transportation, Zhengzhou University, Zhengzhou 450001, China; Correspondence to: School of Water Conservancy and Transportation, 450001, China.Study Area: The Tailan River Basin in the Aksu region and the Yulong Kashi River in the Hotan River Basin of Xinjiang are located at respective geographical coordinates of 80°21'44'' to 81°10'14'' E, 40°41'41'' to 42°15'13'' N, and 77.25° to 81.75° E, 34.75° to 36.25° N. Study Focus: To tackle the complexity of runoff prediction in high-altitude cold regions, alongside the limitations of existing machine learning approaches, where nonlinear relationships, long-term dependencies, and sparse observational data pose significant challenges, previous models have consistently struggled to account for these issues. In response, we propose a hybrid runoff prediction model that combines Dung Beetle Optimization (DBO)'s optimization capabilities, Temporal Convolutional Networks (TCN)’s proficiency in extracting local temporal features, and the Transformer’s ability to capture long-term dependencies. In addition, the Bootstrap method is employed to merge point prediction outcomes for interval runoff forecasting, providing robust uncertainty estimates to address data limitations in these regions. New Hydrological Insights for the Region: The DBO-TCN-Transformer model consistently attains a Nash-Sutcliffe Efficiency (NSE) above 0.81, showcasing enhanced performance over traditional models. Across various forecast periods, the model’s NSE values are 6.9–26.9 % higher than those of the TCN and Transformer models, offering more reliable short-term and long-term predictions. Furthermore, the Bootstrap algorithm’s probabilistic approach provides valuable insights into forecast uncertainty, a crucial feature for managing water resources and mitigating flood risks in high-altitude cold regions with complex hydrological dynamics.http://www.sciencedirect.com/science/article/pii/S2214581825001351Runoff ForecastingDBO-TCN-Transformer ModelProbabilistic ForecastingTailan River Basin |
| spellingShingle | Qiying Yu Wenzhong Li Yungang Bai Zhenlin Lu Yingying Xu Chengshuai Liu Lu Tian Chen Shi Biao Cao Tianning Xie Jianghui Zhang Caihong Hu Probabilistic daily runoff forecasting in high-altitude cold regions using a hybrid model combining DBO and transformer variants Journal of Hydrology: Regional Studies Runoff Forecasting DBO-TCN-Transformer Model Probabilistic Forecasting Tailan River Basin |
| title | Probabilistic daily runoff forecasting in high-altitude cold regions using a hybrid model combining DBO and transformer variants |
| title_full | Probabilistic daily runoff forecasting in high-altitude cold regions using a hybrid model combining DBO and transformer variants |
| title_fullStr | Probabilistic daily runoff forecasting in high-altitude cold regions using a hybrid model combining DBO and transformer variants |
| title_full_unstemmed | Probabilistic daily runoff forecasting in high-altitude cold regions using a hybrid model combining DBO and transformer variants |
| title_short | Probabilistic daily runoff forecasting in high-altitude cold regions using a hybrid model combining DBO and transformer variants |
| title_sort | probabilistic daily runoff forecasting in high altitude cold regions using a hybrid model combining dbo and transformer variants |
| topic | Runoff Forecasting DBO-TCN-Transformer Model Probabilistic Forecasting Tailan River Basin |
| url | http://www.sciencedirect.com/science/article/pii/S2214581825001351 |
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