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

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@Article{Babic:2021:Remote_Sensing,

• author = "Matej Babic and Dusan Petrovic and Jost Sodnik and Bozo Soldo and Marko Komac and Olena Chernieva and Miha Kovacic and Matjaz Mikos and Michele Cali",
• title = "Modeling and Classification of Alluvial Fans with {DEMs} and Machine Learning Methods: A Case Study of {Slovenian} Torrential Fans",
• journal = "Remote Sensing",
• year = "2021",
• volume = "13",
• number = "9",
• article-number = "1711",
• month = "28 " # apr,
• keywords = "genetic algorithms, genetic programming, random forest, RF, support vector machine, SVM, neural network, ANN, digital elevation model, torrential fan surfaces, geomorphometric parameters, graph method, debris flows",
• publisher = "MDPI",
• ISSN = "2072-4292",
• URL = "https://repozitorij.uni-lj.si/IzpisGradiva.php?id=127268",
• URL = "https://www.mdpi.com/2072-4292/13/9/1711",
• DOI = "doi:10.3390/rs13091711",
• size = "18 pages",
• 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 analyse 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.",
• notes = "Faculty of Information Studies, Ljubljanska cesta 31a, SI-8000 Novo Mesto, Slovenia",

}

Genetic Programming entries for Matej Babic Dusan Petrovic Jost Sodnik Bozo Soldo Marko Komac Olena Chernieva Miha Kovacic Matjaz Mikos Michele Cali

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