Uncertainty-Aware Learning With Label Noise for Glacier Mass Balance Modeling

Glacier mass balance (MB) modeling is crucial for understanding the impact of climate change on Earth's freshwater resources and sea-level rise. Recent works have shown the benefit of using machine learning (ML) and deep learning (DL) methods to better capture the nonlinearities in the system than commonly used temperature-index models. However, when relying on remote sensing products for training, the presence of data noise is a challenge for these methods, and therefore quantifying the uncertainty becomes essential. In this work, we produce a tabular dataset consisting of annual MBs for 1000 glaciers over 20 years with meteorological and topographical input features. Using this dataset, we systematically study various uncertainty estimation methods and their impact on the quality of the predictions. Our experimental results show that ensemble methods are promising for capturing the uncertainty in the data: their predictions are more accurate, more robust against label noise, and better calibrated. In particular, the multilayer perceptron (MLP) ensemble coupled with an explicit noise model shows an increase of up to 5.5% in the explained variance and is much less affected by the gradually injected label noise: the average mean absolute error (MAE) increases at a rate twice smaller. For reproducibility, code and data are available at https://github.com/dcodrut/oggm_smb_dl_uq.