Enhanced prediction of karst spring discharge using a hybrid LSTM-XGBoost model optimized with grid search

Abstract

Globally, intensifying droughts taxed water supplies, particularly in karst areas where it is difficult to predict spring discharge due to complex hydrology. Data-driven models represent a viable alternative, with the significance of karst aquifers to freshwater production. To enhance the accuracy of spring discharge prediction, this study introduces a new LSTM-XGBoost hybrid model for more accurate karst spring discharge prediction in Chaharmahal Bakhtiari Province, Iran. The hybrid model exploits the benefits of LSTM in capturing temporal dependency and the strength of XGBoost in modeling nonlinear relationships, and Grid Search is utilized for tuning hyperparameters. The performance of the LSTM-XGBoost model is compared with the optimized ML models. The study utilizes a dataset of 3,266 day, month, and spring discharge records of the Dehghara Springs. The results depict the excellence of the suggested LSTM-XGBoost hybrid model with the highest test R² = 0.8798, Explained Variance (EV) = 0.8857, and the lowest error metrics (MAE = 0.3355, RMSE = 0.5795, MAPE = 21.84%). The hybrid model outperforms both the baseline traditional and Deep Learning (DL). Feature importance analysis reveals that seasonal factors, particularly the month with an importance score of 0.919, have a significantly greater impact on spring discharge than daily variations. The proposed LSTM-XGBoost hybrid model provides a reliable and accurate tool for karst spring discharge prediction, offering valuable insights for water resource management in regions affected by climate change and increasing water demand.