Preprints
https://doi.org/10.5194/egusphere-2022-1076
https://doi.org/10.5194/egusphere-2022-1076
 
10 Nov 2022
10 Nov 2022
Status: this preprint is open for discussion.

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

Ritu Anilkumar1,2, Rishikesh Bharti2, Dibyajyoti Chutia1, and Shiv Prasad Aggarwal1 Ritu Anilkumar et al.
  • 1North Eastern Space Applications Centre, Department of Space, Umiam, Ri Bhoi
  • 2Department of Civil Engineering, Indian Institute of Technology, Guwahati

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: open (until 05 Jan 2023)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Ritu Anilkumar et al.

Viewed

Total article views: 242 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
161 75 6 242 18 4 3
  • HTML: 161
  • PDF: 75
  • XML: 6
  • Total: 242
  • Supplement: 18
  • BibTeX: 4
  • EndNote: 3
Views and downloads (calculated since 10 Nov 2022)
Cumulative views and downloads (calculated since 10 Nov 2022)

Viewed (geographical distribution)

Total article views: 237 (including HTML, PDF, and XML) Thereof 237 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 06 Dec 2022
Download
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.