Improving the prediction of streamflow in large watersheds based on seasonal trend decomposition and vectorized deep learning models
Accurate streamflow prediction is essential for water resource management and ecological conservation. With climate change and human activities intensifying extreme weather events, the risks associated with floods have grown, threatening both socioeconomic robustness and ecological integrity. Conven...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-12-01
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| Series: | Ecological Informatics |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1574954125003000 |
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| _version_ | 1849233598694031360 |
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| author | Ningchang Kang Zhaocai Wang Anbin Zhang Hang Chen |
| author_facet | Ningchang Kang Zhaocai Wang Anbin Zhang Hang Chen |
| author_sort | Ningchang Kang |
| collection | DOAJ |
| description | Accurate streamflow prediction is essential for water resource management and ecological conservation. With climate change and human activities intensifying extreme weather events, the risks associated with floods have grown, threatening both socioeconomic robustness and ecological integrity. Conventional prediction methods, such as physical and statistical models, often struggle to capture the complex nonlinear and nonstationary characteristics of streamflow. To address this challenge, this study presents a vectorized hybrid STL-LSTM-GRU-Transformer model designed to enhance prediction accuracy and stability. The approach begins by applying seasonal-trend decomposition using loess (STL) to separate streamflow data into trend, seasonal, and residual components. These components are then modeled independently: long short-term memory (LSTM) and convolutional neural networks (CNN) handle trend and seasonal patterns, while gated recurrent units (GRU) and Transformer process residual fluctuations. Furthermore, the model incorporates the Runoff process vectorization (RPV) method alongside vectorization techniques to improve sensitivity to extreme events. Evaluated on 2010–2022 data from six Jialing River stations, the model achieves 0.9991 (NSE), outperforming 12 benchmarks. SHAP analysis identifies dew point temperature (26.7 % contribution) and solar radiation (15.7 %) as key drivers, while kernel density estimation provides reliable probabilistic forecasts (PICP = 0.90 at 95 % CI). Demonstrating robust performance in flood-drought transition prediction (NSE > 0.9983), this approach contributes valuable insights for advancing flood early warning systems and hydro-ecological security. |
| format | Article |
| id | doaj-art-4e0603035ee8469dbe1d595b1b643604 |
| institution | Kabale University |
| issn | 1574-9541 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Informatics |
| spelling | doaj-art-4e0603035ee8469dbe1d595b1b6436042025-08-20T05:05:35ZengElsevierEcological Informatics1574-95412025-12-019010329110.1016/j.ecoinf.2025.103291Improving the prediction of streamflow in large watersheds based on seasonal trend decomposition and vectorized deep learning modelsNingchang Kang0Zhaocai Wang1Anbin Zhang2Hang Chen3College of Information, Shanghai Ocean University, Shanghai 201306, PR ChinaCorresponding author.; College of Information, Shanghai Ocean University, Shanghai 201306, PR ChinaCollege of Information, Shanghai Ocean University, Shanghai 201306, PR ChinaCollege of Information, Shanghai Ocean University, Shanghai 201306, PR ChinaAccurate streamflow prediction is essential for water resource management and ecological conservation. With climate change and human activities intensifying extreme weather events, the risks associated with floods have grown, threatening both socioeconomic robustness and ecological integrity. Conventional prediction methods, such as physical and statistical models, often struggle to capture the complex nonlinear and nonstationary characteristics of streamflow. To address this challenge, this study presents a vectorized hybrid STL-LSTM-GRU-Transformer model designed to enhance prediction accuracy and stability. The approach begins by applying seasonal-trend decomposition using loess (STL) to separate streamflow data into trend, seasonal, and residual components. These components are then modeled independently: long short-term memory (LSTM) and convolutional neural networks (CNN) handle trend and seasonal patterns, while gated recurrent units (GRU) and Transformer process residual fluctuations. Furthermore, the model incorporates the Runoff process vectorization (RPV) method alongside vectorization techniques to improve sensitivity to extreme events. Evaluated on 2010–2022 data from six Jialing River stations, the model achieves 0.9991 (NSE), outperforming 12 benchmarks. SHAP analysis identifies dew point temperature (26.7 % contribution) and solar radiation (15.7 %) as key drivers, while kernel density estimation provides reliable probabilistic forecasts (PICP = 0.90 at 95 % CI). Demonstrating robust performance in flood-drought transition prediction (NSE > 0.9983), this approach contributes valuable insights for advancing flood early warning systems and hydro-ecological security.http://www.sciencedirect.com/science/article/pii/S1574954125003000Streamflow predictionDeep learningFeature extractionMode decompositionRunoff process vectorization |
| spellingShingle | Ningchang Kang Zhaocai Wang Anbin Zhang Hang Chen Improving the prediction of streamflow in large watersheds based on seasonal trend decomposition and vectorized deep learning models Ecological Informatics Streamflow prediction Deep learning Feature extraction Mode decomposition Runoff process vectorization |
| title | Improving the prediction of streamflow in large watersheds based on seasonal trend decomposition and vectorized deep learning models |
| title_full | Improving the prediction of streamflow in large watersheds based on seasonal trend decomposition and vectorized deep learning models |
| title_fullStr | Improving the prediction of streamflow in large watersheds based on seasonal trend decomposition and vectorized deep learning models |
| title_full_unstemmed | Improving the prediction of streamflow in large watersheds based on seasonal trend decomposition and vectorized deep learning models |
| title_short | Improving the prediction of streamflow in large watersheds based on seasonal trend decomposition and vectorized deep learning models |
| title_sort | improving the prediction of streamflow in large watersheds based on seasonal trend decomposition and vectorized deep learning models |
| topic | Streamflow prediction Deep learning Feature extraction Mode decomposition Runoff process vectorization |
| url | http://www.sciencedirect.com/science/article/pii/S1574954125003000 |
| work_keys_str_mv | AT ningchangkang improvingthepredictionofstreamflowinlargewatershedsbasedonseasonaltrenddecompositionandvectorizeddeeplearningmodels AT zhaocaiwang improvingthepredictionofstreamflowinlargewatershedsbasedonseasonaltrenddecompositionandvectorizeddeeplearningmodels AT anbinzhang improvingthepredictionofstreamflowinlargewatershedsbasedonseasonaltrenddecompositionandvectorizeddeeplearningmodels AT hangchen improvingthepredictionofstreamflowinlargewatershedsbasedonseasonaltrenddecompositionandvectorizeddeeplearningmodels |