Revealing the Drivers of Land Surface Temperature Variation in Guiyang City Using XGBoost and SHAP
Based on revealing the interannual and seasonal spatiotemporal evolution trends of the Land Surface Temperature(LST)in Guiyang City through mathematical statistics methods,a diagnostic analysis of the reasons behind the spatiotemporal evolution patterns of LST in Guiyang City was conducted.This analysis used the eXtreme Gradient Boosting(XGBoost)algo-rithm and the SHapley Additive ExPlanations(SHAP)method to better understand the driving effects of influencing factors and their interactions on LST.The results showed that from 2000 to 2020,the proportion of low-temperature areas during au-tumn showed a significant decreasing trend,with an average annual reduction rate of approximately 0.068％.The LST warm-ing exhibited a distribution characteristic of being higher in the south and lower in the north,with the changes in the sub-high temperature and high-temperature areas being most evident in Yunyan District and Nanming District.XGBoost was able to effectively characterize the response relationship between LST and influencing factors in various seasons,achieving high modeling accuracy.The constructed model had RMSE,MAE,and R2 values of 0.516 9 ℃,0.389 3 ℃,and 0.895 0,respectively,on the validation set.The analysis results based on SHAP indicated that the importance of influencing factors on LST varied seasonally.Overall,Gross Domestic Product(GDP),elevation,precipitation,and vegetation cover had the greatest impact on LST.Except for GDP and population density,the influencing factors mainly exhibited a nonlinear relationship with LST changes.Elevation,precipitation,vegetation,and water bodies primarily had a cooling effect on LST in the study area,while impervious surfaces,bare land,agricultural land,population density,and GDP primarily had a warming effect.
land surface temperaturespatiotemporal evolutioninfluencing factorsXGBoostSHAP
万方数据
维普
