A Novel Deep Learning Framework For Rainfall Prediction In Weather Forecasting

Abstract

Precipitation data may be used to evaluate important water supplies, drainage, ecosystems, and hydrology. For these purposes, data-driven model predictions using deep learning algorithms are promising. Rainfall is one of the most important sources of freshwater for all living things on the planet. The rainfall prediction model demonstrates how various climatological variables influence rainfall amounts. By allowing self-learning data labels, Deep Learning has recently allowed the development of a data-driven model for a time series dataset. As data availability and computational power have increased, deep neural network architectures have made significant progress in predicting problems across multiple domains. As data availability and computational power have increased in recent years, deep neural network frameworks have made significant progress in predicting problems across multiple domains. In this paper, a rainfall prediction system based on deep learning convolutional neural networks is proposed to provide insights into changes in environment and atmosphere variables such as precipitation, temperature, and humidity.