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Article

A Comprehensive Assessment of XGBoost Algorithm for Landslide Susceptibility Mapping in the Upper Basin of Ataturk Dam, Turkey

1
Department of Geomatics Engineering, Hacettepe University, 06800 Beytepe Ankara, Turkey
2
Department of Geological Engineering, Hacettepe University, 06800 Beytepe Ankara, Turkey
*
Author to whom correspondence should be addressed.
Academic Editor: Hyuck-Jin Park
Appl. Sci. 2021, 11(11), 4993; https://doi.org/10.3390/app11114993
Received: 26 March 2021 / Revised: 23 May 2021 / Accepted: 25 May 2021 / Published: 28 May 2021
(This article belongs to the Special Issue Assessment of Landslide Susceptibility and Hazard in the Big Data Era)
The success rate in landslide susceptibility mapping efforts increased with the advancements in machine learning algorithms and the availability of geospatial data with high spatial and temporal resolutions. Existing data-driven susceptibility mapping models are not globally applicable due to the high variability of landslide conditioning parameters and the limitations in the availability of up-to-date and accurate data. Among numerous applications, landslide susceptibility maps are essential for site selection and health monitoring of engineering structures, such as dams, for increasing their lifetime and to prevent from disastrous events caused by the damages. In this study, landslide susceptibility mapping performance of XGBoost algorithm was evaluated in a landslide-prone area in the upper basin of Ataturk Dam, which is a prime investment located in the southeast of Turkey. The study area has a size of 2718.7 km2 with an elevation difference of ca. 2000 m and contains 27 lithological units. EU-DEM v1.1 from the Copernicus Programme was used to derive the geomorphological features. High classification accuracy with area under curve value of 0.96 could be obtained from the XGBoost algorithm. According to the results, the main factors controlling the landslides in the study area are the lithology, altitude and topographic wetness index. View Full-Text
Keywords: landslide susceptibility; XGBoost; Ataturk Dam; machine learning; EU-DEM landslide susceptibility; XGBoost; Ataturk Dam; machine learning; EU-DEM
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MDPI and ACS Style

Can, R.; Kocaman, S.; Gokceoglu, C. A Comprehensive Assessment of XGBoost Algorithm for Landslide Susceptibility Mapping in the Upper Basin of Ataturk Dam, Turkey. Appl. Sci. 2021, 11, 4993. https://doi.org/10.3390/app11114993

AMA Style

Can R, Kocaman S, Gokceoglu C. A Comprehensive Assessment of XGBoost Algorithm for Landslide Susceptibility Mapping in the Upper Basin of Ataturk Dam, Turkey. Applied Sciences. 2021; 11(11):4993. https://doi.org/10.3390/app11114993

Chicago/Turabian Style

Can, Recep, Sultan Kocaman, and Candan Gokceoglu. 2021. "A Comprehensive Assessment of XGBoost Algorithm for Landslide Susceptibility Mapping in the Upper Basin of Ataturk Dam, Turkey" Applied Sciences 11, no. 11: 4993. https://doi.org/10.3390/app11114993

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