Spatio-temporal reconstruction of annual glacier mass balance in the Central Asia (1950- 2020) using machine learning method

This dataset reconstructs the annual mass balance of glaciers larger than 0.1 km² in the Tien Shan and Pamir regions from 1950 to 2022. The dataset is derived using a nonlinear relationship between glacier mass balance and meteorological and topographical variables. The reconstruction method employs the XGBoost algorithm. Initially, XGBoost is trained on the complete training dataset, followed by incremental training for each sub-region to tailor models to specific regional characteristics. The final training results yield an average coefficient of determination (R²) of 0.87.

All code used in this dataset is publicly available and organized into the following five sections:

Data Processing

Code for extracting monthly meteorological variables.

Combines meteorological and topographical variables for each glacier.

Model Training

Implements the two-step training process for all ensemble learning methods tested in this study.

Result Analysis

Pie charts of mass balance distribution for clustered glaciers.

Line graphs of annual mass balance for each sub-region.

Result Evaluation

Extracts glacier mass balance data from previous studies.

Compares these data with the results of this study.

SHAP Analysis

Provides scripts to generate SHAP (SHapley Additive exPlanations) value-related figures, highlighting the contribution of different variables to model predictions.