A Review on Ensemble Model-Based Rainfall Prediction Techniques

The most common catastrophes are rains, which cause fatalities and the destruction of livelihoods, both of which have an impact on the economics of the countries. Several research and innovative methods have been developed to effectively create Rainfall Forecasting Systems (RFS). The authors observed and discussed the current trend in rainfall prediction towards data-driven techniques. For predicting tasks, the ensemble Machine Learning (ML) and Deep Learning (DL)-based models that were trained using historical data on climatic parameters are becoming more and more valuable. The main goal of this work is to study ensemble ML and DL-based algorithms to illustrate recent advances in rainfall forecasting. The authors examined several well-known ensemble machine learning and deep learning methods for rainfall forecasting, which a variety of experts may utilize to create their solutions.