Integrating landslide magnitude in the susceptibility assessment of the City of Doboj, using machine learning and heuristic approach

Објеката

Тип
Рад у часопису
Верзија рада
објављена верзија
Језик
енглески
Креатор
Cvjetko Sandić, Miloš Marjanović, Biljana Abolmasov, Radislav Tošić
Извор
Journal of Maps
Издавач
Taylor&Francis
Датум издавања
2023
Сажетак
In this work, landslide assessment of the Doboj City area was modeled by combining machine learning and heuristic tools. The machine learning part was used to map the Morphometric factor. i.e. probability of landslides based on relation between the magnitude of events and morphometric parameters: elevation, distance to streams, slope, profile curvature, and aspect. The Random Forest and Support Vector Machines algorithms were implemented in the learning protocol, which included several strategies: balancing of the training/testing set size, algorithm optimization via cross-validation, and cross-scaling. The best performing Morphometric factor ap was created by learning on 50 m and testing on 25 m dataset. The heuristic part was used for modeling of Lithological factor and Land Cover factor maps, by expert-driven scoring of their units, within 0-1 range of values. The final Susceptibility map was obtained by multiplying all three factor maps resulting in a high-performing model with AUC=0.97 and acc=92%.
том
19
Број
1
број страница
10
doi
10.1080/17445647.2022.2163199
issn
1744-5647
Просторно покривање
Bosna i Hercegovina
Subject
podložnost na kliženje, magnituda klizišta, mašinsko učenje, heuristički pristup, Doboj
Landslide susceptibility, landslide magnitude, machine learning, heuristic approach, Doboj
Шира категорија рада
M20
Ужа категорија рада
М22
Права
Отворени приступ
Лиценца
Creative Commons – Attribution 4.0 International
Формат
.pdf

Cvjetko Sandić, Miloš Marjanović, Biljana Abolmasov, Radislav Tošić. "Integrating landslide magnitude in the susceptibility assessment of the City of Doboj, using machine learning and heuristic approach" in Journal of Maps, Taylor&Francis (2023). https://doi.org/ 10.1080/17445647.2022.2163199

This item was submitted on 26. фебруар 2023. by [anonymous user] using the form “Рад у часопису” on the site “Радови”: http://gabp-dl.rgf.rs/s/repo

Click here to view the collected data.