Standardization Of Rainfall Prediction In Bangladesh Using Machine Learning Approach
Nushrat Jahan Ria, Jannatul Ferdous Ani, Mirajul Islam, Abu Kaisar Mohammad Masum
Abstract
Rainfall has a significant impact on human life in many areas, including natural disasters such as agriculture, droughts, floods, and landslides. Artificial intelligence technologies have a high chance of succeeding in these fields. The goal of this research is to develop the rain prediction model using Machine Learning. This model is built based on a 2,391 records dataset that data has collected from the website named Bangladesh Jatiyo Tottho Batayon. We used a total of five models for the study. Each model has trained with eight input features then it has validated the rainfall predictions. All of the model performance indicated well. But Random Forest predicts the dataset most accurately so that it is the best for this study. Random Forest classifier delivered higher Accuracy, Recall, Precision, and Fl Score.