Precipitation prediction in Bangladesh using machine learning approaches

In the assessment of different hydrological activities, the prediction of rainfall is essential. As agriculture is critical to survival in Bangladesh, rainfall or precipitation is most important. This study shows how a machine learning approach can be used to make a reliable model for predicting rain. This way, people can know when rain is coming and take the steps they need to protect their crops. Many techniques have been applied so far to predict rainfall. But machine learning algorithms can provide more accuracy in this case. Nine machine learning algorithms have been used to find a good model that can be used to predict rain in Bangladesh. The prediction models were evaluated by dint of evaluation metrics such as coefficient of determination (R2), root mean squared error (RMSE), and mean absolute error (MAE). Among nine algorithms and eight models, the model H including all meteorological exogenous inputs with gradient boosting regressor algorithm led to the best predictions (R-2 = 0.78, RMSE = 134, MAE = 92) for Sylhet division. The model G excluding wind speed with gradient boosting regressor algorithm shows the best predictions (R-2 = 0.76, RMSE = 147, MAE = 89) for both Chittagong and Rangpur divisions.