Prediction of agricultural drought index in a hot and dry climate using advanced hybrid machine learning

Abstract

Drought monitoring and forecasting are essential for efficient water resources management. The present research aims to provide a reliable prediction of the effective Reconnaissance Drought Index (eRDI) based on seven evaporation stations in the southern Baluchestan sub-basin of Iran. To achieve this purpose, artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and support vector regression (SVR) machine learning methods are used and combined with the marine predator optimization algorithm (MPA) to enhance efficiency. Drought monitoring and forecasting have been performed on time scales of 1-, 3-, and 6-months intervals. The results demonstrated the superiority of the ANFIS-MPA algorithm over the SVR-MPA and ANN-MPA approaches. In addition, as the time scale increased, the accuracy of all models improved. The best results were for the eRDI 6-month at Kajdar Sarbaz station by ANFIS-MPA (MAE = 0.33, NSE = 0.83, R2 = 0.99), SVR-MPA (MAE = 0.36, NSE = 0.78, R2 = 0.85) and ANN-MPA (MAE = 0.37, NSE = 0.72, R2 = 0.83).

Keywords

• Drought Index
• Marine Predators Algorithm (MPA)
• Adaptive neuro-fuzzy inference system (ANFIS)
• Artificial neural network (ANN)
• Support vector regression (SVR)
• Baluchestan