The identification of influencing factors (IFs) of land surface temperature (LST) is crucial for developing effective strategies to mitigate global warming and conducting other relevant studies. However, most previous studies ignored the potential impact of interactions between IFs, which might lead to biased conclusions. Generalized additivity models (GAMs) can provide more explanatory results compared to traditional machine learning models. Therefore, this study employs GAMs to investigate the impact of IFs and their interactions on LST, aiming to accurately detect significant factors that drive the changes in LST. The results of this case study conducted in Nanjing, China, showed that the GAMs incorporating the interactions between factors could improve the fitness of LST and enhance the explanatory power of the model. The autumn model exhibited the most significant improvement in performance, with an increase of 0.19 in adjusted-R² and a 17.9% increase in deviance explained. In the seasonal model without interaction, vegetation, impervious surface, water body, precipitation, sunshine hours, and relative humidity showed significant effects on LST. However, when considering the interaction, the previously observed significant influence of the water body in spring and impervious surface in summer on LST became insignificant. In addition, under the interaction of precipitation, relative humidity, and sunshine hours, as well as the cooling effect of NDVI, there was no statistically significant upward trend in the seasonal mean LST during 2000–2020. Our study suggests that taking into account the interactions between IFs can identify the driving factors that affect LST more accurately.

Groundwater is essential for water resources but faces over-extraction and supply-demand imbalance. Precisely comprehending alterations in groundwater is crucial for sustainable development. Groundwater levels demonstrate a delayed reaction to meteorological circumstances, frequently neglected in current research, diminishing predictive accuracy. This study investigates the lag effect of precipitation and evapotranspiration on groundwater forecasting in Hebei Province, China. We performed a lag analysis utilizing long-term data to ascertain correlations between groundwater levels and climatic variables. Two groundwater prediction models using the random forest algorithm were developed, one incorporating the lag effect and the other excluding it. The Shapley Additive exPlanations (SHAP) method assessed the significance of each element and its influence on model variations. Research reveals a notable lag in groundwater response: shallow groundwater reacts to precipitation after 4.55 months and to evapotranspiration after 9.21 months; deep groundwater responds after 5.91 and 9.63 months, respectively. The inclusion of the lag effect resulted in higher accuracy of the model, with an average reduction of 35.7% in MAE and 18.20% in RMSE. The improved model more accurately captured the influence of meteorological factors on groundwater levels, potentially providing more scientific decision support for the rational allocation and sustainable use of water resources.