Advancing Rainfall Prediction in Pakistan: A Fusion of Machine Learning and Time Series Forecasting Models

Abstract

This study brings about an innovative approach as rainfall forecast predominantly Boolean calculation is reviewed overall for 6 major cities of Pakistan for the time span of last quarter century. The study is aimed at improving the accuracy and the reliability of rainfall forecasting by making use of the capabilities of Artificial Intelligence (AI). This research investigates the efficacy of various machine learning models, including Naive Bayes, Logistic Regression, Support Vector Machines (SVM), K Nearest Neighbors (KNN), Gradient Boosting, and time series forecasting model ARIMA (Autoregressive Integrated Moving Average), for rainfall prediction. The model gets trained using 20 years of pakistan historical data performance where improving precision is the objective. With a highly scientific evaluation against real-world datasets, the new approach shows remarkable improvements in the accuracy of rainfall prediction compared to other conventional methods. Machine learning model KNN and time series forecasting model ARIM provide the good result in term of higher accuracy and lower RMSE. This results in the combination of environmental data science for innovation of the meteorological forecasting of this polarized area where the judgments are not so accurate.