Trend Analysis of Rainfall for Multi-Purpose Water Resources Projects Using Machine Learning Predictive Model-ARIMA

Rainfall prediction is a major concern in every part of the world especially for the multi-benefit projects like irrigation, agriculture and flood control where efficient water resource management is essential. The purpose of this study is to provide detailed trend analysis of rainfall data using a Machine Learning Technique based called ARIMA Model with a view of forecasting future rainfall trends. Forecasts made through ARIMA is preferred for use for time series causality as it incorporates differencing in its method of analysis a feature that makes it ideal for non-stationary data. Rainfall history of district Saharanpur in India is obtained and analysed to find the important features and patterns of seasonality. ARIMA model used in this context is trained using this data to predict future rainfall and this includes both short term oscillations and long term trends. The model parameters are adjusted through auto tuning mechanisms in order to fit the dataset optimality. Further, to enhance the reliability of the developed predictive model, exhaustive statistical verification is carried out. The model is assessed with a number of measures such as Bayesian information criterion (BIC) and the Akaike Information Criterion (AIC). These metrics give information about the capability of the model to estimate the amount and the distribution of rainfall. The outcome shows that applying ARIMA model could cyclic and annual precipitation and have high accurate predictions. In addition, the Residual Diagnostics test including Ljung-Box test and Jarque-Bera test are conducted for measuring how well the chosen model fits the data by checking whether it has white noise residuals, or whether it is autocorrelated. These validations add fuel to the credibility of the real-life application of the ARIMA based predictions. The study is also validated with coefficient of determination between actual rainfall and predicted rainfall that comes 0.705. The study findings apply well when planning and managing multi-purpose water resource projects. This method offers a broader application planning for future rainfall prediction and water resource solicitation.