气候变化领域集成服务门户(CCPortal): A new interpretable streamflow prediction approach based on SWAT-BiLSTM and SHAP

CCPortal

DOI	10.1007/s11356-024-32725-z
	A new interpretable streamflow prediction approach based on SWAT-BiLSTM and SHAP
	Huang, Feiyun; Zhang, Xuyue
发表日期	2024
ISSN	0944-1344
EISSN	1614-7499
英文摘要	Streamflow is a crucial variable for assessing the available water resources for both human and environmental use. Accurate streamflow prediction plays a significant role in water resource management and assessing the impacts of climate change. This study explores the potential of coupling conceptual hydrological models based on physical processes with machine learning algorithms to enhance the performance of streamflow simulations. Four coupled models, namely SWAT-Transformer, SWAT-LSTM, SWAT-GRU, and SWAT-BiLSTM, were constructed in this research. SWAT served as a transfer function to convert four meteorological features, including precipitation, temperature, relative humidity, and wind speed, into six hydrological features: soil water content, lateral flow, percolation, groundwater discharge, surface runoff, and evapotranspiration. Machine learning algorithms were employed to capture the underlying relationships between these ten feature variables and the target variable (streamflow) to predict daily streamflow in the Sandu-River Basin (SRB). Among the four coupled models and the calibrated SWAT model, SWAT-BiLSTM exhibited the best streamflow simulation performance. During the calibration period (training period), it achieved R2 and NSE values of 0.92 and 0.91, respectively, and maintained them at 0.90 during the validation period (testing period). Additionally, the performance of all four coupled models surpassed that of the calibrated SWAT model. Compared to the tendency of the SWAT model to underestimate streamflow, the absolute values of PBIAS for all coupled models are below 10%, which indicates that there is no significant systematic bias evident. SHapley Additive exPlanations (SHAP) were used to analyze the impact of different feature variables on streamflow prediction. The results indicated that precipitation contributed the most to streamflow prediction, with a global importance of 29.7%. Hydrological feature variable output by the SWAT model played a dominant role in the Bi-LSTM's prediction process. Coupling conceptual hydrological models with machine learning algorithms can significantly enhance the predictive performance of streamflow. The application of SHAP improves the interpretability of the coupled models and enhances researchers' confidence in the prediction results.
英文关键词	SWAT; Bi-LSTM; Coupled modeling; SHAP; Streamflow prediction
语种	英语
WOS研究方向	Environmental Sciences & Ecology
WOS类目	Environmental Sciences
WOS记录号	WOS:001174453200007
来源期刊	ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/291145
作者单位	Sichuan University
推荐引用方式 GB/T 7714	Huang, Feiyun,Zhang, Xuyue. A new interpretable streamflow prediction approach based on SWAT-BiLSTM and SHAP[J],2024.
APA	Huang, Feiyun,&Zhang, Xuyue.(2024).A new interpretable streamflow prediction approach based on SWAT-BiLSTM and SHAP.ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH.
MLA	Huang, Feiyun,et al."A new interpretable streamflow prediction approach based on SWAT-BiLSTM and SHAP".ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH (2024).