Climate Impact on Evapotranspiration in the Yellow River Basin: Interpretable Forecasting with Advanced Time Series Models and Explainable AI
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 spee...
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| Format: | Article |
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MDPI AG
2025-01-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/1/115 |
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| author | Sheheryar Khan Huiliang Wang Umer Nauman Rabia Dars Muhammad Waseem Boota Zening Wu |
| author_facet | Sheheryar Khan Huiliang Wang Umer Nauman Rabia Dars Muhammad Waseem Boota Zening Wu |
| author_sort | Sheheryar Khan |
| collection | DOAJ |
| description | 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). |
| format | Article |
| id | doaj-art-79b069c8cc724a5db1472702a99b18c5 |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-79b069c8cc724a5db1472702a99b18c52025-01-10T13:20:16ZengMDPI AGRemote Sensing2072-42922025-01-0117111510.3390/rs17010115Climate Impact on Evapotranspiration in the Yellow River Basin: Interpretable Forecasting with Advanced Time Series Models and Explainable AISheheryar Khan0Huiliang Wang1Umer Nauman2Rabia Dars3Muhammad Waseem Boota4Zening Wu5School of Water Conservancy and Transportation, Zhengzhou University, Zhengzhou 450001, ChinaSchool of Water Conservancy and Transportation, Zhengzhou University, Zhengzhou 450001, ChinaCollege of Information Science and Engineering, Henan University of Technology, Zhengzhou 450001, ChinaSchool of Water Conservancy and Transportation, Zhengzhou University, Zhengzhou 450001, ChinaCollege of Geography and Environmental Science, Henan University, Kaifeng 475004, ChinaSchool of Water Conservancy and Transportation, Zhengzhou University, Zhengzhou 450001, ChinaEvapotranspiration (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).https://www.mdpi.com/2072-4292/17/1/115evapotranspirationmachine learningclimate variablesexplainable AIinterpretabilityYellow River Basin China |
| spellingShingle | Sheheryar Khan Huiliang Wang Umer Nauman Rabia Dars Muhammad Waseem Boota Zening Wu Climate Impact on Evapotranspiration in the Yellow River Basin: Interpretable Forecasting with Advanced Time Series Models and Explainable AI Remote Sensing evapotranspiration machine learning climate variables explainable AI interpretability Yellow River Basin China |
| title | Climate Impact on Evapotranspiration in the Yellow River Basin: Interpretable Forecasting with Advanced Time Series Models and Explainable AI |
| title_full | Climate Impact on Evapotranspiration in the Yellow River Basin: Interpretable Forecasting with Advanced Time Series Models and Explainable AI |
| title_fullStr | Climate Impact on Evapotranspiration in the Yellow River Basin: Interpretable Forecasting with Advanced Time Series Models and Explainable AI |
| title_full_unstemmed | Climate Impact on Evapotranspiration in the Yellow River Basin: Interpretable Forecasting with Advanced Time Series Models and Explainable AI |
| title_short | Climate Impact on Evapotranspiration in the Yellow River Basin: Interpretable Forecasting with Advanced Time Series Models and Explainable AI |
| title_sort | climate impact on evapotranspiration in the yellow river basin interpretable forecasting with advanced time series models and explainable ai |
| topic | evapotranspiration machine learning climate variables explainable AI interpretability Yellow River Basin China |
| url | https://www.mdpi.com/2072-4292/17/1/115 |
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