Volume 11 - Volume 11
Machine Learning Models Applied for Rainfall Prediction
Predicting rainfall is an important step in generating data for climate impact studies. Rainfall
predictions are a key process for providing climate impact assessments with inputs. A consistent
rainfall pattern is typically good for normal plants; nevertheless, too much or too little rainfall can
be disastrous to crops, even deadly. Drought can damage plants and lead to erosion, while heavy
rainfall can encourage the growth of destructive fungi. Machine Learning (ML) can be helpful in
overcoming such issues; for example, ML can be used to predict rainfall and apply it to foresee crop
health and yield. Predictive analysis is a subset of data mining that forecasts future probabilities and
patterns. Various sectors like the Agricultural Produce Markets Committee (APMC), Kisaan call
centre, etc., can use proposed method, enabling the sector and farmers to obtain information on
future precipitation, crop yields and crop health.
Author's Name: Gujanatti Rudrappa, Nataraj Vijapur, Rajesh Pattar, Ravi Rathod, Rashmi Kulkarni, Vudu Sree Chandana and Sateesh N. Hosmane
Volume: Volume 11
Issues: Volume 11
Keywords: Rainfall, Rainfall Prediction, Machine Learning, Predictive Analysis.