Landslide database construction is one of the most crucial stages of the landslide susceptibility mapping studies.Although there are many techniques for preparing landslide database in the literature,representative data selection from huge data sets is a challenging,and,to some extent,a subjective task.Thus,in order to produce reliable landslide susceptibility maps,data-driven,objective and representative database construction is a very important stage for these maps.This study mainly focuses on a landslide database construction task.In this study,it was aimed at building a representative landslide database extraction approach by using Chebyshev theorem to evaluate landslide susceptibility in a landslide prone area in the WesternBlack Sea region of Turkey.The study area was divided into two different parts such as training(Basin 1) and testing areas(Basin 2).A total of nine parameters such as topographical elevation,slope,aspect,planar and profile curvatures,stream power index,distance to drainage,normalized difference vegetation index and topographical wetness index were used in the study.Next,frequency distributions of the considered parameters in both landslide and nonlandslide areas were extracted using different sampling strategies,and a total of nine different landslide databases were obtained.Of these,eight databases were gathered by the methodology proposed by this study based on different standard deviations and algebraic multiplication of raster parameter maps.To evaluate landslide susceptibility,Artificial Neural Network method was used in thestudy area considering the different landslide and nonlandslide data.Finally,to assess the performance of the so-produced landslide susceptibility map based on nine data sets,Area Under Curve(AUC approach was implemented both in Basin 1 and Basin2.The best performances(the greatest AUC values were gathered by the landslide susceptibility map produced by two standard deviation databas extracted by the Chebyshev theorem,as 0.873 and0.761,respectively.Results revealed that th methodology proposed by this study is a powerful and objective approach in landslide susceptibility mapping. Landslide database construction is one of the most crucial stages of the landslide susceptibility mapping studies. Although there are many techniques for preparing landslide database in the literature, representative data selection from huge data sets is a challenging, and, to some extent, a subjective task .Thus, in order to produce reliable landslide susceptibility maps, data-driven, objective and representative database construction is a very important stage for these maps. This study mainly focuses on a landslide database construction task. In this study, it was aimed at building a representative landslide database extraction approach by using Chebyshev theorem to evaluate landslide susceptibility in a landslide prone area in the Western Black Sea region of Turkey. The study area was divided into two different parts such as training (Basin 1) and testing areas (Basin 2) . A total of nine parameters such as topographical elevation, slope, aspect, planar and profile curvatures, stream power index, distance to drainage, normalized difference vegetation index and topographical wetness index were used in the study .Next, frequency distributions of the considered parameters in both landslide and nonlandslide areas were extracted using different sampling strategies, and a total of nine different landslide databases were obtained. Of these, eight databases were gathered by the methodology proposed by this study based on different standard deviations and algebraic multiplication of raster parameter maps. To evaluate landslide susceptibility, Artificial Neural Network method was used in the study area considering the different landslide and nonlandslide data. to assess the performance of the so-produced landslide susceptibility map based on nine data sets, Area Under Curve (AUC approach was implemented both in Basin 1 and Basin 2. best performances (the greatest AUC values ​​were gathered by the landslide susceptibility map produced by two standard deviation databas extracted by the Chebyshev theoremm, as 0.873 and 0.761, respectively. Results revealed that th methodology proposed by this study is a powerful and objective approach in landslide susceptibility mapping.