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Rainfall plays a crucial role in agricultural productivity and water resource management. This research work introduces a comprehensive analysis of rainfall prediction in India using Seasonal Autoregressive Integrated Moving Average (SARIMA) model. It enhances the precision of rainfall prediction through the implementation of the SARIMA model. Traditionally, time-series analysis has been instrumental in capturing the temporal patterns in the climatic data. However, to augment the conventional approach, our study introduces an innovative dimension by incorporating an OpenAI powered data analysis element. This addition transforms our analysis into an interactive and dynamic process, allowing users to pose inquiries and derive insights directly from the dataset. This not only enriches the interpretability of our results but also establishes a more engaging platform for stakeholders, researchers, and policymakers to interact with the complexities of rainfall data. Furthermore, the study provides insights into the key drivers of rainfall variability in India.
Surabhi Byju (Wed,) studied this question.