Journal of Natural Environment

Evaluating the role of geomorphic characteristics in landslide vulnerability and sensitivity

Document Type : Research Paper

Authors

1 Department of Arid and Mountainous Regions Reclamation, Faculty of Natural Resources, University of Tehran, Karaj, Iran.

2 Department of Engineering and Studies of the General Department of Natural Resources and Watershed of Lorestan, Iran.

10.22059/jne.2024.376982.2675

Abstract

Landslides, as a natural hazard and geomorphic disturbance, have numerous direct and indirect impacts on human communities, the natural environment, and landscape transformation, and can lead to significant human and financial losses.Therefore, identifying the factors influencing vulnerability to this hazard is essential for effective management. The aim of this study is to identify the factors affecting land susceptibility to landslides and to prepare a susceptibility map by integrating machine learning methods and statistical modeling. In this research, the Random Forest algorithm was used to determine the weight and importance of influencing factors including slope, aspect, topographic position index, topographic wetness index, Plan curvature, lithology, distance from faults, distance from stream, distance from roads, and land use. Subsequently, Artificial Neural Networks (ANN) and Maximum Entropy (MaxEnt) models were applied to model and predict landslide-prone areas. To evaluate model performance, validation was conducted in an adjacent area to the Kakashraf watershed, and relevant evaluation indices were calculated. The results indicated that distance from faults and slope were the most significant factors influencing landslide susceptibility. According to the Area Under the Curve (AUC), the ANN model (0.92) had higher predictive accuracy than the MaxEnt model (0.801). The most vulnerable areas were found within 200 meters of stream, near fault lines, and with slopes between 20% and 40%. Therefore, land use management in such areas should be prioritized. These findings can contribute to improved land planning, natural resource protection, and effective landslide risk management.

Keywords

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Journal of Natural Environment
Volume 77, Issue 4
March 2025
Pages 731-747