Conduct an In-Depth Investigation into the Utilization of Satellite Data and Machine Learning Techniques for the Prediction of Rainfall

This research aimed to explore the viability of utilizing machine learning algorithms for predicting rainfall. The study involved the examination of four distinct models: linear regression, multilayer perceptron, k-nearest neighbors, and decision tree. These models underwent training using a historical dataset containing recorded rainfall and were subsequently assessed against a separate holdout dataset. The findings revealed that the linear regression model demonstrated exceptional performance, achieving an impressive R2 score, an exceptionally low RMSE, and a modest MAE. Additionally, both the MLP and KNN models displayed commendable accuracy respectively. This study underscores the potential effectiveness of employing linear regression for rainfall forecasting. In forthcoming research endeavours, this method could be harnessed to estimate rainfall in localized regions, contributing to more precise predictions.