Machine Learning Applied to the Analysis of Glacier Masses
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-45438-7_11
Reference19 articles.
1. Abatzoglou, J.T., Dobrowski, S.Z.: Terraclimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958–2015. Sci. Data 5(1), 180160 (2018)
2. Basantes-Serrano, R., et al.: Slight mass loss revealed by reanalyzing glacier mass-balance observations on Glaciar Antisana 15$$\alpha $$ (inner tropics) during the 1995–2012 period. J. Glaciol. 62(231), 124–136 (2016). https://doi.org/10.1017/jog.2016.17
3. Basantes-Serrano, R., Rabatel, A., Vincent, C., Sirguey, P.: An optimized method to calculate the geodetic mass balance of mountain glaciers. J. Glaciol. 64(248), 917–931 (2018)
4. Bolibar, J., Rabatel, A., Gouttevin, I., Galiez, C., Condom, T., Sauquet, E.: Deep learning applied to glacier evolution modelling. Cryosphere 14(2), 565–584 (2020). https://doi.org/10.5194/tc-14-565-2020. https://tc.copernicus.org/articles/14/565/2020/
5. Box, J.E.: Greenland ice sheet mass balance reconstruction. PART II: Surface mass balance (1840–2010). J. Clim. 26(18), 6974–6989 (2013)
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