Application of an STL–ARIMA Hybrid Framework for Monthly Rainfall Forecasting in Maiduguri, Nigeria
DOI:
https://doi.org/10.70882/josrar.2026.v3i1.146Keywords:
STL–ARIMA Hybrid Model, Rainfall Forecasting, Maiduguri Climate Variability, Seasonal-Trend Decomposition, Climate Change (SDG 13)Abstract
The problems of climate variability in Sahel regions with severe rainfall anomalies bring serious challenges to the water resource management and mitigation of the flood disasters. This paper analyzes the effectiveness of a hybrid STL-ARIMA model to predict monthly rainfall in Maiduguri, Nigeria, which is a town that was affected by the 2024 floods. The article compares the hybrid, traditional Seasonal ARIMA (SARIMA) and Exponential Smoothing by Holt-Winter to assess previous rainfall data of 1981-2023. The suggested method employs Seasonal-Trend Decomposition by Loess (STL) to single out non-linear and seasonal trends that are too complicated then subjecting the time series to ARIMA modeling. With regards to performance, it can be seen that the STL-ARIMA model is far ahead of the conventional approach with a Root mean square error of 29.54mm, as opposed to 43.94mm and 43.01mm using the SARIMA and Holt-Winter models respectively. The hybrid model minimized the Mean Squared Error (MSE) by nearly 55% and it was more effective in terms of capturing the sharp variance variation and extreme wet-season peaks, which are characteristic of the area. These results provide a strong scientific foundation in enhancing flood early warning systems directly related to SDG 13 (Climate Action) goals in Northeastern Nigeria.
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