Highly Robust South Fork Zumbro River Flow Forecasting Based on Deep Temporal Modeling
River flow forecasting plays a vital role in water resource management and ecological conservation. Accurate flow forecasting enables decision makers to allocate resources efficiently, implement early flood prevention measures, and protect ecosystems. However, environmental noise interferes with for...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-10-01
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| Series: | Alexandria Engineering Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110016825009160 |
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| _version_ | 1849228455729692672 |
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| author | Yingjun Sun Peixia Wang Chong Wang Xiaodong Wang Hailin Feng Wei Wang Kai Fang |
| author_facet | Yingjun Sun Peixia Wang Chong Wang Xiaodong Wang Hailin Feng Wei Wang Kai Fang |
| author_sort | Yingjun Sun |
| collection | DOAJ |
| description | River flow forecasting plays a vital role in water resource management and ecological conservation. Accurate flow forecasting enables decision makers to allocate resources efficiently, implement early flood prevention measures, and protect ecosystems. However, environmental noise interferes with forecasting, reducing the precision and reliability of decision making. To address this, we propose a Highly Robust River Flow Forecasting Model (HRRFFM). The model comprises two components: data preprocessing and deep temporal modeling. Data preprocessing involves interpolation, environmental noise simulation, and Wiener filtering to improve data quality and model robustness. The deep temporal modeling integrates Bidirectional Long Short-Term Memory (BILSTM) networks and Transformer architecture to capture river flow dynamics. BILSTM captures bidirectional features, enhancing the model’s capacity to learn complex flow sequences. Transformer utilizes self-attention and multi-head attention mechanisms to model global dependencies and amplify subtle time-series variations, significantly improving feature extraction efficiency and forecasting accuracy. In this paper, Gaussian noise is employed to simulate environmental disturbances. The model’s performance is validated through ablation studies across varying noise levels and forecast horizons. At the noise intensity of σ = 0.05, for the three-hour-ahead predictions, HRRFFM outperforms baseline models with average improvements of 11.56%, 13.59%, and 4.98% in RMSE, MAE, and R2, respectively. |
| format | Article |
| id | doaj-art-9845b71f2c744979b5c7b99af70b0cc0 |
| institution | Kabale University |
| issn | 1110-0168 |
| language | English |
| publishDate | 2025-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj-art-9845b71f2c744979b5c7b99af70b0cc02025-08-23T04:47:38ZengElsevierAlexandria Engineering Journal1110-01682025-10-011291122113010.1016/j.aej.2025.08.020Highly Robust South Fork Zumbro River Flow Forecasting Based on Deep Temporal ModelingYingjun Sun0Peixia Wang1Chong Wang2Xiaodong Wang3Hailin Feng4Wei Wang5Kai Fang6Zhejiang Hydrological Management Center, Hangzhou 310020, ChinaChangshan County Hydrological Station, Quzhou 324299, ChinaZhejiang Hydrological New Technology Development and Management Co., Ltd, Hangzhou 310020, ChinaZhejiang Jiuzhou Water Control Technology Co., Ltd, Quzhou 324000, China; Corresponding author.College of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou 311300, ChinaGuangdong-Hong Kong-Macao Joint Laboratory for Emotion Intelligence and Pervasive Computing, Artificial Intelligence Research Institute, Shenzhen MSU-BIT University, Shenzhen 518172, China; School of Medical Technology, Beijing Institute of Technology, Beijing 100081, ChinaCollege of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou 311300, ChinaRiver flow forecasting plays a vital role in water resource management and ecological conservation. Accurate flow forecasting enables decision makers to allocate resources efficiently, implement early flood prevention measures, and protect ecosystems. However, environmental noise interferes with forecasting, reducing the precision and reliability of decision making. To address this, we propose a Highly Robust River Flow Forecasting Model (HRRFFM). The model comprises two components: data preprocessing and deep temporal modeling. Data preprocessing involves interpolation, environmental noise simulation, and Wiener filtering to improve data quality and model robustness. The deep temporal modeling integrates Bidirectional Long Short-Term Memory (BILSTM) networks and Transformer architecture to capture river flow dynamics. BILSTM captures bidirectional features, enhancing the model’s capacity to learn complex flow sequences. Transformer utilizes self-attention and multi-head attention mechanisms to model global dependencies and amplify subtle time-series variations, significantly improving feature extraction efficiency and forecasting accuracy. In this paper, Gaussian noise is employed to simulate environmental disturbances. The model’s performance is validated through ablation studies across varying noise levels and forecast horizons. At the noise intensity of σ = 0.05, for the three-hour-ahead predictions, HRRFFM outperforms baseline models with average improvements of 11.56%, 13.59%, and 4.98% in RMSE, MAE, and R2, respectively.http://www.sciencedirect.com/science/article/pii/S1110016825009160BILSTMTransformerHighly robustRiver flow forecasting |
| spellingShingle | Yingjun Sun Peixia Wang Chong Wang Xiaodong Wang Hailin Feng Wei Wang Kai Fang Highly Robust South Fork Zumbro River Flow Forecasting Based on Deep Temporal Modeling Alexandria Engineering Journal BILSTM Transformer Highly robust River flow forecasting |
| title | Highly Robust South Fork Zumbro River Flow Forecasting Based on Deep Temporal Modeling |
| title_full | Highly Robust South Fork Zumbro River Flow Forecasting Based on Deep Temporal Modeling |
| title_fullStr | Highly Robust South Fork Zumbro River Flow Forecasting Based on Deep Temporal Modeling |
| title_full_unstemmed | Highly Robust South Fork Zumbro River Flow Forecasting Based on Deep Temporal Modeling |
| title_short | Highly Robust South Fork Zumbro River Flow Forecasting Based on Deep Temporal Modeling |
| title_sort | highly robust south fork zumbro river flow forecasting based on deep temporal modeling |
| topic | BILSTM Transformer Highly robust River flow forecasting |
| url | http://www.sciencedirect.com/science/article/pii/S1110016825009160 |
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