(3) Why it matters:
🌊 Glaciers = key freshwater reservoirs & climate indicators
🧊 Few are directly monitored
🤖 ML (XGBoost) helps bridge data gaps

Mass Balance Machine on GitHub 👉 lnkd.in/eyFB5SZp

Link to paper 👉 lnkd.in/eD-H5DT8

#Glaciology #MachineLearning #Cryosphere #ClimateChange #Hydrology

(2) Tested on independent Norwegian glaciers, MBM was compared with GloGEM, OGGM & PyGEM:
✅ RMSE ≈ 0.59–1.00 m w.e.
✅ Bias: –0.01 to +0.04 m w.e.
✅ Outperforms conventional models for seasonal MB
✅ Promising transferability across glaciers & climates 🌍

Link to preprint: egusphere.copernicus.org/preprints/20...

(3/3) Using just two predictors obtained through dimensionality reduction techniques, miniML-MB can closely match the PMB for individual glacier sites, surpassing the PDD model for most sites as long as predictions are made within a range of meteorological conditions similar to the training set.

(2/3) Our dimensionality reduction framework singles out summer temps (May–Aug) & winter precip (Oct–Feb) as key drivers of glacier PMB. Unlike PDD models, our ML approach directly selects predictors from data—boosting performance & validating climatic drivers in Swiss glaciers.

(1/3) miniML-MB is designed to simulate annual point mass balance at individual glacier sites using meteorological variables (air temperature and total precipitation). We rely on data collected at 28 individual measurement sites across the Swiss Alps:

In case of interest in the Mass Balance Machine, check out our GitHub 🧊 github.com/ODINN-SciML/...

Link to apply: sirop.org/app/75ab995e...