Meteorological Time-Series Forecasting: A Deep Recurrent Neural Network Framework Utilizing Long Short-Term Memory (LSTM) for Rainfall Prediction

Authors

  • VANGARA SUDEEKSHANA,Dr. G.V. Ramesh Babu

Keywords:

Rainfall Prediction, Weather Forecasting, Artificial Intelligence, Machine Learning, Long Short-Term Memory (LSTM).

Abstract

Accurate rainfall prediction and weather forecasting are essential for agriculture, disaster management,water resource planning, and environmental sustainability. Traditional forecasting techniques often struggle to capture complex atmospheric patterns and nonlinear relationships present in meteorologicaldata. This research proposes an intelligent Rainfall Prediction

References

Seneviratne, S.I.; Zhang, X.; Adnan, M.; Badi, W.; Dereczynski, C.; Di Luca, A.; Ghosh, S.; Iskandar, I.; Kossin, J.; Lewis, S.; et al. Weather and

Climate Extreme Events in a Changing Climate. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change; Cambridge University Press: Cambridge, UK; New York, NY, USA, 2021. [Google Scholar]

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Published

2026-06-14

How to Cite

VANGARA SUDEEKSHANA,Dr. G.V. Ramesh Babu. (2026). Meteorological Time-Series Forecasting: A Deep Recurrent Neural Network Framework Utilizing Long Short-Term Memory (LSTM) for Rainfall Prediction. Journal of Computational Analysis and Applications (JoCAAA), 35(6), 34–41. Retrieved from https://www.eudoxuspress.com/index.php/pub/article/view/5567

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Section

Articles