Landslide susceptibility mapping using logistic regression and frequency ratio approaches, case study from Souk Ahras region, N E Algeria

Abstract

Landslides are the most active process of soil erosion and landscape evolution; it is controlled by a set of non-linear factors. Therefore, a large amount of spatial data must be considered and analyzed in order to achieve some degree of prediction about it. Landslide susceptibility assessment (LSA) is carried out using various statistical modeling techniques among which figures the logistic regression (LR) and the Frequency Ratio (FR) models. This work allowed producing landslide susceptibility maps (LSMs) on a geographic information system (GIS) platform using LR and FR methods in the Northwest of Souk Ahras region, N E of Algeria. Landslide inventory map was established from visual interpretation of satellite images and field survey data. Slope instability phenomena in this region are related to a large variety of factors pertaining to the geological, geomorphological, hydrological and climate characteristics of the terrain. Consequently, a spatial database of seven causal factors were identified and used for predicting landslide prone areas. LSMs produced using LR and FR statistical models subdivided into five classes according to their degree of susceptibility to landslides: very low, low, moderate, high and very high. These rasters based LSMs was compared and verified with both training and testing inventory datasets. The AUC (area under the curve) was used for model evaluation. Results showed that the LR model provides a higher prediction accuracy of the LS mapping than the FR model with an AUC based on success rate equal 90.45 % and that based on prediction rate was 91.81 %. In addition, the results showed that about 30% to 37% of the study area was located in high and very high hazard classes. The resulting LSMs play an indispensable role in the region management and can be used in sustainable development planning.