Articles | Volume 23, issue 4
https://doi.org/10.5194/nhess-23-1453-2023
© Author(s) 2023. 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-23-1453-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Hydrological drought forecasting under a changing environment in the Luanhe River basin
Min Li
CORRESPONDING AUTHOR
College of Hydraulic Science and Engineering, Yangzhou University, Yangzhou, 225000, China
State Key Laboratory of Hydraulic Engineering Simulation and Safety, Tianjin University, Tianjin, 300072, China
Mingfeng Zhang
College of Hydraulic Science and Engineering, Yangzhou University, Yangzhou, 225000, China
Runxiang Cao
College of Water Resources, North China University of Water Resources and Electric Power, Zhengzhou, 450046, China
Yidi Sun
College of Hydraulic Science and Engineering, Yangzhou University, Yangzhou, 225000, China
Xiyuan Deng
CORRESPONDING AUTHOR
Nanjing Hydraulic Research Institute, Nanjing, 210029, China
State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing, 210029, China
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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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This study examines the risk of possible water shortages in the Huaihe River Basin and explores whether dry periods can be predicted more reliably. Using decades of daily environmental records, we built a model that separates complex signals into simpler parts and learns their patterns. It forecasts dry conditions more accurately than earlier methods and performs well across regions and time spans. The findings can help the basin prepare for potential water shortages and support better planning.
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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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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.
It is an important disaster reduction strategy to forecast hydrological drought. In order to...
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