Assessment of Statistical Models for Rainfall Forecasting Using Machine Learning Technique

Document Type : Regular Article


1 School of Computing, SASTRA Deemed to be University, Thanjavur, India

2 School of Civil Engineering, SASTRA Deemed to be University, Thanjavur, India


As heavy rainfall can lead to several catastrophes; the prediction of rainfall is vital. The forecast encourages individuals to take appropriate steps and should be reasonable in the forecast. Agriculture is the most important factor in ensuring a person's survival. The most crucial aspect of agriculture is rainfall. Predicting rain has been a big issue in recent years. Rainfall forecasting raises people's awareness and allows them to plan ahead of time to preserve their crops from the elements. To predict rainfall, many methods have been developed. Instant comparisons between past weather forecasts and observations can be processed using machine learning. Weather models can better account for prediction flaws, such as overestimated rainfall, with the help of machine learning, and create more accurate predictions. Thanjavur Station rainfall data for the period of 17 years from 2000 to 2016 is used to study the accuracy of rainfall forecasting. To get the most accurate prediction model, three prediction models ARIMA (Auto-Regression Integrated with Moving Average Model), ETS (Error Trend Seasonality Model) and Holt-Winters (HW) were compared using R package. The findings show that the model of HW and ETS performs well compared to models of ARIMA. Performance criteria such as Akaike Information Criteria (AIC) and Root Mean Square Error (RMSE) have been used to identify the best forecasting model for Thanjavur station.


Main Subjects

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