Logo Lanfrica
  • Accueil
  • Atlas
  • Analyses
  • Documentation
  • Sign in

© 2026 Lanfrica. Tous droits réservés. Tous les droits d'auteur des ressources affichées sur le site Web Lanfrica appartiennent aux détenteurs de droits d'auteur d'origine, sauf indication contraire explicite.

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

Domaine:

climate

Type de record:

paper
Créateur:
IkpEko
Éditeur:
Can
Hôte:
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.

Visit

doi.org

Licenses

https://creativecommons.org/licenses/by/4.0

Similaires

Forecasting Rainfall in Mauritius using Seasonal Autoregressive Integrated Moving Average and Artificial Neural NetworksA Comparative Study of Autoregressive Integrated Moving Average and Artificial Neural Networks ModelsForecasting Commodity Price Index of Food and Beverages in Kenya Using Seasonal Autoregressive Integrated Moving Average (SARIMA) ModelsPREDICTIVE ABILITY OF ARTIFICIAL NEURAL NETWORK (ANN) AND AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA) ON COVID-19 CASES IN NIGERIAForecasting the South African tax-to-GDP ratio series utilizing seasonal autoregressive integrated moving average and artificial neural networks modelsA Comparative Analysis of Artificial Neural Network and Autoregressive Integrated Moving Average Model on Modeling and Forecasting Exchange Rate

Forecasting Rainfall in Mauritius using Seasonal Autoregressive Integrated Moving Average and Artificial Neural Networks

In this paper, two forecasting methods namely, the autoregressive integrated moving average (ARIMA)

A Comparative Study of Autoregressive Integrated Moving Average and Artificial Neural Networks Models

In this study, the forecasting capabilities of nonlinear models as Artificial Neural Networks and li

Forecasting Commodity Price Index of Food and Beverages in Kenya Using Seasonal Autoregressive Integrated Moving Average (SARIMA) Models

Price stability is the primary monetary policy objective in any economy since it protects the intere

PREDICTIVE ABILITY OF ARTIFICIAL NEURAL NETWORK (ANN) AND AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA) ON COVID-19 CASES IN NIGERIA

Forecasting the South African tax-to-GDP ratio series utilizing seasonal autoregressive integrated moving average and artificial neural networks models

South Africa has experienced successful tax collections but has not achieved the governance outcomes

A Comparative Analysis of Artificial Neural Network and Autoregressive Integrated Moving Average Model on Modeling and Forecasting Exchange Rate

This paper examines the forecasting performance of Autoregressive Integrated Moving Average