Articles | Volume 25, issue 11
https://doi.org/10.5194/nhess-25-4299-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-25-4299-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Hydrological drought prediction and its influencing features analysis based on a machine learning model
Min Li
CORRESPONDING AUTHOR
College of Hydraulic Science and Engineering, Yangzhou University, Yangzhou, 225000, China
State Key Laboratory of Water Disaster Prevention, 210000 Nanjing, China
Yuhang Yao
College of Hydraulic Science and Engineering, Yangzhou University, Yangzhou, 225000, China
Zilong Feng
JiLin Province Water Resource and Hydropower Consultative Company of P.R CHINA, Changchun, 130012, China
College of Hydraulic Science and Engineering, Yangzhou University, Yangzhou, 225000, China
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Min Li, Zilong Feng, Mingfeng Zhang, Lijie Shi, and Yuhang Yao
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2024-174, https://doi.org/10.5194/nhess-2024-174, 2025
Revised manuscript accepted for NHESS
Short summary
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The purpose of this study is to analyse the effects of climate change and watershed characteristics on drought transmission. Based on the GAMLSS framework and the Copula model, the probabilities and thresholds for the spread of drought in different seasons are calculated without the influence of climate factors. The results show that the spatio-temporal changes in the spread of drought are influenced by climate change and watershed characteristics.
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It is an important disaster reduction strategy to forecast hydrological drought. In order to analyse the impact of human activities on hydrological drought, we constructed the human activity factor based on the method of restoration. With the increase of human index (HI) value, hydrological droughts tend to transition to more severe droughts. The conditional distribution model involving of human activity factor can further improve the forecasting accuracy of drought in the Luanhe River basin.
Min Li, Zilong Feng, Mingfeng Zhang, Lijie Shi, and Yuhang Yao
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2024-174, https://doi.org/10.5194/nhess-2024-174, 2025
Revised manuscript accepted for NHESS
Short summary
Short summary
The purpose of this study is to analyse the effects of climate change and watershed characteristics on drought transmission. Based on the GAMLSS framework and the Copula model, the probabilities and thresholds for the spread of drought in different seasons are calculated without the influence of climate factors. The results show that the spatio-temporal changes in the spread of drought are influenced by climate change and watershed characteristics.
Min Li, Mingfeng Zhang, Runxiang Cao, Yidi Sun, and Xiyuan Deng
Nat. Hazards Earth Syst. Sci., 23, 1453–1464, https://doi.org/10.5194/nhess-23-1453-2023, https://doi.org/10.5194/nhess-23-1453-2023, 2023
Short summary
Short summary
It is an important disaster reduction strategy to forecast hydrological drought. In order to analyse the impact of human activities on hydrological drought, we constructed the human activity factor based on the method of restoration. With the increase of human index (HI) value, hydrological droughts tend to transition to more severe droughts. The conditional distribution model involving of human activity factor can further improve the forecasting accuracy of drought in the Luanhe River basin.
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Short summary
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.
This study proposes an innovative method for predicting drought in the Huaihe River Basin of...
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