Evaluation of the landslide susceptibility map obtained by a GIS matrix method: a case of Al Hoceima city (northern Morocco)

This work presents a method for preparing landslide susceptibility maps based on an analysis between past movements and predisposing factors. The proposed methodology for determining susceptibility is an adaptation of the matrix method in the territory of the city of Al Hoceima (Northern Morocco). The data needed to evaluate vulnerability has based on the landslide inventory and the available cartographic documents. These data were prepared and integrated into a GIS. This phase of the data preparation has followed by assessment and susceptibility mapping. The obtained map was validated and compared with an independent landslide dataset than the one used to develop the model. The results of the ROC (receiver operating characteristic) curve demonstrate the good predictive ability (AUC = 0.94). We can deduce that the map correlates well with the existing terrain conditions. The successfully validated prediction will enable decision-makers to offer handy information for infrastructure construction and urban planning in the future or other areas with similar situations

Validation of landslide susceptibility maps (Case study: Urban area of the municipality of Banja Luka - B&H)

Landslides represent a serious geo-hazard in many areas of the world. They are one of the most damaging and most significant geo-hazards in Bosnia and Herzegovina. In the previous research, three GIS-based methodologies (Index based method, Statistical index method, and Landslide susceptibility analysis), have been used to assess the landslide susceptibility in urban area of the Municipality of Banja Luka. Validation technique is performed by comparing existing landslide data from 2012 with obtained landslide susceptibility maps. In this research, the landslide susceptibility maps were supplemented by landslide susceptibility map which is prepared using a GIS Matrix method. The area and percentage distribution of the susceptibility classes in the study area were determined as a result of the four different techniques. The Statistical index method has provided the most satisfying results. As a consequence of heavy rains during the period from May to August 2014, 126 landslides occurred in the study area, and they offer a good opportunity to validate obtained landslide susceptibility maps of the study area with landslides that occurred in different periods of time. Validation was performed by using a ?degree of fit? method. The values of the degree of fit in high and very high category in all used methods are over 80%, except for GIS matrix method, where the percentage is 60%. The validation of obtained landslide susceptibility maps suggests that the applications of all the used techniques provide a good basis for creation landslide susceptibility maps, but the best results are given by using a Statistical index method.

Factors selection in landslide susceptibility modelling on large scale following the gis matrix method: application to the river Beiro basin (Spain)

A procedure to select the controlling factors connected to the slope instability has been defined. It allowed us to assess the landslide susceptibility in the Rio Beiro basin (about 10 km2) over the northeastern area of the city of Granada (Spain). Field and remote (Google EarthTM) recognition techniques allowed us to generate a landslide inventory consisting in 127 phenomena. To discriminate between stable and unstable conditions, a diagnostic area had been chosen as the one limited to the crown and the toe of the scarp of the landslide. 15 controlling or determining factors have been defined considering topographic, geologic, geomorphologic and pedologic available data. Univariate tests, using both association coefficients and validation results of single-variable susceptibility models, allowed us to select the best predictors, which were combined for the unique conditions analysis. For each of the five recognised landslide typologies, susceptibility maps for the best models were prepared. In order to verify both the goodness of fit and the prediction skill of the susceptibility models, two different validation procedures were applied and compared. Both procedures are based on a random partition of the landslide archive for producing a test and a training subset. The first method is based on the analysis of the shape of the success and prediction rate curves, which are quantitatively analysed exploiting two morphometric indexes. The second method is based on the analysis of the degree of fit, by considering the relative error between the intersected target landslides by each of the different susceptibility classes in which the study area was partitioned. Both the validation procedures confirmed a very good predictive performance of the susceptibility models and of the actual procedure followed to select the controlling factors.