Asagha et al. (2026) Utilizing Artificial Neural Networks to Predict El Niño Southern Oscillation Events Using Nigerian Rainfall Data: A Teleconnection Analysis

⚠️ Warning: This summary was generated from the abstract only, as the full text was not available.

Identification

• Journal: Open MIND
• Year: 2026
• Date: 2026-04-03
• Authors: Asagha, Emmanuel Nkoro, Ngang, Benedict Ugboji
• DOI: 10.5281/zenodo.19401990

Research Groups

Not specified in the provided text.

Short Summary

This study investigates the teleconnection between Nigerian rainfall variability and El Niño–Southern Oscillation (ENSO) events using Artificial Neural Networks (ANNs), demonstrating that ANNs can accurately model these climate signals for enhanced adaptation and early warning systems.

Objective

• To investigate the teleconnection between Nigerian rainfall variability and El Niño–Southern Oscillation (ENSO) events using Artificial Neural Networks (ANNs).

Study Configuration

• Spatial Scale: Nigeria (Lagos, Port Harcourt, Abuja, Kano)
• Temporal Scale: Monthly

Methodology and Data

• Models used: Artificial Neural Networks (ANNs) for both regression and classification. Compared against ARIMA and Multiple Linear Regression models.
• Data sources: Monthly rainfall data from Lagos, Port Harcourt, Abuja, and Kano; Niño 3.4 index.

Main Results

• The rainfall regression model achieved a testing R² of 0.79 and a Root Mean Square Error (RMSE) of 17.21 mm.
• The ANN regression model outperformed ARIMA and Multiple Linear Regression models.
• ENSO phase classification accuracy reached 87.2% on testing data.
• The findings confirm measurable teleconnection signals between Pacific Ocean variability and West African rainfall.
• The study demonstrates the applicability of machine learning frameworks in strengthening climate adaptation and early warning systems.

Contributions

• Advances predictive climate intelligence for resilience planning in sub-Saharan Africa.
• Demonstrates the effectiveness of Artificial Neural Networks in modeling complex climate teleconnections for improved climate adaptation and early warning systems.
• Contributes to Sustainable Development Goals (SDGs) 2 (Zero Hunger), 6 (Clean Water and Sanitation), 11 (Sustainable Cities and Communities), and 13 (Climate Action).

Funding

Not specified in the provided text.

Citation

@article{Asagha2026Utilizing,
  author = {Asagha and Nkoro, Emmanuel and Ngang and Ugboji, Benedict},
  title = {Utilizing Artificial Neural Networks to Predict El Niño Southern Oscillation Events Using Nigerian Rainfall Data: A Teleconnection Analysis},
  journal = {Open MIND},
  year = {2026},
  doi = {10.5281/zenodo.19401990},
  url = {https://doi.org/10.5281/zenodo.19401990}
}