Application of neural modeling and the SPI index for the prediction of weather drought in the Saïss Plain (Northern Morocco)

Abstract

This contribution will verify the effectiveness of formal neural networks for predicting drought in a semiarid region using a hybrid model of formal neural networks (ANN-MLP) and the standardized precipitation index ( SPI). Three types of models have been optimized to achieve this objective. A database consisting of SPI values, rain, temperature and potential evapotranspiration (PET) at the monthly time step was used as input for these models. These data have been standardized between 0 and 1 and subdivided into two blocks: a first block composed of 2/3 of the data for learning and a second block composed of 1/3 of the data for the test and the validation of the models. These models have been optimized with supervised learning. The activation function chosen is the logistic variant of the type sigmoid. The mean square error (RMSE), the correlation coefficient (R), the criterion of Nash-Sutcliffe (Nash) and the absolute mean error (MAE) were used to test the performance of these models. The results obtained show that the 3rd model is the most efficient. The application of neural networks for the estimation of the dryness of the Saïss Plain yielded quite good results. Indeed, the coefficients of correlation between the predicted and the measured values range from 0.63 to 0.97. It is therefore noted that the performances obtained are relatively good and could be improved by using a larger database.