Random Forest Regressor Model for Rainfall Prediction

Rainfall is cause of natural calamities like flood. Rainfall prediction is a major problem everywhere. Rainfall is a factor that affects several activities like agriculture, production, construction, generation of power, and tourism. Machine Learning is very useful nowadays to predict rainfall which is basically needed for today. In the current scenario, accurate rainfall prediction is a difficult task. Rainfall Prediction is an attempt to predict the rainfall for a region. Different algorithms for predicting rainfall is applied in this paper which includes classifiers well as a Regressor like Random Forest Regressor, Random Forest Classifier, Linear Regression, Support vector regressor (SVR), and Decision Tree Algorithm. Effective rainfall plays a huge role in the growth rate of crops in agriculture during crop growth. Applying Machine learning will calculate the amount of predictable rainfall which will be used for water resources, crop productivity and pre-planning of water usage. The proposed machine learning (ML) based approach will help to predict the amount of rainfall every year, with the help of a machine learning algorithm, the comparison of different algorithm results is also given in the paper. It is evident from the application of a different algorithm that the Random forest regressor outperforms over other algorithms with an accuracy score of 89%.