An innovative, fast method for landslide susceptibility mapping using GIS-based LSAT toolbox

In this study, landslide susceptibility maps (LSM) of the Akıncılar region were produced with the methods of frequency ratio (FR), information value (IV), logistic regression (LR), random forest (RF), and multi-layer perceptron (MLP) by using a new GIS-based toolbox (LSAT, Landslide Susceptibility A...

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Vydané v:Environmental earth sciences Ročník 80; číslo 6; s. 217
Hlavný autor: Polat, Ali
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Berlin/Heidelberg Springer Berlin Heidelberg 01.03.2021
Springer Nature B.V
Predmet:
Biogeosciences
Earth and Environmental Science
Earth Sciences
Earthquakes
Environmental Science and Engineering
Geochemistry
Geographic information systems
Geographical information systems
Geology
Hydrology/Water Resources
Landslides
Landslides & mudslides
Methods
Model accuracy
Multilayers
Original Article
prediction
regression analysis
Support vector machines
Susceptibility
Terrestrial Pollution
Geographic information system (GIS)
Landslide susceptibility
Weka
Akıncılar
Python scripting
ISSN:1866-6280, 1866-6299
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Shrnutí:In this study, landslide susceptibility maps (LSM) of the Akıncılar region were produced with the methods of frequency ratio (FR), information value (IV), logistic regression (LR), random forest (RF), and multi-layer perceptron (MLP) by using a new GIS-based toolbox (LSAT, Landslide Susceptibility Assessment Tool). LSAT was used to assess the landslide susceptibility of the Akıncılar region located 150 km northwest of Sivas city (Turkey). LSM was successfully constructed using five different methods for the study area. Area under the curve (AUC) values were calculated as 70.95%, 71.85%, 72.57%, 72.67%, 73.93% for prediction rate of FR, IV, LR, MLP and RF methods, respectively. Time-consuming processes are one of the significant problems of constructing LSM. LSAT can be used easily in this type of study and minimizes such problems. Data preparation processes, visualization of modeling results, and accuracy assessment of LSM could very quickly and automatically be done thanks to this tool.
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ISSN:1866-6280
1866-6299
DOI:10.1007/s12665-021-09511-y