Monthly rainfall forecasting using deep learning methods in big databases: a case study of northwestern Iran

This investigation introduces a monthly rainfall forecast model employing Deep Belief Network (DBN) techniques for rainfall prediction in northwestern Iran. Meteorological, climatic, and topographic data were used to model the rainfall. Since the parameter combination directly impacts the accuracy of results, an optimized combination of input parameters was selected through the Genetic Algorithm (GA). Moreover, considering the imbalanced nature of the dataset, Random Oversampling and Random Undersampling were exploited to increase the model's performance. The predictive capabilities of the suggested model were compared with those of a Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM) methods. The findings revealed that the MLP achieved an accuracy of 0.67, while the DBN exhibited a higher accuracy of 0.71. The comparison proved the superiority of the developed model based on DBN. The result also revealed that the Random Undersampling technique successfully identified classes with less distribution.