The Application of GIS – Based Logistic Regression And Frequency Ratio Approaches For Landslide Susceptibility Assessment. A Case Study of Souk Ahras Region, N E Algeria.
Fatna Mahdadi 1*, Abederrahmane Boumezbeur 2.
1 Geology and Environment Laboratory, Department of Geology, University of Constantine1, Constantine, Algeria. [email protected]
2 Department of Geology, Sciences Faculty, University of Tebessa, Tebessa, Algeria.
Abstract
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 to produce a 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 raster 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 % (0.9045) and that based on prediction rate was 91.81 % (0.9181). 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.
Keywords: statistical modeling, geographic information system (GIS), landslide inventory, Souk Ahras region, landslide – related factors.
1 Introduction
Landslides are natural processes; they cause a great deal of damage to man and his environment especially in rapidly growing population areas of the less developed countries.
Recently with the development of computer technologies, GIS can play an important role in landslide prediction; it has a distinct advantage of storage, analyze and display of results in a large amount of data, either directly from the field or from remote sensing techniques, to predict the slope stabilities within the area.
In the literature, a various statistical methods were used in the field of LSA. Such techniques are logistic regression (Jacobs et al., 2018), analytical hierarchy process (Achour et al., 2017), weight of evidence (Teerarungsigul et al., 2016), frequency ratio (Youssef et al., 2015), and many more. These approaches have been successfully applied by several researchers such as Lee and Sambath, 2006; Pradhan et al., 2010; Greco and Sorriso-Valvo, 2013; Sivakami and Sundaram, 2014; Chen et al., 2016; Hadji et al., 2016, Le et al., 2017, using the GIS software for handling the geospatial database.
As a case study, a part of the northwest of Souk Ahras region, N E Algeria, which is one of the most areas exposed to landslide phenomenon in our country, was selected for LSA on a Pixel-based mapping unit.
Souk Ahras is a mountainous region, it known by the widespread occurrence of landslides. Their study requires that geomorphological, geological and hydro – climatic factors likely to affect the slope stability should be considered altogether at the same time with a characteristic weight for each factor.
For this study, 07 common causative parameters were produced for the LS analyses such as: slope angle, elevation, slope aspect, lithological units, distance to river, NDVI, and rainfall events, to prepare LSM using a LR and FR statistical approaches.
The accuracy of the LR and FR models was evaluated using the ROC (receiver operating characteristic) curve and the AUC (area under the curve) parameter. Data processing and modeling have been done using Arc Map 10.4 and XLSTAT – Pro 7.5 software. The results revealed that about 30 to 37 % of the study area was located in high and very high susceptibility classes. The resultant LSMs play an indispensable role in the region management and it can be used in sustainable development planning.
2 General Setting
Souk Ahras region is located in the extreme East of Algeria. It occupies an area of 4 360 km². In this work, the study area is located in the Northwest part of Souk Ahras region (figure 1). It was selected for landslide susceptibility assessment and the establishment of a susceptibility maps. It lies between latitude 36°11’6,16”N – 36°5’18,352”N and longitude 7°27’56,89”E – 7°18’54,91”E. It covers an area of 73 km2 (Fig. 1a). It is a mountainous region that is part of the Tellian mountain belt, with slopes ranging from 0° to more than 66°. The altitude decreases from northeast to southwest between the values of 675 m and 1283 m.
The climate is sub-humid Mediterranean type; characterized by a cold and wet winter against a hot and dry summer, with annual precipitation between a low of 428 mm to a high of 460 mm.
Geological study reveals that this region is essentially formed by sedimentary rocks (figure 2a).The upper Cretaceous formations represented by an alternation of limestone and marl – limestone. A predominantly marly Miocene cover, with some sandstone and conglomerate, the majority of the study area. The Plio – Quaternary constituted by alluvial deposits, sandstones, puddings and gravels.
