20.500.12556/DKUM-70766 Landslide assessment of the Strača basin (Croatia) using machine learning algorithms Bočna nosilnost kratkih togih pilotov v dvoslojnih nevezljivih tleh In this research, machine learning algorithms were compared in a landslide-susceptibility assessment. Given the input set of GIS layers for the Starča Basin, which included geological, hydrogeological, morphometric, and environmental data, a classification task was performed to classify the grid cells to: (i) landslide and non-landslide cases, (ii) different landslide types (dormant and abandoned, stabilized and suspended, reactivated). After finding the optimal parameters, C4.5 decision trees and Support Vector Machines were compared using kappa statistics. The obtained results showed that classifiers were able to distinguish between the different landslide types better than between the landslide and non-landslide instances. In addition, the Support Vector Machines classifier performed slightly better than the C4.5 in all the experiments. Promising results were achieved when classifying the grid cells into different landslide types using 20% of all the available landslide data for the model creation, reaching kappa values of about 0.65 for both algorithms. V tej raziskavi so avtorji primerjali algoritme strojnega učenja v okviru prognoze drsenja terena. Na osnovi GIS slojev področja kotline Starča, ki so vključevali geološke, hidrogeološke, morfometrijske in druge prostorske podatke, je napravljena klasifikacija mrežnih celic na (i) primerih »drsečega« in »stabilnega terena«, (ii) različnih tipih drsečega terena (»potencialen-neaktiven«, »stabiliziran-saniran« in »reaktiviran«). Po optimizaciji parametrov modela za C4.5 decision trees in Support Vector Machines so primerjali dobljene rezultate klasifikacije s pomočjo kappa statistike. Rezultati kažejo, da sta omenjena modela bolje razlikovala med različnimi tipi drsečega terena kot med drsečim in stabilnim terenom. Prav tako je bil klasifikator Support Vector Machines v vseh preizkusih nekoliko uspešnejši od C4.5. Spodbudne rezultate so dobili v eksperimentu, kjer so klasificirali različne tipe drsečega terena, uporabili pa so samo 20% od skupnega števila podatkov o drsečem terenu. V tem primeru so za oba klasifikatorja dobili vrednost kappa okoli 0.65. landslides support vector machines decision trees classifier Starča Basin geofizika plazovi modeli algoritmi kotlina Strača Hrvaška true false true Angleški jezik Slovenski jezik Fakulteta za gradbeništvo, prometno inženirstvo in arhitekturo Univerze v Mariboru Znanstveno delo 2018-06-13 09:47:39 2018-06-13 09:51:00 2022-08-01 19:47:42 2019-07-11 18:19:10 2011 0 0 str. 45-55 št. 2 Letn. 8 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 1854-0171 550.348.435(497.5) 215987712 262545920 1854-0171 URN:SI:UM:DK:IV0E4EOX Acta_geotechnica_Slovenica_2011_Marjanovic_et_al._Landslide_assessment_of_the_Straca_basin_(Croatia)_using_machine_learning_algorithms.pdf Acta_geotechnica_Slovenica_2011_Marjanovic_et_al._Landslide_assessment_of_the_Straca_basin_(Croatia)_using_machine_learning_algorithms.pdf 1 703813029563B01BA05C03EEFCA4E844 28b5c897f3c93e0e144103c6803fac80c6af2d9af7a77c3c2d48b972fba6c85c 0586b814-7c10-11eb-bb7a-00155d0001ca 20.500.12556/dkum/b748b94c-e4a1-4a8b-814c-bf16257bd5d1 https://dk.um.si/Dokument.php?lang=slv&id=125558 http://fgserver3.fg.um.si/journal-ags/2011-2/article-3.asp 1 https://dk.um.si/Dokument.php?lang=slv&id=125559 Fakulteta za gradbeništvo, prometno inženirstvo in arhitekturo Acta geotechnica Slovenica 0 0 0