Extended Abstract

Introduction

Landslides are a common geological hazard in hilly and mountainous terrains worldwide. They affect society and the livelihoods of the impacted communities. Landslides can lead to loss of life and cause considerable damage to infrastructure facilities, agricultural land, and public and private assets (Sujatha & Sridhar, 2021). Due to the interaction with other hazards and the spatially distributed nature of landslide events, it is essential to map susceptibility, particularly in areas that contain elements at risk (Bednarik et al. 2010). Landslide susceptibility can be assessed by applying a statistical model to records of past landslide occurrences along with a set of explanatory variables (Atkinson and Massari 2011). This study evaluated landslide susceptibility in the approximately 75 km² Goujebel Basin, located in the Ahar region of northwestern Iran. The basin is of particular importance for geomorphological hazard assessment because of the high frequency of landslides, the presence of landslide-susceptible geological formations, complex topographic conditions, and the major Tabriz–Ahar transportation corridor. The main objective of the study was to identify the key landslide conditioning factors and model landslide susceptibility using logistic regression within a GIS environment.

Methodology

In this study, logistic regression was employed as a multivariate statistical method to model landslide susceptibility. This method is widely used in landslide susceptibility studies because it can simultaneously analyze quantitative and qualitative variables, does not require normally distributed data, and effectively models binary outcomes. Eleven landslide conditioning factors, including elevation, slope, aspect, slope length, surface curvature, lithology, distance to faults, distance to streams, Topographic Wetness Index (TWI), Normalized Difference Vegetation Index (NDVI), and land use, were incorporated as independent variables. The landslide inventory layer was also defined as a binary dependent variable (presence or absence of landslides). The datasets used in this study included a geological map, an ASTER Digital Elevation Model (DEM), Sentinel-2 satellite imagery, Google Earth imagery, and field surveys. For model training and evaluation, the landslide inventory was randomly divided into training (70%) and testing (30%) datasets. Data processing and model implementation were performed in a GIS environment using TerrSet software. The model output was presented as a landslide susceptibility map classified into different susceptibility classes, and model performance was evaluated using the area under the receiver operating characteristic curve (AUC).

Results and discussion

The results indicated that landslides are among the most significant geomorphological processes in the Goujebel Basin. More than 40 landslides of varying sizes were identified across the study area, some of which remain active and have the potential for reactivation. Landslides are primarily concentrated in the middle reaches of the basin, highlighting the combined influence of topographic, geological, and hydrological factors on slope instability. From a topographic perspective, most landslides occurred at elevations between 1,550 and 1,750 m and on slopes ranging from 10 to 20%. These findings indicate that moderate slopes provide favorable conditions for landslides because of the greater thickness of weathered materials, the presence of water sources, and human activities. In addition, north and northwest facing slopes exhibited higher landslide susceptibility because of greater soil moisture and lower solar radiation. Analysis of surface curvature further revealed that hill slopes and transitional zones between plains and mountains exhibit the highest susceptibility to landslides. From a hydrological perspective, proximity to streams and moderate to high values of the Topographic Wetness Index (TWI) played important roles in increasing slope instability. Water infiltration and the resulting increase in pore-water pressure were identified as the primary triggering mechanisms for landslides in these areas. Among the geological factors, lithology was identified as the dominant conditioning factor controlling landslide occurrence. The PlQ-c formation, consisting of unconsolidated conglomerates with marl interbeds, exhibited the highest landslide susceptibility, with about 88% of the recorded landslides occurring within this unit. This finding highlights the dominant role of the geotechnical properties of geological materials in controlling slope instability. Distance to faults also played a moderate role in landslide occurrence and may act as an aggravating factor under seismic conditions. From a land-cover perspective, land use and vegetation cover played relatively limited roles in controlling landslide occurrence, probably because of the relative homogeneity of vegetation cover across the basin and the dominant influence of geological and topographic factors. The logistic regression results indicated that lithology (2.82), slope (1.89), and elevation (1.17) were the most influential landslide conditioning factors. The model achieved a high predictive performance, with an area under the receiver operating characteristic curve (AUC) of 0.93. The resulting landslide susceptibility map showed that approximately 35% of the basin falls within the high and very high susceptibility classes, which are mainly distributed in the middle and downstream parts of the basin and overlap with settlements, agricultural lands, and transportation infrastructure.

Conclusion

The results indicate that landslide susceptibility in the Goujebel Basin is controlled by a combination of topographic, geological, and hydrological factors, among which lithology, slope, and elevation are the most influential factors controlling slope instability. The concentration of landslides in mid-elevation zones, on moderate slopes, and within unconsolidated geological formations highlights the importance of geomorphological conditions and material properties in landslide occurrence. The results also indicate that a considerable portion of the basin falls within the high and very high landslide susceptibility classes, overlapping with settlements and transportation infrastructure and thereby increasing the area's vulnerability. The logistic regression model implemented within a GIS environment successfully identified landslide-prone areas with high predictive accuracy and provides a scientific framework for hazard management, land-use planning, and risk mitigation. The findings of this study can support management decision-making, sustainable regional development, and the reduction of geomorphological hazards.