Rainfall Prediction using Deep Learning Approach

Abstract

Rainfall prediction is a critical task with far-reaching applications in agriculture, water resource management, disaster preparedness, and climate monitoring. Traditional methods often rely on statistical models that struggle to capture the complex, non-linear patterns inherent in meteorological data. This paper proposes a deep learning-based approach to enhance rainfall prediction accuracy. The study tests how well Long Short-Term Memory (LSTM) networks and Recurrent Neural Networks (RNNs) work with both time-based and space-based meteorological data. LSTM networks are good at handling sequential data, while RNNs are good at handling spatial data. The proposed models are trained and tested on publicly available datasets from meteorological agencies, incorporating features such as temperature, humidity, wind speed, and pressure. The results demonstrate significant improvements in prediction accuracy compared to conventional methods, highlighting the potential of deep learning for reliable rainfall forecasting. These findings contribute to the development of robust, data-driven solutions for addressing climate challenges and ensuring sustainable resource management.