Harnessing Historical Weather Data with Machine Learning for Rainfall Forecasting in Bangladesh: A Time Series Analysis

This study aims to explore the integration of historical weather data with machine learning methods for better rainfall forecasting in Bangladesh. It has the potential to identify rainfall early by observing their prediction on preventing natural disasters. Weather forecasting, a process that is paramount to protecting human lives and property as well as general welfare, at times faces challenges due mainly in part to manual computations. These calculations are likely error-ridden because of the vastness and complexity of data needing to be analyzed. This research combines weather forecasting with machine learning techniques to improve the accuracy and precision of predictions. The main goal is to develop an accurate prediction system of rainfall forecasting in Bangladesh for the future. The model training data set is from historical meteorologic records of Bangladesh during the period 1901–2015. After neatly hyper-tuning the Random Forest model, we achieve some impressive RMSE metrics. The results are compared with a baseline model, Randomized Search CV, using different evaluation metrics such as Mean Absolute Error (MAE), Mean Squared Logarithmic Error (MSLE), and Root-mean-squared-log-error (RMSLE), etc., and the observations of significantly better performance is observable. Machine learning in weather forecasting has become an essential tool that can save lives and billions of dollars in property. An extensive analysis of machine learning techniques was performed in the domain of rainfall prediction. Among all the tried models, the Random Forest model gave perfect predictions, as noted by its Mean Squared Error (MSE) of 12245.52 and absolute error value of 64.25. Furthermore, there was excellent precision (0.95) and recall (0.92), respectively, for the classification of wet days, corresponding to a total accuracy score of 91%. Based on the above result, we can conclude that the Random Forest model is both robust and promising for rainfall prediction problems. Various models such as Linear Regression, Support Vector Regressor (SVR), Decision Tree, K-Nearest Neighbors (KNN), AdaBoost, XG Boost, Ridge, Linear SVR and MLP Regressor showed variations in prediction accuracies and errors. Nevertheless, the Random Forest came out as a better choice in this case, showing its superiority. The plan is to expand the time period of the dataset along with more advance machine learning models to predict weather forecast in future.