Automatic Rainfall Prediction System using Machine Learning with Extreme Gradient Boost Algorithm

Daily rainfall forecasting increases agricultural productivity and secures food and water supplies for healthy populations. Data mining and machine learning have been used to predict rainfall using environmental datasets from several countries. Agriculture—the backbone of the economy—is affected by rainfall disparities. Optimising rainfall water use is crucial to addressing the nation's drought and floods issues. This study uses machine learning to identify atmospheric factors that cause precipitation and predict its severity every day. The regional meteorological office in Bahir Dar City, Ethiopia, provided the dataset for testing Multivariate Linear Regression, Random Forest, and Extreme Gradient Boost. The machine learning model was evaluated using mean absolute error and root mean squared error.