Climate Impact on Evapotranspiration in the Yellow River Basin: Interpretable Forecasting with Advanced Time Series Models and Explainable AI - Sheheryar Khan; Huiliang Wang; Umer Nauman; Rabia Dars; Muhammad Waseem Boota; Zening Wu (Remote Sensing)
Evapotranspiration (ET) plays a crucial role in the hydrological cycle, significantly impacting agricultural productivity and water resource management, particularly in water-scarce areas. This study explores the effects of key climate variables temperature, precipitation, solar radiation, wind speed, and humidity on ET from 2000 to 2020, with forecasts extended to 2030. Advanced data preprocessing techniques, including Yeo-Johnson and Box-Cox transformations, Savitzky–Golay smoothing, and outlier elimination, were applied to improve data quality. Datasets from MODIS, TRMM, GLDAS, and ERA5 were utilized to enhance model accuracy. The predictive performance of various time series forecasting models, including Prophet, SARIMA, STL + ARIMA, TBATS, ARIMAX, and ETS, was systematically evaluated. This study also introduces novel algorithms for Explainable AI (XAI) and SHAP (SHapley Additive exPlanations), enhancing the interpretability of model predictions and improving understanding of how climate variables affect ET. This comprehensive methodology not only accurately forecasts ET but also offers a transparent approach to understanding climatic effects on ET. The results indicate that Prophet and ETS models demonstrate superior prediction accuracy compared to other models. The ETS model achieved the lowest Mean Absolute Error (MAE) values of 0.60 for precipitation, 0.51 for wind speed, and 0.48 for solar radiation. Prophet excelled with the lowest Root Mean Squared Error (RMSE) values of 0.62 for solar radiation, 0.67 for wind speed, and 0.74 for precipitation. SHAP analysis indicates that temperature has the strongest impact on ET predictions, with SHAP values ranging from −1.5 to 1.0, followed by wind speed (−0.75 to 0.75) and solar radiation (−0.5 to 0.5).