Rainfall Prediction Using Deep Learning Algorithms

Abstract

Water scarcity presents a significant challenge for many countries. Given that rainfall serves as the primary source of drinking water, irrigation, and various other purposes, the accuracy of rainfall prediction holds utmost importance for decision-makers. Climate change has exacerbated the unpredictability of rainfall patterns, posing challenges to sustainable water resource management. Artificial Intelligence (AI) techniques, especially deep neural networks, have demonstrated effectiveness in predicting rainfall patterns and enhancing water resource management. This study involved reprocessing data obtained from the Jordanian Ministry of Water and Irrigation Jordan Valley Authority to create a dataset. The dataset was then evaluated using Long Short-Term Memory (LSTM), a type of Recurrent Neural Network (RNN), and Support Vector Machine (SVM) models for rainfall prediction. Evaluation metrics such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared were employed, revealing the reliability of the forecasting models.