| Citation: | Baijun Shang, Jing Gao. Using Machine Learning to Explore Changes in Atmospheric Water Vapor Stable Isotopes: A Case Study at Muztagh Ata, Western Tibetan Plateau. Journal of Earth Science, 2026, 37(3): 1021-1035. doi: 10.1007/s12583-024-1983-y
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Atmospheric water vapor stable isotopes (δ18O and δD) are an important proxy to track the water cycle, changes of historical temperature and humidity, and characterize the monsoon intensity. Isotope general circulation models (iGCMs) are the main method for water vapor stable isotopes (Vaporiso) simulation, but the simulations from iGCMs are difficult to investigate Vaporiso dynamics at meso and sub-mesoscales due to the low spatial resolution and uncertainties of parameterization. Here, we present how machine learning can be a powerful tool in representing changes of daily Vaporiso, combined with the high-resolution Vaporiso observed at Muztagh Ata, western Tibetan Plateau (TP), from January 2022 to February 2023, where is dominated by the Westerlies. Different machine learning methods (random forest, BP neural network, support vector machine) and different parameter optimization schemes (genetic algorithm, particle swarm optimization) are used to represent the Vaporiso and corresponding meteorological conditions. We also compared simulations from machine learning and IsoGSM with the observed Vaporiso. The root-mean-square error (RMSE) of δ18O and δD simulated by machine learning is smaller than IsoGSM, and the correlation coefficient (
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