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In Malaysia, rainfall forecasting is a difficult process due to the complexity of the meteorological process and the involvement of fluctuating weather at certain times and places. This paper presents an Artificial Neural Network (ANN) solution model based on meteorological data for rainfall prediction. The ANN model helps find patterns and relationships hidden within the meteorological data. Forecasting of rainfall meteorological (FRM) data used for testing the ANN model is taken from the Meteorological Department of Malaysia. The result of the developed ANN predictor of rainfall forecasting shows an average accuracy of 99.94% and an average mean squared error (MSE) of 0.0726. This work can help meteorology stations implement the rainfall distribution forecast process.

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Artificial Neural Network for Rainfall Prediction Based on Meteorological Data

  • 会议论文

  • 发表日期：

    2025年

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  • Siti Nur Hidayah Md Kassim,
  • Rozaida Ghazali,
  • Lokman Hakim Ismail,
  • Salama A. Mostafa,
  • Umar Farooq Khattak,
  • Mohd Zainuri Saringat

摘要

In Malaysia, rainfall forecasting is a difficult process due to the complexity of the meteorological process and the involvement of fluctuating weather at certain times and places. This paper presents an Artificial Neural Network (ANN) solution model based on meteorological data for rainfall prediction. The ANN model helps find patterns and relationships hidden within the meteorological data. Forecasting of rainfall meteorological (FRM) data used for testing the ANN model is taken from the Meteorological Department of Malaysia. The result of the developed ANN predictor of rainfall forecasting shows an average accuracy of 99.94% and an average mean squared error (MSE) of 0.0726. This work can help meteorology stations implement the rainfall distribution forecast process.