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This article presents nonlinear autoregressive neural network for time-series prediction of seasonal monsoon rainfall in India during the month of July. Real-time data of past 119 years has been considered for the training of proposed model using three different machine learning algorithms namely Levenberg–Marquardt (LM), Scaled-Conjugate Gradient (SCG) and Resilient Backpropagation (RP). The performance of these algorithms has been compared in terms of mean absolute error (MAE), root mean squared error (RMSE) and mean absolute percentage error (MAPE). The findings highlight superior performance of LM-algorithm compared to other two machine learning algorithms.

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Machine Learning Applications for Seasonal Monsoon Rainfall Prediction in India

  • 会议论文

  • 发表日期：

    2025年

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  • Tarun Kumar Dhiman,
  • Ashwani Kharola,
  • Paritosh Mishra,
  • Amir Shaikh,
  • Sankula Madhava

摘要

This article presents nonlinear autoregressive neural network for time-series prediction of seasonal monsoon rainfall in India during the month of July. Real-time data of past 119 years has been considered for the training of proposed model using three different machine learning algorithms namely Levenberg–Marquardt (LM), Scaled-Conjugate Gradient (SCG) and Resilient Backpropagation (RP). The performance of these algorithms has been compared in terms of mean absolute error (MAE), root mean squared error (RMSE) and mean absolute percentage error (MAPE). The findings highlight superior performance of LM-algorithm compared to other two machine learning algorithms.