Observations of active layer thickness (ALT) in the permafrost zone of the Northern Hemisphere are based on the Global Permafrost Network (GTN-P) database (gtnpdatabase.org), and we have also extended the GTN-P data and added more ALT observations from the relevant literature. For other relevant data, please refer to Jin et al., 2024.

  The machine learning models used include logistic regression (LR), random forest (RF), and LightGBM (LGB). Considering that using a single method may lead to overfitting in the simulation, we used the set and average results of the three methods mentioned above when simulating ALT. Using TDD, FDD, leaf area index, precipitation, snow cover, solar radiation, soil moisture content, soil organic matter, and gravel volume content as input variables, corresponding to station observations, the model was established with 90% of the data as the test set and 10% of the data as the validation set to evaluate the accuracy of the model. In order to reduce the uncertainty of a single run, this study ran the three machine learning models 100 times and used their ensemble average results to simulate the distribution changes of active layer thickness in permafrost regions at different periods by combining environmental variables at each period

  Good quality of data