Landslide Susceptibility Assessment for Maragheh County, Iran, Using the Logistic Regression Algorithm

Abstract

Landslide susceptibility assessment is the globally approved procedure to prepare geo-hazard maps of landslide-prone areas, which are highly used in urban management and minimizing the possible disasters due to landslides. Multiple approaches to providing susceptibility maps for landslides have one specification. Logistic regression is a statistical-based model that investigates the probabilities of the events which is received extensive success in landslide susceptibility assessment. The presented study attempted to use a logistic regression application to prepare the Maragheh County hazard risk map. In this regard, several predisposing factors (e.g., elevation, slope aspect, slope angle, rainfall, land use, lithology, weathering, distance from faults, distance from the river, distance from the road, and distance from cities) are identified as main responsible for landslide occurrence and 20 historical sliding events which used to prepare hazard risk maps. As verification, the models were controlled by operating relative characteristics (ROC) curves which reported the overall accuracy for susceptibility assessment. According to the results, the region is located in a moderate to high-hazard risk zone. The north and northeast parts of Maragheh County show high suitability for landslides. Verification results of the model indicated that the AUC estimated for the training set is 0.885, and the AUC estimated for the testing set is 0.769. To justify the model, the results of the LR were comparatively checked with several benchmark learning models. Results indicated that LR model performance is reasonable.

Utilizing Hybrid Machine Learning and Soft Computing Techniques for Landslide Susceptibility Mapping in a Drainage Basin

“…Moreover, triggering factors inform land use planning and zoning regulations, ensuring that areas prone to landslides are designated for less critical purposes, thereby diminishing exposure to risk. Finally, they inform the development of tailored mitigation strategies, such as enhanced drainage systems or slope stabilization, based on the specific triggering factors prevalent in an area [20]. In essence, triggering factors are the foundation upon which informed decision making, risk mitigation, and community safety in landslide-prone regions are built.…”

“…The assessment of landslide risk methods can be broadly categorized into three primary approaches, i.e., qualitative, quantitative, and semi-quantitative. Qualitative approaches often rely on aerial imagery, field interpretation, and expert/engineering judgment [20][21][22][23].Despite the logical outcomes and high performance of various models, geologists always seek new methods for more precise identification of landslide-prone areas and the creation of reliable maps required for environmental planning. Therefore, introducing a novel approach based on artificial intelligence algorithms, deep learning, and remote sensing (RS) and geographic information systems (GIS) techniques for landslide modeling is of paramount importance in landslide risk management [24].…”

Generative probabilistic prediction of precipitation induced landslide deformation with variational autoencoder and gated recurrent unit

“…Zhang et al (2022) applied Bayesian approach to optimize the hyper-parameters of Random Forest algorithm to predict the deformation in reservoir landslides. Cemiloglu et al (2023) applied logistic regression algorithm to assess the probability of landslide occurrences. Mao et al (2024) explored several machine learning algorithms to map the susceptibility of landslides in the drainage basin.…”

Application of the principal component analysis–cloud model in the assessment of the seismic stability of slopes

“…As the main type of landslide in the western mountain area of China, the earthquake landslide is a kind of main secondary disasters triggered by a strong earthquake that has the characteristics of wide distribution, strong burst, and large quantity; it often causes road disruption, river blockage, house collapse, lifeline project damage, etc. So, it seriously hinders the rescue work after the earthquake and aggravates the impact of earthquake disasters (Azarafza et al, 2021;Nanehkaran et al, 2021;Cemiloglu et al, 2023;Nanehkaran et al, 2023).…”

“…In total, 48 people died in landslides triggered by the earthquake (Nanehkaran et al, 2023). In addition, in China, there are also many examples of earthquake-induced landslides, one notable instance being the large-scale landslide caused by the MS8.5 earthquake in Haiyuan, Ningxia Province, on 16 December 1920 (Cemiloglu et al, 2023). The landslide covered an area of 31 km 2 .…”