Development of a Rainfall Forecasting System Using AI Techniques

Abstract:-– This paper presents an in-depth analysis of historical meteorological data for eight major cities in India, covering over three decades of daily records from January 1, 1990, to July 20, 2022. The dataset included essential meteorological variables, such as average, minimum, and maximum temperatures, as well as daily precipitation levels. The focus of the research was primarily on precipitation data, which was meticulously preprocessed to serve as the key input for the development of an ARIMA-based forecasting model. Data preprocessing included handling missing values and refining the dataset to ensure its quality and reliability for model training and testing. The ARIMA model was employed to forecast rainfall patterns, demonstrating significant predictive accuracy. This ability to predict future precipitation levels holds immense value for critical applications in agriculture, water resource management, and disaster preparedness, where rainfall forecasts are essential for planning and mitigating risks. Additionally, the study explored seasonal patterns, temperature variability, and precipitation trends across the selected cities, offering insights into regional climate variations. These findings contribute to a broader understanding of how climate trends can influence urban and rural planning efforts in India. The careful preprocessing of the dataset, combined with the application of the ARIMA model, allowed for effective rainfall prediction, underscoring the model's potential to inform decision-making processes in areas heavily impacted by weather variability. The research highlights the importance of accurate climate modeling for improving resilience to climate change and enhancing the management of natural resources

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