Predicting Rainfall Using an Artificial Neural Network-Based Model

Rainfall estimation poses a significant and intricate environmental challenge in today's context. Achieving accurate forecasts often necessitates the deployment of sophisticated machine models and simulations because weather patterns and rainfall dynamics are typically highly nonlinear and constantly changing. In India, where agriculture serves as the cornerstone of the economy, special emphasis is placed on the relationship between agricultural productivity and rainfall. Farmers rely on rainfall forecasts to gauge potential crop yields. Precipitation forecasting leverages scientific and technological advancements to assess atmospheric conditions. Accurate monitoring of rainfall is pivotal for optimal crop growth, efficient water resource management, and strategic planning of water systems. To emulate the behavior of such intricate nonlinear systems, artificial neural networks (ANN s) have proven to be a valuable tool. Over the years, numerous researchers in this field have effectively harnessed ANN s. This research highlights several approaches employed by various researchers to predict rainfall using ANN s. This proposal underscores that forecasts based on ANN s outperform those derived from conventional mathematical and numerical methods, offering greater precision in rainfall predictions.