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Modeling and classification of alluvial fans with DEMs and machine learning methods : a case study of Slovenian torrential fans
ID Babič, Matej (Author), ID Petrovič, Dušan (Author), ID Sodnik, Jošt (Author), ID Soldo, Božidar (Author), ID Komac, Marko (Author), ID Chernieva, Olena (Author), ID Kovačič, Miha (Author), ID Mikoš, Matjaž (Author), ID Calì, Michele (Author)

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Abstract
Alluvial (torrential) fans, especially those created from debris-flow activity, often endanger built environments and human life. It is well known that these kinds of territories where human activities are favored are characterized by increasing instability and related hydrological risk; therefore, treating the problem of its assessment and management is becoming strongly relevant. The aim of this study was to analyze and model the geomorphological aspects and the physical processes of alluvial fans in relation to the environmental characteristics of the territory for classification and prediction purposes. The main geomorphometric parameters capable of describing complex properties, such as relative fan position depending on the neighborhood, which can affect their formation or shape, or properties delineating specific parts of fans, were identified and evaluated through digital elevation model (DEM) data. Five machine learning (ML) methods, including a hybrid Euler graph ML method, were compared to analyze the geomorphometric parameters and physical characteristics of alluvial fans. The results obtained in 14 case studies of Slovenian torrential fans, validated with data of the empirical model proposed by Bertrand et al. (2013), confirm the validity of the developed method and the possibility to identify alluvial fans that can be considered as debris-flow prone.

Language:English
Keywords:digital elevation model, torrential fan surfaces, geomorphometric parameters, graph method, debris flows
Work type:Article
Typology:1.01 - Original Scientific Article
Organization:FGG - Faculty of Civil and Geodetic Engineering
Publication status:Published
Publication version:Version of Record
Year:2021
Number of pages:18 str.
Numbering:Vol. 13, iss. 9, art. 1711
PID:20.500.12556/RUL-127268 This link opens in a new window
UDC:528.7:556(497.4)
ISSN on article:2072-4292
DOI:10.3390/rs13091711 This link opens in a new window
COBISS.SI-ID:61449731 This link opens in a new window
Publication date in RUL:01.06.2021
Views:791
Downloads:138
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Record is a part of a journal

Title:Remote sensing
Shortened title:Remote sens.
Publisher:MDPI
ISSN:2072-4292
COBISS.SI-ID:32345133 This link opens in a new window

Licences

License:CC BY 4.0, Creative Commons Attribution 4.0 International
Link:http://creativecommons.org/licenses/by/4.0/
Description:This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.
Licensing start date:01.05.2021

Secondary language

Language:Slovenian
Keywords:digitalni model reliefa, DMR, daljinsko zaznavanje, hidrologija, struktura hudourniških vršajev, geomorfometrični parametri, metoda grafov, drobirski tokovi

Projects

Funder:ARRS - Slovenian Research Agency
Project number:J7-8273
Name:Prepoznavanje potencialno nevarnih hudourniških vršajev z metodami geomorfometrije in simulacijami nastanka vršajev

Funder:Other - Other funder or multiple funders
Funding programme:International Programme on Landslides
Project number:IPL-225

Funder:Other - Other funder or multiple funders
Funding programme:Università degli Studi di Catania
Project number:308811
Acronym:NASCAR

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