Rainfall Prediction using Deep Learning Algorithms

Rainfall prediction is a crucial aspect of weather forecasting, agricultural planning, and disaster management. Traditional methods for rainfall prediction often face challenges in accurately capturing the complex and nonlinear patterns inherent in meteorological data. Our project presents a promising approach for rainfall prediction using deep learning, specifically through the application of Artificial Neural Networks. In recent years, deep learning techniques have shown promising results in various fields, including weather prediction. The proposed model leverages the capabilities of ANN to learn intricate relationships within historical meteorological datasets. The input features include various meteorological parameters such as temperature, humidity, wind speed, and atmospheric pressure. The ANN is designed with multiple layers, allowing it to automatically extract relevant features and patterns from the input data. The proposed model shows considerable potential in enhancing the accuracy and efficiency of rainfall forecasting, thereby contributing to better- informed decision-making in weather-dependent domains.