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Drought is one of the main natural factors influencing different aspects of human life. Over the decades, intelligent techniques have proven to be very capable of modeling and predicting nonlinear and dynamic time series. Therefore, the present study aims to predict drought by using and comparing neuro-fuzzy adaptive inference systems (ANFIS), artificial neural network of multilayered perceptron (ANN-MLP) and the support vector model (SVR). For this purpose, the precipitation data obtained from the Ain Bittit station were used for a statistical period of 34 years. In addition, the short-term (3 and 6 months) and long-term (9 and 12 months) time scales were calculated using the standardized precipitation index (SPI). Then, depending on the results of the calculations, the period 1979-2000 was selected as a control group and the period 2003-2012 was selected as an experimental group. In order to predict the SPI for the (t + 1) period, SPI values, precipitation from previous months were used. The results indicated that, in the majority of time scales, the ANFIS model gives SPI values and predictive dryness more accurately than the SVR,

international journal of intelligent engineering and systems

Application of Several Artificial Intelligence Models for Forecasting Meteorological Drought Using the Standardized Precipitation Index in the Saïss Plain (Northern Morocco)