Rainfall forecasting in arid regions using an ensemble of artificial neural networks

Abstract

Water rainfall prediction is one of the most difficult tasks in hydrology because rainfall events are extremely random. This research presents a comparative analysis of different models that predict rainfall in an arid region. The forecasting models comprise the feed-forward, general regression, recurrent, cascade, and Elman neural networks. The performance of the aforementioned models is assessed using three evaluation metrics, namely the correlation coefficient, coefficient of efficiency, and Willmott's index of agreement. Furthermore, the statistical significance of the neural network models is evaluated using the Wilcoxon-Mann-Whitney test. Finally, the correspondence of the neural network model results compared to the observations is examined using the Taylor diagram. The findings reveal that the general neural network exhibits the best performance compared to other models using the tropical rainfall measuring mission dataset at Suez city in Egypt. The Egyptian water municipality is intended to benefit from the proposed model in monthly rainfall forecasting in this arid region. The precise modeling of rainfall is vital for managing water resources such as food production, water allocation, and drought management.