An explainable Bayesian gated recurrent unit model for multi-step streamflow forecasting
Study region: In the middle and lower reaches of the Yangtze River Basin of ChinaStudy focus: We propose an explainable Bayesian gated recurrent unit (EB-GRU) model for reliable multi-step streamflow forecasting. The proposed model introduces Bayesian inference into a gated recurrent unit (GRU) to q...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-02-01
|
| Series: | Journal of Hydrology: Regional Studies |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214581824004907 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832591772836954112 |
|---|---|
| author | Lizhi Tao Yueming Nan Zhichao Cui Lei Wang Dong Yang |
| author_facet | Lizhi Tao Yueming Nan Zhichao Cui Lei Wang Dong Yang |
| author_sort | Lizhi Tao |
| collection | DOAJ |
| description | Study region: In the middle and lower reaches of the Yangtze River Basin of ChinaStudy focus: We propose an explainable Bayesian gated recurrent unit (EB-GRU) model for reliable multi-step streamflow forecasting. The proposed model introduces Bayesian inference into a gated recurrent unit (GRU) to quantify the uncertainty of streamflow prediction, and uses SHapley Additive exPlanations (SHAP) method to analyze the importance of hydrometeorological indices on streamflow prediction. The EB-GRU is examined by forecasting the multi-step streamflow at Hukou and Qilishan stations in the middle and lower reaches of the Yangtze River Basin, and compared with the Transformer (TSF), multi-layer perceptron (MLP) and support vector machine (SVM).New hydrological insights for the region: The comparative results show that the performance of the proposed EB-GRU surpasses that of the TSF, except for the streamflow forecast at the Hukou station with a 1-day lead time. The EB-GRU outperforms the MLP and SVM at each lead time, particularly at shorter lead times, highlighting its effectiveness in capturing short-term streamflow dynamics. The analysis of uncertainty quantization shows that noise in the input data is the primary source of overall uncertainty in model prediction, whereas a notable increase is observed in the uncertainty caused by the model in the flood season. Furthermore, the application of the SHAP method reveals the critical role of water level in streamflow prediction. |
| format | Article |
| id | doaj-art-6832999ab2944f61910db9b4800f8980 |
| institution | Kabale University |
| issn | 2214-5818 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Hydrology: Regional Studies |
| spelling | doaj-art-6832999ab2944f61910db9b4800f89802025-01-22T05:42:11ZengElsevierJournal of Hydrology: Regional Studies2214-58182025-02-0157102141An explainable Bayesian gated recurrent unit model for multi-step streamflow forecastingLizhi Tao0Yueming Nan1Zhichao Cui2Lei Wang3Dong Yang4Key Laboratory of Poyang Lake Wetland and Watershed Research of Ministry of Education & School of Geography and Environmental Science, Jiangxi Normal University, Nanchang 330022, ChinaKey Laboratory of Poyang Lake Wetland and Watershed Research of Ministry of Education & School of Geography and Environmental Science, Jiangxi Normal University, Nanchang 330022, ChinaKey Laboratory of Poyang Lake Wetland and Watershed Research of Ministry of Education & School of Geography and Environmental Science, Jiangxi Normal University, Nanchang 330022, ChinaBeijing Fengyun Meteorological Technology Development Company Limited, Beijing 100000, ChinaJiangxi Academy of Eco-Environmental Sciences & Planning, Nanchang 330029, China; Jiangxi Provincial Key Laboratory of Environmental Pollution Control, Nanchang 330029, China; Corresponding author at: Jiangxi Provincial Key Laboratory of Environmental Pollution Control, Nanchang 330029, China.Study region: In the middle and lower reaches of the Yangtze River Basin of ChinaStudy focus: We propose an explainable Bayesian gated recurrent unit (EB-GRU) model for reliable multi-step streamflow forecasting. The proposed model introduces Bayesian inference into a gated recurrent unit (GRU) to quantify the uncertainty of streamflow prediction, and uses SHapley Additive exPlanations (SHAP) method to analyze the importance of hydrometeorological indices on streamflow prediction. The EB-GRU is examined by forecasting the multi-step streamflow at Hukou and Qilishan stations in the middle and lower reaches of the Yangtze River Basin, and compared with the Transformer (TSF), multi-layer perceptron (MLP) and support vector machine (SVM).New hydrological insights for the region: The comparative results show that the performance of the proposed EB-GRU surpasses that of the TSF, except for the streamflow forecast at the Hukou station with a 1-day lead time. The EB-GRU outperforms the MLP and SVM at each lead time, particularly at shorter lead times, highlighting its effectiveness in capturing short-term streamflow dynamics. The analysis of uncertainty quantization shows that noise in the input data is the primary source of overall uncertainty in model prediction, whereas a notable increase is observed in the uncertainty caused by the model in the flood season. Furthermore, the application of the SHAP method reveals the critical role of water level in streamflow prediction.http://www.sciencedirect.com/science/article/pii/S2214581824004907Gated recurrent unitBayesian deep learningSHapley Additive exPlanationsMulti-step streamflow forecasting |
| spellingShingle | Lizhi Tao Yueming Nan Zhichao Cui Lei Wang Dong Yang An explainable Bayesian gated recurrent unit model for multi-step streamflow forecasting Journal of Hydrology: Regional Studies Gated recurrent unit Bayesian deep learning SHapley Additive exPlanations Multi-step streamflow forecasting |
| title | An explainable Bayesian gated recurrent unit model for multi-step streamflow forecasting |
| title_full | An explainable Bayesian gated recurrent unit model for multi-step streamflow forecasting |
| title_fullStr | An explainable Bayesian gated recurrent unit model for multi-step streamflow forecasting |
| title_full_unstemmed | An explainable Bayesian gated recurrent unit model for multi-step streamflow forecasting |
| title_short | An explainable Bayesian gated recurrent unit model for multi-step streamflow forecasting |
| title_sort | explainable bayesian gated recurrent unit model for multi step streamflow forecasting |
| topic | Gated recurrent unit Bayesian deep learning SHapley Additive exPlanations Multi-step streamflow forecasting |
| url | http://www.sciencedirect.com/science/article/pii/S2214581824004907 |
| work_keys_str_mv | AT lizhitao anexplainablebayesiangatedrecurrentunitmodelformultistepstreamflowforecasting AT yuemingnan anexplainablebayesiangatedrecurrentunitmodelformultistepstreamflowforecasting AT zhichaocui anexplainablebayesiangatedrecurrentunitmodelformultistepstreamflowforecasting AT leiwang anexplainablebayesiangatedrecurrentunitmodelformultistepstreamflowforecasting AT dongyang anexplainablebayesiangatedrecurrentunitmodelformultistepstreamflowforecasting AT lizhitao explainablebayesiangatedrecurrentunitmodelformultistepstreamflowforecasting AT yuemingnan explainablebayesiangatedrecurrentunitmodelformultistepstreamflowforecasting AT zhichaocui explainablebayesiangatedrecurrentunitmodelformultistepstreamflowforecasting AT leiwang explainablebayesiangatedrecurrentunitmodelformultistepstreamflowforecasting AT dongyang explainablebayesiangatedrecurrentunitmodelformultistepstreamflowforecasting |