Causes of watershed drought analyzed using explainable deep learning: a case study of the Fenhe River Basin
This study predicted daily-scale drought for the Fenhe River (FHR) Basin and applied the explainable artificial intelligence (XAI) method to the model’s prediction results. Daily-scale drought prediction can provide more timely and detailed drought information, while deep learning interpretable meth...
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| Format: | Article |
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Frontiers Media S.A.
2025-06-01
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| Series: | Frontiers in Earth Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/feart.2025.1543497/full |
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| author | Zixuan Chen Zixuan Chen Zixuan Chen Xikun Wei Guojie Wang Yifan Hu Haonan Liu Jinman Zhang Jinman Zhang Jinman Zhang Shuang Zhou Shuang Zhou Shuang Zhou Zengbao Zhao Zengbao Zhao Zengbao Zhao Yushan Liu Yushan Liu Yushan Liu |
| author_facet | Zixuan Chen Zixuan Chen Zixuan Chen Xikun Wei Guojie Wang Yifan Hu Haonan Liu Jinman Zhang Jinman Zhang Jinman Zhang Shuang Zhou Shuang Zhou Shuang Zhou Zengbao Zhao Zengbao Zhao Zengbao Zhao Yushan Liu Yushan Liu Yushan Liu |
| author_sort | Zixuan Chen |
| collection | DOAJ |
| description | This study predicted daily-scale drought for the Fenhe River (FHR) Basin and applied the explainable artificial intelligence (XAI) method to the model’s prediction results. Daily-scale drought prediction can provide more timely and detailed drought information, while deep learning interpretable methods can help understand the impact of different predictors on droughts and improve the credibility of the model. The standardized antecedent precipitation evapotranspiration index (SAPEI) was selected as an index for evaluating drought conditions. Five classical deep learning prediction models, namely, long short-term memory (LSTM), gated recurrent unit (GRU), bidirectional long short-term memory (biLSTM) networks, transformer (TFR), and informer (IFR), were applied in the experiment, and the performance of each model was comprehensively evaluated. The results of the test set show that all models make effective predictions of droughts in the FHR Basin, with a Pearson correlation coefficient (R) higher than 0.75. BiLSTM performs better in short-term prediction, while TFR and IFR are better at long-term prediction. The results of the deep learning interpretable model show that, aside from the strong influence of the SAPEI itself in the prediction process, the mean temperature (TM) has the greatest influence among the auxiliary predictors, followed by precipitation (PRE) and relative humidity (RHU), with potential evapotranspiration (PET) being the weakest. Our work emphasizes the importance of timely warnings of drought and the role of XAI in the development of artificial intelligence. |
| format | Article |
| id | doaj-art-9b5da9e629c84e53a20be8c887aeefe7 |
| institution | OA Journals |
| issn | 2296-6463 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Earth Science |
| spelling | doaj-art-9b5da9e629c84e53a20be8c887aeefe72025-08-20T02:10:23ZengFrontiers Media S.A.Frontiers in Earth Science2296-64632025-06-011310.3389/feart.2025.15434971543497Causes of watershed drought analyzed using explainable deep learning: a case study of the Fenhe River BasinZixuan Chen0Zixuan Chen1Zixuan Chen2Xikun Wei3Guojie Wang4Yifan Hu5Haonan Liu6Jinman Zhang7Jinman Zhang8Jinman Zhang9Shuang Zhou10Shuang Zhou11Shuang Zhou12Zengbao Zhao13Zengbao Zhao14Zengbao Zhao15Yushan Liu16Yushan Liu17Yushan Liu18China Meteorological Administration Xiong’an Atmospheric Boundary Layer Key Laboratory, Xiong’an, ChinaKey Laboratory of Meteorology and Ecological Environment of Hebei Province, Shijiazhuang, ChinaHebei Provincial Meteorology Service Center, Shijiazhuang, ChinaKey Laboratory for Climate Risk and Urban-Rural Smart Governance, School of Geography, Jiangsu Second Normal University, Nanjing, ChinaSchool of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing, ChinaSchool of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing, ChinaSchool of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing, ChinaChina Meteorological Administration Xiong’an Atmospheric Boundary Layer Key Laboratory, Xiong’an, ChinaKey Laboratory of Meteorology and Ecological Environment of Hebei Province, Shijiazhuang, ChinaHebei Provincial Meteorology Service Center, Shijiazhuang, ChinaChina Meteorological Administration Xiong’an Atmospheric Boundary Layer Key Laboratory, Xiong’an, ChinaKey Laboratory of Meteorology and Ecological Environment of Hebei Province, Shijiazhuang, ChinaHebei Provincial Meteorology Service Center, Shijiazhuang, ChinaChina Meteorological Administration Xiong’an Atmospheric Boundary Layer Key Laboratory, Xiong’an, ChinaKey Laboratory of Meteorology and Ecological Environment of Hebei Province, Shijiazhuang, ChinaHebei Provincial Meteorology Service Center, Shijiazhuang, ChinaChina Meteorological Administration Xiong’an Atmospheric Boundary Layer Key Laboratory, Xiong’an, ChinaKey Laboratory of Meteorology and Ecological Environment of Hebei Province, Shijiazhuang, ChinaHebei Provincial Meteorology Service Center, Shijiazhuang, ChinaThis study predicted daily-scale drought for the Fenhe River (FHR) Basin and applied the explainable artificial intelligence (XAI) method to the model’s prediction results. Daily-scale drought prediction can provide more timely and detailed drought information, while deep learning interpretable methods can help understand the impact of different predictors on droughts and improve the credibility of the model. The standardized antecedent precipitation evapotranspiration index (SAPEI) was selected as an index for evaluating drought conditions. Five classical deep learning prediction models, namely, long short-term memory (LSTM), gated recurrent unit (GRU), bidirectional long short-term memory (biLSTM) networks, transformer (TFR), and informer (IFR), were applied in the experiment, and the performance of each model was comprehensively evaluated. The results of the test set show that all models make effective predictions of droughts in the FHR Basin, with a Pearson correlation coefficient (R) higher than 0.75. BiLSTM performs better in short-term prediction, while TFR and IFR are better at long-term prediction. The results of the deep learning interpretable model show that, aside from the strong influence of the SAPEI itself in the prediction process, the mean temperature (TM) has the greatest influence among the auxiliary predictors, followed by precipitation (PRE) and relative humidity (RHU), with potential evapotranspiration (PET) being the weakest. Our work emphasizes the importance of timely warnings of drought and the role of XAI in the development of artificial intelligence.https://www.frontiersin.org/articles/10.3389/feart.2025.1543497/fulldroughtpredictiondaily-scaledeep learningexplainable |
| spellingShingle | Zixuan Chen Zixuan Chen Zixuan Chen Xikun Wei Guojie Wang Yifan Hu Haonan Liu Jinman Zhang Jinman Zhang Jinman Zhang Shuang Zhou Shuang Zhou Shuang Zhou Zengbao Zhao Zengbao Zhao Zengbao Zhao Yushan Liu Yushan Liu Yushan Liu Causes of watershed drought analyzed using explainable deep learning: a case study of the Fenhe River Basin Frontiers in Earth Science drought prediction daily-scale deep learning explainable |
| title | Causes of watershed drought analyzed using explainable deep learning: a case study of the Fenhe River Basin |
| title_full | Causes of watershed drought analyzed using explainable deep learning: a case study of the Fenhe River Basin |
| title_fullStr | Causes of watershed drought analyzed using explainable deep learning: a case study of the Fenhe River Basin |
| title_full_unstemmed | Causes of watershed drought analyzed using explainable deep learning: a case study of the Fenhe River Basin |
| title_short | Causes of watershed drought analyzed using explainable deep learning: a case study of the Fenhe River Basin |
| title_sort | causes of watershed drought analyzed using explainable deep learning a case study of the fenhe river basin |
| topic | drought prediction daily-scale deep learning explainable |
| url | https://www.frontiersin.org/articles/10.3389/feart.2025.1543497/full |
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