Articles | Volume 13, issue 12
Nat. Hazards Earth Syst. Sci., 13, 3339–3355, 2013
https://doi.org/10.5194/nhess-13-3339-2013

Special issue: Progress in landslide hazard and risk evaluation

Nat. Hazards Earth Syst. Sci., 13, 3339–3355, 2013
https://doi.org/10.5194/nhess-13-3339-2013

Research article 19 Dec 2013

Research article | 19 Dec 2013

Assessing the spatial variability of coefficients of landslide predictors in different regions of Romania using logistic regression

M. C. Mărgărint1, A. Grozavu1, and C. V. Patriche2 M. C. Mărgărint et al.
  • 1Faculty of Geography and Geology, Alexandru Ioan Cuza University of Iaşi, Carol I 20A, Iaşi, Romania
  • 2Romanian Academy, Department of Iaşi, Geography Group, Romania

Abstract. In landslide susceptibility assessment, an important issue is the correct identification of significant contributing factors, which leads to the improvement of predictions regarding this type of geomorphologic processes. In the scientific literature, different weightings are assigned to these factors, but contain large variations. This study aims to identify the spatial variability and range of variation for the coefficients of landslide predictors in different geographical conditions. Four sectors of 15 km × 15 km (225 km2) were selected for analysis from representative regions in Romania in terms of spatial extent of landslides, situated both on the hilly areas (the Transylvanian Plateau and Moldavian Plateau) and lower mountain region (Subcarpathians). The following factors were taken into consideration: elevation, slope angle, slope height, terrain curvature (mean, plan and profile), distance from drainage network, slope aspect, land use, and lithology. For each sector, landslide inventory, digital elevation model and thematic layers of the mentioned predictors were achieved and integrated in a georeferenced environment. The logistic regression was applied separately for the four study sectors as the statistical method for assessing terrain landsliding susceptibility. Maps of landslide susceptibility were produced, the values of which were classified by using the natural breaks method (Jenks). The accuracy of the logistic regression outcomes was evaluated using the ROC (receiver operating characteristic) curve and AUC (area under the curve) parameter, which show values between 0.852 and 0.922 for training samples, and between 0.851 and 0.940 for validation samples. The values of coefficients are generally confined within the limits specified by the scientific literature. In each sector, landslide susceptibility is essentially related to some specific predictors, such as the slope angle, land use, slope height, and lithology. The study points out that the coefficients assigned to the landslide predictors through logistic regression are capable to reveal some important characteristics in landslide manifestation. The study also shows that the logistic regression could be an alternative method to the current Romanian methodology for landslide susceptibility and hazard mapping.

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