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Modeling Average Rainfall in Nigeria With Artificial Neural Network (ANN) Models and Seasonal Autoregressive Integrated Moving Average (SARIMA) Models

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  • Ikpang Nkereuwem Ikpang
  • Ekom-obong Jackson Okon

Abstract

Rainfall prediction is one of the most essential and challenging operational obligations undertaken by meteorological services globally. In this article we conduct a comparative study between the ANN models and the traditional SARIMA models to show the most suitable model for predicting rainfall in Nigeria. Average monthly rainfall data in Nigerian for the period Jan. 1991 to Dec.2020 were considered. The ACF and PACF plots clearly identifies the SARIMA (1,0,2)x(1,1,2)12 as an appropriate model for predicting average monthly rainfall. The performance of the trained Neural Network (NN) analysis clearly favours Levenberg-Marquardt (LM) over the Scaled Conjugate Gradient Descent (SCGD) algorithms and Bayesian Regularization (BR) method with Average Absolute Error 0.000525056. The forecasting performance metric using the RSME and MAE, showed that Neural Network trained by Levenberg-Marquadrt algorithm gives better predicted values of Nigerian rainfall than the SARIMA (1,0,2)x(1,1,2)12.

Suggested Citation

  • Ikpang Nkereuwem Ikpang & Ekom-obong Jackson Okon, 2022. "Modeling Average Rainfall in Nigeria With Artificial Neural Network (ANN) Models and Seasonal Autoregressive Integrated Moving Average (SARIMA) Models," International Journal of Statistics and Probability, Canadian Center of Science and Education, vol. 11(4), pages 1-53, November.
    • Handle: RePEc:ibn:ijspjl:v:11:y:2022:i:4:p:53
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    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

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