Local interpretable soil texture prediction based on modified geographically weighted regression AdaBoost method

To address these issues, this study proposes an improved interpretability oriented geographically weighted regression adaptive enhancement (IGWRABoost) method and applies it to the spatial prediction of soil texture in Yongqiao District, Suzhou City, Anhui Province, China, which is an important wheat producing area on the Huang Huai Hai Plain.

The AdaBoost sub model is constructed by incorporating geographic weighting into the local community. Subsequently, multiple local models underwent Gaussian weighted fusion during the prediction phase to capture the spatial non-stationary relationship between texture and environmental factors. The results showed that the IGWRABoost method achieved the highest R ² and lowest root mean square error (RMSE) and mean absolute error (MAE) values in soil texture prediction.