2024/06/28 by Răzvan Popescu, Simon Filhol, Bernd Etzelmüller +13 · 11 citations
Earth and Planetary Sciences · Environmental Science · #Climate change and permafrost #Cryospheric studies and observations #Debris #Geography #Geology #Geomorphology #Geotechnical engineering #Hydrology (agriculture) #Landslides and related hazards #Permafrost #Physical geography #Snow
paper · pdf · doi:10.1002/ppp.2232
published in Permafrost and Periglacial Processes 35(3), 243-261 (Wiley)
openalex publication_date 2024/06/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/22
ABSTRACT Computer modeling of sporadic and isolated patches of mountain permafrost distribution is difficult to implement without overestimating it. The main challenge is to determine the very areas where the criteria for permafrost maintenance are met. This paper aims to modeling the permafrost distribution in the Southern Carpathians (SC), a typical marginal periglacial mountain range. For this purpose, a collection of 883 bottom temperature of late winter snow cover (BTS) points was used as a proxy for permafrost presence or absence in order to train several machine learning models. The performances of each model were evaluated with AUC with varying between 0.99 for Maxent and 0.74 for K‐nearest neighbors and most models (five) exhibiting values between 0.82 and 0.86. Other tests such as confusion matrices, sensitivity analyses, data shuffling, and data size reduction tests indicated that Maxent, AdaBoost, and support vector machine offered the best results while logistic regression, neural network, and gradient boosting exhibited rather poor permafrost distributions. The final ensemble median model indicated a total permafrost area of 19.2 km 2 occupying 1%–9% of the alpine area of the studied massifs. NDVI proved crucial for permafrost prediction because it allows delimiting the debris surfaces where permafrost is probable.