A hybrid model based on Boruta feature selection and neural network for forecasting hydrological drought

Abstract. Accurate hydrological drought prediction is vital for water management. This study proposes a hybrid model combining Boruta feature selection, convolutional neural network (CNN), and bidirectional long short-term memory (BiLSTM) methods, to predict hydrological drought in the Huaihe River Basin of China. The Boruta algorithm selected key predictors from 31 potential drought-influencing factors. By comparing the established model Boruta-CNN-BiLSTM with other models, including Boruta-CNN-LSTM, Boruta-CNN-XGBoost, Boruta-BiLSTM, Boruta-LSTM, and Boruta-XGBoost, the results show Boruta significantly enhances all models. The Boruta-CNN-BiLSTM model has achieved the highest accuracy across 28 basin grid regions, exhibiting the largest performance gains. Furthermore, the prediction performance of the model is mainly influenced by factors such as precipitation, volumetric soil water (0–7 cm), volumetric soil water (7–28 cm) and surface net solar radiation. The model's prediction performance is most affected by precipitation, followed by volumetric soil water (0–7 cm), volumetric soil water (7–28 cm), and surface net solar radiation has the least impact. It provides enhanced support for basin-scale drought risk assessment and water resources management.