Machine Learning Models Applied for Rainfall Prediction

Abstract

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.

Prognostication of Weather Patterns using Meteorological Data and ML Techniques

“…The Random Forest Regressor shows the impressive in regression, while the Gradient Boosting Tree classifier excels in classification. The model is noted for its user-friendliness and efficiency, proving reliable for predicting rainfall which is crucial for agriculture, aviation, and water management [3,4,5].…”

Performance Evaluation of Machine Learning Regression Models for Rainfall Prediction

“…Ensemble learning integrates different ML classifiers for predicting rainfall. Rudrappa et al (2021) predicted rainfall using several predictor variables which included humidity, pressure, wind speed, and wind direction. In comparison to other models, XGboost acquired greater accuracy, precision, recall, and F1 score.…”