Utilizing Artificial Neural Networks to Predict El Niño Southern Oscillation Events Using Nigerian Rainfall Data: A Teleconnection Analysis

Climate variability poses significant threats to sustainable development in sub-Saharan Africa, particularly in sectors dependent on rainfall such as agriculture, water resources, and disaster management. This study investigates the teleconnection between Nigerian rainfall variability and El Niño–Southern Oscillation (ENSO) events using Artificial Neural Networks (ANNs). Monthly rainfall data from Lagos, Port Harcourt, Abuja and Kano were integrated with the Niño 3.4 index to develop both regression and classification ANN models. The dataset was partitioned into training (70%), validation (15%) and testing (15%) subsets. The rainfall regression model achieved a testing R² of 0.79 and RMSE of 17.21 mm, outperforming 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 and demonstrate the applicability of machine learning frameworks in strengthening climate adaptation and early warning systems. The study contributes to Sustainable Development Goals (SDGs) 2, 6, 11 and 13 by advancing predictive climate intelligence for resilience planning.