Articles | Volume 25, issue 11
https://doi.org/10.5194/nhess-25-4299-2025
https://doi.org/10.5194/nhess-25-4299-2025
Research article
 | 
04 Nov 2025
Research article |  | 04 Nov 2025

Hydrological drought prediction and its influencing features analysis based on a machine learning model

Min Li, Yuhang Yao, Zilong Feng, and Ming Ou

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Cited articles

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Bachmair, S., Svensson, C., Hannaford, J., Barker, L. J., and Stahl, K.: A quantitative analysis to objectively appraise drought indicators and model drought impacts, Hydrol. Earth Syst. Sci., 20, 2589–2609, https://doi.org/10.5194/hess-20-2589-2016, 2016. 
Barnwal, A., Cho, H., and Hocking, T.: Survival Regression with Accelerated Failure Time Model in XGBoost, J. Comput. Graph. Stat., 31, 1292–1302, https://doi.org/10.1080/10618600.2022.2067548, 2022. 
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Choi, H.-S., Kim, S., Oh, J. E., Yoon, J. E., Park, J. A., Yun, C.-H., and Yoon, S.: XGBoost-Based Instantaneous Drowsiness Detection Framework Using Multitaper Spectral Information of Electroencephalography, Washington, DC, USA, https://doi.org/10.1145/3233547.3233567, 2018. 
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This study proposes an innovative method for predicting drought in the Huaihe River Basin of China using advanced machine learning and interpretable artificial intelligence techniques. By analyzing more than 50 years of data, the model successfully predicted four drought categories with an accuracy of 79.9 %. It used explanatory methods to analyze the contribution of different drought influencing factors, providing key insights for early warning systems and water resources planning.
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