Multi-timescale drought prediction using new hybrid artificial neural network models

Abstract

In this study, new hybrid artificial neural network (ANN) models were used for predicting the groundwater resource index. The salp swarm algorithm (SSA), particle swarm optimization (PSO), and genetic algorithm (GA) were used to find the weight and bias values of the ANN models. The ANN-PSO, ANN-SSA and ANN-GA models were used to predict the groundwater resource index (GRI)-based drought at different timescales (6, 12, and 24 months) in Yazd plain, Iran. Five input scenarios were used for modeling GRI. The best input scenario was a combination of one-month-lagged GRI, two-month-lagged GRI, three-month-lagged GRI, four-month-lagged GRI, and five-month-lagged GRI, which is known as the fifth input scenario. The outputs of models indicated that the ANN-SSA model with input scenario (5) decreased the mean absolute error (MAE) of ANN-PSO (5) and ANN-GA (5) by 43% and 51%, respectively. Among the hybrid ANN models, ANN-SSA (5), ANN-PSO (5) and ANN-GA (5) outperformed the other hybrid ANN models.