Modelling the Point Mass Balance for the Glaciers of Central European Alps using Machine Learning Techniques

Abstract

Abstract. Glacier mass balance is typically estimated using a range of in-situ measurements, remote sensing measurements, and physical and temperature index modelling techniques. With improved data collection and access to large datasets, data-driven techniques have recently gained prominence in modelling natural processes. The most common data-driven techniques used today are linear regression models and, to some extent, non-linear machine learning models such as artificial neural networks. However, the entire host of capabilities of machine learning modelling has not been applied to glacier mass balance modelling. This study used monthly meteorological data from ERA5-Land to drive four machine learning models: random forest (ensemble tree type), gradient-boosted regressor (ensemble tree type), support vector machine (kernel type) and artificial neural networks (neural type). We also use ordinary least squares linear regression as a baseline model against which to compare the performance of the machine learning models. Further, we assess the requirement of data for each of the models and the requirement for hyperparameter tuning. Finally, the importance of each meteorological variable in the mass balance estimation for each of the models is estimated using permutation importance. All machine learning models outperform the linear regression model. The neural network model depicted a low bias, suggesting the possibility of enhanced results in the event of biased input data. However, the ensemble tree-based models, random forest and gradient-boosted regressor outperformed all other models in terms of the evaluation metrics and interpretability of the meteorological variables. The gradient-boosted regression model depicted the best coefficient of determination value of 0.713. The feature importance values associated with all machine learning models suggested high importance to meteorological variables associated with ablation. This is in line with predominantly negative mass balance observations. We conclude that machine learning techniques are promising in estimating glacier mass balance and can incorporate information from more significant meteorological variables as opposed to a simplified set of variables used in temperature index models.

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

“…ML has recently become popular for MB modelling: [7] projects the 21st century glacier evolution in the French Alps with a standard multi-layer perceptron (MLP) model for MB as a better alternative to linear regression (LR); [12] models winter point MBs using gradient boosting regressor (GBR); [13] estimates annual point MBs using four different methods, i.e. support-vector machine (SVM), RF, GBR and MLP.…”

Universal Differential Equations for glacier ice flow modelling

“…Baumhoer et al (2019); Mohajerani et al (2019)). Nonetheless, progress has been made with surrogate models for ice flow modelling (Riel et al, 2021;Jouvet et al, 2021), subglacial processes (Brinkerhoff et al, 2020), glacier mass balance modelling (Bolibar et al, 2020a, b;Anilkumar et al, 2022;Guidicelli et al, 2023) or super-resolution applications to downscale glacier ice thickness (Leong and Horgan, 2020). In terms of modelling glacier processes regionally or globally, it is still very challenging to move from small-scale detailed observations and physical processes to large-scale observations and parametrizations.…”