Evaluation of Machine Learning Techniques for Hydrological Drought Modeling: A Case Study of the Wadi Ouahrane Basin in Algeria

Forecasting meteorological and hydrological drought using standardized metrics of rainfall and runoff (SPI/SRI) is critical for the long-term planning and management of water resources at the global and regional levels. In this study, various machine learning (ML) techniques including four methods (i.e., ANN, ANFIS, SVM, and DT) were utilized to construct hydrological drought forecasting models in the Wadi Ouahrane basin in the northern part of Algeria. The performance of ML models was assessed using evaluation criteria, including RMSE, MAE, NSE, and R-2. The results showed that all the ML models accurately predicted hydrological drought, while the SVM model outperformed the other ML models, with the average RMSE = 0.28, MAE = 0.19, NSE = 0.86, and R-2 = 0.90. The coefficient of determination of SVM was 0.95 for predicting SRI at the 12-months timescale; as the timescale moves from higher to lower (12 months to 3 months), R-2 starts decreasing.

Automated summary

This study utilized various machine learning techniques to construct hydrological drought forecasting models in the Wadi Ouahrane basin in northern Algeria, and found that the SVM model outperformed other models in predicting hydrological drought.