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| DOI：10.13522/j.cnki.ggps.2025144 |
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| A machine leaning model for hydrological drought prediction: Model development and application |
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YAO Yuhang, OU Ming, LI Min, FENG Zilong
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1. College of Water Resources Science and Engineering, Yangzhou University, Yangzhou 225000, China;
2. Jilin Province Water Resource and Hydropower Consultative Company, Changchun 130012, China
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| Abstract: |
| 【Objective】Accurately forecasting hydrological drought is critical for proactively mitigating the detrimental impacts of water scarcity. This study proposes a model to predict hydrological drought at catchment scale. 【Method】A hybrid Boruta-CNN-LSTM model was developed, which integrates the Boruta-random forest feature selection algorithm, convolutional neural networks (CNN), and long short-term memory (LSTM) networks. The accuracy of the model was tested against monthly precipitation, evapotranspiration, and soil moisture data measured from 1960—2020 in the Huai River Basin. The accuracy of the LSTM, Boruta-LSTM and Boruta-CNN-LSTM was evaluated based on their root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2) for predicting droughts in the Huai River Basin.【Result】① The Boruta algorithm effectively reduces interference from redundant features. Among the 31 initial influencing factors selected in the models, monthly standardized precipitation index, evapotranspiration, and soil temperature affects predictive ability of the models the most in all sub-regions in the basin. ② The Boruta-CNN-LSTM model is most accurate, with R2=0.853 6, MAE=0.262 and RMSE=0.352. 【Conclusion】The hybrid Boruta-CNN-LSTM model is the most accurate for predicting spatiotemporal variation of hydrological droughts in the Huaihe River Basin. Integrating LSTM, Boruta feature selection, and CNN provides a robust model for predicting drought under different hydrological scenarios. |
| Key words: hydrological drought prediction; boruta feature selection; LSTM; Huaihe River Basin |
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