A Comparative Analysis of ARIMA and other Statistical Techniques in Rainfall Forecasting: A Case Study in Kolkata (KMC), West Bengal

Rainfall forecasting in urban areas is a significant consideration for city planners due to its connection with urban water management. In this study, the ARIMA (auto-regressive integrated moving average) model, as well as several regression approaches such as simple linear and second to sixth-degree polynomial regression equations, have been used to forecast the annual rainfall based on 120 years of monthly and annual rainfall from 1901 to 2020 in Kolkata Municipal Corporation (KMC), West Bengal. This study compares the performance of ARIMA and other regression techniques in forecasting rainfall using the metrics of R-squared and root mean square error (RMSE). The ARIMA model has been implemented using machine learning techniques in the Python programming language, while additional regression equations have been computed and analyzed using Microsoft Excel 2019. In order to employ the ARIMA model, all assumptions were assessed, and the optimal model order was established using the import auto-Arima package from the pmdarima.arima library. The stepwise model.aic function yielded 0,1,1 as the most suitable order for the model. The findings indicate that, out of all the regression methods employed for rainfall prediction, the fifth-degree polynomial equation exhibits the lowest root mean square error (RMSE), establishing it as the most effective model for rainfall forecasting in this study.