Abstract:
Climate change presents growing challenges in regions like Kenya, where diverse terrain and climatic variability
complicate accurate environmental forecasting. Traditional climate models often fall short in capturing both
the non-linear relationships among climatic variables and the spatial dependencies inherent in such data.
To address these limitations, this study introduces a novel hybrid model that integrates Gaussian Process
Regression (GPR) and Generalized Additive Models (GAM) to enhance spatio-temporal climate prediction.The model was developed by combining the structured, interpretable components of GAM with the spatially
aware, probabilistic strengths of GPR, using climate data collected from the Google Earth Engine covering
the period 2015–2024. Model parameters were estimated through generalized cross-validation and optimized
using the L-BFGS algorithm. Results indicate that the hybrid model significantly improves predictive
accuracy compared to standalone GPR or GAM approaches, achieving an RMSE of 1.27°C and an R²
of 0.91. These findings demonstrate the model’s effectiveness in capturing Kenya’s spatial and climatic
heterogeneity. The study recommends the hybrid model’s application in climate-sensitive sectors such as
agriculture, infrastructure development, and early warning systems, with future work focusing on scalability
and real-time deployment.
