Rainfall Prediction using Machine Learning Algorithms: A Comparative Analysis Approach

Authors : Yeluri Kiranmai; Thota Kedhara Varshini; Tarun Kumar Kodali; Venigalla Sai Teja; N. Md. Jubair Basha

Volume/Issue : Volume 7 - 2022, Issue 6 - June

Google Scholar : https://bit.ly/3IIfn9N

Scribd : https://bit.ly/38TUEno

DOI : https://doi.org/10.5281/zenodo.6618458

Rainfall prognosis is one of the most important technique to anticipate the climatic conditions across globe. Heavy Rainfall is a major issue for meteorological department as it is closely connected to the economy and life of human beings. It is a source for natural disasters like flood and drought which are encountered by people across the globe every year. Prompt and explicit predictions can assist proactively reduce human and financial loss. The paper introduces a Rainfall prediction model using Multi Linear Regression, Random Forest Regression and KNN Regression. The given input data is having numerous inputs and to anticipate the rainfall in more specific. The Mean Squared Error (MSE), accuracy score of training and testing, R2_Score for the parameters used to verify the proposed model. The comparative outcome among various algorithms is also presented in this paper.

Keywords : Rainfall, Prediction, Machine Learning.


Paper Submission Last Date
31 - March - 2024

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