Leveraging SARIMAX for Accurate Rainfall Forecasting in India

Dinesh Kumar1 Dr. Pankaj Richariya2
  • 1 Research Scholar, Department of Computer Science Engineering, Bhopal Institute of Technology and Sciences
  • 2 Hod(CSE), Research Scholar, Department of Computer Science Engineering, Bhopal Institute of Technology and Sciences.

Abstract

This research uses cutting-edge modelling approaches to give a thorough analysis of rainfall forecast in India. The main goals are to increase forecast accuracy and comprehend the intricate dynamics of rainfall patterns, which are important for many industries like infrastructure development, water resource management, and agriculture. The research assesses many predictive models, such as SARIMAX, Decision Tree, Support Vector Machine, ARIMA, Exponential Smoothing, and Exponential Smoothing, with a focus on SARIMAX because of its capacity to include exogenous variables and seasonal trends. The results show that SARIMAX performs better than the other models, with an amazing R-squared (R²) score of 0.99 and an extremely low Mean Absolute Error (MAE) of 0.044. The innovative aspect of this research is the use of SARIMAX, which provides insightful information and reliable approaches for rainfall forecast. These findings have significance for resource management and decision-making processes within the meteorological context of India.

PUBLISHED
2024-06-03
ISSUE
Vol 3 Issue 2

Journal Details

   ISSN : 2583 – 7117

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