A Review of Rainfall Prediction Using Machine Learning and Deep Learning

Abstract

Exact rainfall forecasting is necessary in disaster management, long-term planning, agriculture, flood control, and water resource planning. In the past decade, there has been rapid development and enhancement in terms of data and computing technologies. The review presents a detailed description of new developments in satellite-based rainfall prediction, hydrological estimation, modelling, and especially changes from traditional methods. Current Artificial Intelligence and Machine Learning systems used in ground-based and satellite datasets consist of popular sources in India. GPM IMERG, Meteorological Department observation data, and CHIRPS datasets support large-scale modelling, but they are still challenged by issues of unequal spatial coverage, time, and variability in climatic regions. It is critical to deal with these problems to develop better prediction models. Machine Learning methods such as Random Forest, Gradient Boosting, Ensemble methods, and Support Vector Machines have been shown to perform well in short-term forecasting of rainfall, primarily because they work with sound input variables and detect hidden regularities. Deep learning models, including LSTM, CNN-based, and hybrid deep network models, also increase predictive ability by modelling detailed, non-linear, and spatiotemporal interdependencies that traditional models do not tend to reflect. This Artificial Intelligence-driven extreme forecasting is particularly beneficial for systems dealing with localised and monsoon rainfall variability.