Predicting Net Primary Productivity Using Geographically Weighted Machine Learning: A Comparative Study in the Eastern Sahel

Net Primary Productivity (NPP) is a vital ecological indicator used to monitor land productivity and the health of ecosystems, particularly in climate-sensitive areas like the Eastern Sahel. However, the spatial heterogeneity in the relationships between NPP and environmental factors complicates accurate predictions. This research aimed to evaluate the effectiveness of geographically weighted statistical and machine learning models in predicting NPP, while considering spatial non-stationarity and non-linear interactions. The study used 939 spatial observations of the NPP in conjunction with four environmental predictors: rainfall, temperature, soil moisture, and elevation, spanning Niger, Chad, and Sudan. Initially, a global Ordinary Least Squares (OLS) model was used as a reference point. Subsequently, three geographically weighted models, Geographically Weighted Regression (GWR), Geographically Weighted Random Forest (GWRF) and Geographically Weighted Neural Network (GWNN) were executed to account for spatial variability and non-linear effects. The performance of the models was assessed using R 2 , Mean Absolute Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and spatial residual diagnostics. All geographically weighted models outperformed the global OLS baseline in terms of both predictive accuracy and spatial sensitivity. GWNN achieved the highest performance ( R 2 = 0.9360; RMSE = 0.0333), followed closely by GWRF ( R 2 = 0.9308) and GWR ( R 2 = 0.9207), compared to OLS ( R 2 = 0.8354). The residual spatial autocorrelation was completely resolved in GWNN and GWRF. Rainfall was consistently the most significant predictor, while the effects of other variables, such as elevation and temperature, varied between different spatial contexts. The findings of this research emphasise the value of combining spatial weighting with machine learning methodologies to model ecological productivity in heterogeneous landscapes. The GWNN model, in particular, stands out as a powerful tool for improving NPP predictions in regions sensitive to climate change.