Next Article in Journal
The Sentinel-3 SLSTR Atmospheric Motion Vectors Product at EUMETSAT
Previous Article in Journal
Analysis Ready Data of the Chinese GaoFen Satellite Data
Article

Modeling and Classification of Alluvial Fans with DEMs and Machine Learning Methods: A Case Study of Slovenian Torrential Fans

1
Faculty of Information Studies, Ljubljanska cesta 31a, SI-8000 Novo Mesto, Slovenia
2
Faculty of Civil and Geodetic Engineering, University of Ljubljana, SI-1000 Ljubljana, Slovenia
3
Tempos Ltd., Environmental Civil Engineering, SI-1000 Ljubljana, Slovenia
4
Department of Civil Engineering, University North, 42000 Varaždin, Croatia
5
Department of Reinforced Concrete Structures and Transport Facilities, Odessa State Academy of Civil Engineering, 65000 Odesa, Ukraine
6
Štore Steel Ltd., Železarska cesta 3, SI-3220 Štore, Slovenia
7
Electric, Electronics and Computer Engineering Department, University of Catania, 95125 Catania, Italy
*
Author to whom correspondence should be addressed.
Remote Sens. 2021, 13(9), 1711; https://doi.org/10.3390/rs13091711
Received: 9 March 2021 / Revised: 21 April 2021 / Accepted: 25 April 2021 / Published: 28 April 2021
(This article belongs to the Section Engineering Remote Sensing)
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. View Full-Text
Keywords: digital elevation model; torrential fan surfaces; geomorphometric parameters; graph method; debris flows digital elevation model; torrential fan surfaces; geomorphometric parameters; graph method; debris flows
Show Figures

Figure 1

MDPI and ACS Style

Babič, M.; Petrovič, D.; Sodnik, J.; Soldo, B.; Komac, M.; Chernieva, O.; Kovačič, M.; Mikoš, M.; Calì, M. Modeling and Classification of Alluvial Fans with DEMs and Machine Learning Methods: A Case Study of Slovenian Torrential Fans. Remote Sens. 2021, 13, 1711. https://doi.org/10.3390/rs13091711

AMA Style

Babič M, Petrovič D, Sodnik J, Soldo B, Komac M, Chernieva O, Kovačič M, Mikoš M, Calì M. Modeling and Classification of Alluvial Fans with DEMs and Machine Learning Methods: A Case Study of Slovenian Torrential Fans. Remote Sensing. 2021; 13(9):1711. https://doi.org/10.3390/rs13091711

Chicago/Turabian Style

Babič, Matej, Dušan Petrovič, Jošt Sodnik, Božo Soldo, Marko Komac, Olena Chernieva, Miha Kovačič, Matjaž Mikoš, and Michele Calì. 2021. "Modeling and Classification of Alluvial Fans with DEMs and Machine Learning Methods: A Case Study of Slovenian Torrential Fans" Remote Sensing 13, no. 9: 1711. https://doi.org/10.3390/rs13091711

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop