Abstract

Accurate identification of weather patterns and timely detection of anomalies are critical for effective meteorological forecasting, especially in regions where predictive systems remain underdeveloped. In much of sub-Saharan Africa, the use of hybrid machine learning methods for long-term weather analysis is still limited. This study investigates the combination of Self-Organizing Maps (SOM) and K-Nearest Neighbours (KNN) to improve weather pattern recognition and anomaly detection. Focusing on meteorological data from Oyo State, Nigeria, spanning 2013 to 2023, the research utilizes SOM to project multidimensional weather variables onto a two-dimensional topological grid, facilitating clustering of similar conditions. KNN is subsequently applied to these clusters to flag outliers that represent potential anomalies. The dataset, obtained from regional meteorological stations, was complete and did not require data imputation. Model performance was assessed using the Silhouette Score and the Davies-Bouldin Index, both of which indicated satisfactory cluster cohesion and separation. The findings show that the integrated SOM-KNN approach reliably identifies recurring weather trends and isolates unusual events, highlighting its value in climate monitoring and anomaly detection. The study demonstrates the applicability of hybrid machine learning techniques in enhancing environmental data analysis in data-limited settings. It offers a practical framework for supporting early warning systems and developing region-specific climate adaptation strategies.

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