###### Abstract  
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 km\({}^{2}\) 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.  
l 20  
The landslide susceptibility (LS) maps serve to the detection of landslide-prone areas even if no activity could be observed, and help the decision-makers to take the preventive measures. The LS assessment models take the conditioning parameters into account together with existing landslide inventories, which may be difficult to extract in settlement areas and heavy construction sites [6]. Due to the requirement of manual efforts and high level of expertise for the landslide inventory preparation, these maps may be incomplete in areas with low accessibility and having poor or no data. Production of a full landslide inventory map with accurate temporal dimension is still difficult [7], and incomplete landslide inventory maps may lead to serious uncertainties [8].  
The main aim of this study was to evaluate the prediction performance of a recently developed ML algorithm, extreme gradient boosting (XGBoost) [9], for the LS mapping of upper basin of Ataturk Dam. In a Web of Science (search topic = \"landslide susceptibility\" and title = \"Turkey\") search by the time of this writing; approximately one hundred studies regarding LS mapping in Turkey have been found. However, most of them focused on the north and northeast part of Turkey. Accessibility, terrain characteristics and data availability played in general a key role in the study site selection. The EU-DEM v1.1 [10] provided via the  
Figure 1: The location, the digital elevation model (DEM) in hillshade representation and the landslide inventory of the study area.  
Copernicus Land Monitoring Service [11] of European Union's Earth Observation Programme was employed to extract the geomorphological characteristics of the study area since it is suitable for regional use, highly accurate (\(\pm 7\) m vertical accuracy) and freely available.