10 Nov 2022
 | 10 Nov 2022

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

Ritu Anilkumar, Rishikesh Bharti, Dibyajyoti Chutia, and Shiv Prasad Aggarwal

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.

Ritu Anilkumar et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Review of Anilkumar et al.', Jordi Bolibar, 20 Dec 2022
    • AC1: 'Reply on RC1', Ritu Anilkumar, 11 Jan 2023
  • RC2: 'Comment on egusphere-2022-1076', Anonymous Referee #2, 21 Feb 2023
    • AC2: 'Reply on RC2', Ritu Anilkumar, 15 Mar 2023

Ritu Anilkumar et al.


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Short summary
Our analysis demonstrates the capability of machine learning models in estimating glacier mass balance in terms of performance metrics and dataset availability. Feature importance analysis suggests that ablation features are significant. This is in agreement with the predominantly negative mass balance observations. We show that ensemble tree models typically depict the best performance. However, neural network models are preferable for biased inputs and kernel-based models for smaller datasets.