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Accurate climate modeling and simulation become essential for predicting and mitigating extreme climate events. Rainfall prediction holds significant importance due to its direct effect on droughts and floods. In this research, we have applied a recent deep learning model called (N-BEATS) in order to predict rainfall in time series data, then we compared the results with state-of-the-art traditional methods, namely LSTM and GRU. Our analytical study compared these algorithms using many performance metrics for multiple window sizes (5, 10 & 20). Our analytical study proved the superiority of the N-BEATS architecture in predicting future rainfall with much faster training time, and more accuracy.

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Modeling and Simulation of Rainfall Prediction in Climate Data Using LSTM, GRU, and N-BEATS: A Comparative Analysis

  • 会议论文

  • 发表日期：

    2025年

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  • Murad A. Yaghi,
  • Huthaifa Al-Omari,
  • Shahed AlNawaiseh

摘要

Accurate climate modeling and simulation become essential for predicting and mitigating extreme climate events. Rainfall prediction holds significant importance due to its direct effect on droughts and floods. In this research, we have applied a recent deep learning model called (N-BEATS) in order to predict rainfall in time series data, then we compared the results with state-of-the-art traditional methods, namely LSTM and GRU. Our analytical study compared these algorithms using many performance metrics for multiple window sizes (5, 10 & 20). Our analytical study proved the superiority of the N-BEATS architecture in predicting future rainfall with much faster training time, and more accuracy.