Integrating empirical analysis and deep learning for accurate monsoon prediction in Kerala, India

IF 2.6 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Applied Computing and Geosciences Pub Date : 2024-12-01 DOI:10.1016/j.acags.2024.100211

Yajnaseni Dash, Ajith Abraham

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Abstract

Kerala, a coastal state in India characterized by its humid tropical monsoon climate, is profoundly influenced by the Western Ghats and the Arabian Sea. Kerala receives significant rainfall during both the southwest monsoon (June to September, JJAS) and the northeast monsoon (October to December, OND) seasons. Given the substantial impact of rainfall on the state's economy and livelihoods, accurate precipitation forecasting is of critical importance. Although Kerala's annual rainfall is approximately 2.5 times higher than the national average, the state frequently experiences water scarcity due to rapid runoff into the Arabian Sea. This study builds upon previous research concerning Kerala's rainfall patterns and introduces a novel approach to improving rainfall predictions. Usage of a hybrid model that integrates Empirical Mode Decomposition (EMD) with Detrended Fluctuation Analysis (DFA) and deep Long Short-Term Memory (LSTM) neural networks, demonstrates enhanced precision in forecasting. Thus, by integrating empirical data analysis with advanced deep learning techniques, this research offers a robust framework for predicting rainfall in Kerala, making a significant contribution to the field of climate informatics and providing practical benefits for the region's economy and environmental management.

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