Journal of Geographical Studies of Mountainous Areas

Journal of Geographical Studies of Mountainous Areas

Landslide risk zoning in Aliabad Katul County

Document Type : Original Article

Authors
Department of Physical Geography, Faculty of Social Sciences, University of Mohaghegh Ardabili, Ardabil, Iran.
Abstract
Introduction
A landslide, as a type of slope movement, refers to the downward movement of a mass of rock, debris, and soil on a slope under the influence of gravity. The formation of landslide risk depends on multiple factors, including slope, geology, tectonic activity, earthquakes, weathering, climatic conditions, hydrological conditions, vegetation cover, land use type, and human factors. Landslides are one of the most important environmental hazards, the occurrence of which not only causes huge losses to human society and economic development but also poses a serious threat to the environment. Today, considering the destructive effects of landslides, it is necessary to investigate the effective factors and identify areas prone to this hazard using zoning techniques to achieve solutions to control this phenomenon and select the most appropriate and practical management option. Aliabad Katul County, located in Golestan Province, has always been at risk of landslides due to its specific climatic, physiographic, geological, and human conditions. Because no study has been conducted to date to zone Aliabad Katul County in terms of landslide risk, and considering that landslides are among the hazards that have multiple options and criteria, and the study of this hazard using multi-criteria decision-making methods has received much attention from researchers today, in the present study, the zoning of Aliabad Katul County against landslide risk has been considered using the new Marcos multi-criteria algorithm.
 
2. Methodology
The present study, considering the nature of the problem and the subject under study, is of a research-applied type, and its research method is an analysis based on the integration of data analysis, geographic information systems, and the use of multi-criteria analysis techniques. ENVI, Ecognition, ArcGIS, Idrisi, and Excel software were used for image processing and data analysis. Considering that landslides occur under the influence of several factors, identifying the factors effective in the occurrence of landslides is of great importance. First, after reviewing similar scientific research in the field of the subject, conducting field observations, and considering the natural and human conditions of the region, 9 factors of DEM, slope, aspect, geology, distance from the fault, land use, precipitation, distance from communication road, and distance from the river were identified as effective factors in creating landslide risk in Aliabad Katul County. In the next stage, information layers related to each factor were prepared in the geographic information system environment. The weighting of the factors studied was done according to the CRITIC method, and the final analysis was done using the MARCOS multi-criteria method. After preparing the landslide susceptibility map, the accuracy of the models was examined using the ROC curve.
 
3. Results
According to the landslide hazard zoning map, 170.612 square kilometers of the county have a very low potential, 262.922 square kilometers have a low potential, and 277.032 square kilometers have a medium potential. The high-risk and very high-risk classes cover 253.622 square kilometers and 151.902 square kilometers of the county area, respectively. Matching the distribution of landslide points and areas at risk based on the zoning map obtained from the study indicates that the largest number and percentage of landslide surfaces are located in the two very high-risk (30.43 percent of landslide points) and high-risk (63.89 percent of landslide points) classes. In addition, the medium-risk class also includes 19.57 percent of the landslide areas of the county, and in the two low-risk and very low-risk classes, the distribution of landslide points is not observed. In addition, the results of this study showed that slope, land use, and precipitation factors with weight coefficients of 0.153, 0.142, and 0.138 are the most important factors affecting the occurrence of instability in Aliabad-Katul County. Also, considering the use of the Rock Curve method, the accuracy of the Marcus method in identifying areas prone to landslide risk in Aliabad-Katul County with an area under the curve (0.89) is very good.
 
4. Discussion
The results of this study indicate the high potential of this county in terms of the possibility of landslides. In general, in high-risk and very high-risk areas, the spatial distribution of factors is such that it provides suitable conditions for landslides to occur, which include the dominance of slopes with medium to steep slopes (15 to 80), altitudes of 1000 to 3000 meters, abundant rainfall, diverse land uses (forest cover, pastures, agricultural and residential), trenching and destruction of the slopes following road construction and development activities, susceptible geological formations (alternation of limestone, marl, shale and sandstone layers), undercutting of the slope support by flowing water, fault structures and slope direction (especially; north, west, east, southeast and northwest directions).
 
5. Conclusion
Finally, it should be noted that given the large area of landslide-prone areas in Aliabad Katul County, expert protection, watershed management, and management measures should be taken. It can also be acknowledged that the results of this research have a practical aspect and can be used as a more powerful tool for risk management and reducing losses and casualties from the environmental hazard of landslides by stakeholders and organizations such as the Crisis Management Organization, the General Directorate of Natural Resources and Watershed Management, the General Directorate of Roads and Urban Development, the Regional Water Organization, and other organizations related to environmental hazard issues.
Author Contributions
In the preparation and writing of this article, all author has contributed equally and jointly. All stages of the research, from study design and data collection to analysis of results and final writing of the article, are the result of collaboration and collective agreement of all authors.
 
Data Availability Statement
Data available on request from the authors.
 
Acknowledgments
This article is an extract from a research project funded by the Vice Chancellor for Research and Technology of the University of Mohaghegh Ardabili. Therefore, we express our gratitude and appreciation.
 
 
Ethical Considerations
All authors affirm that this research was conducted in accordance with ethical standards, with no data fabrication, falsification, or plagiarism.
 
Funding
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
 
Conflict of Interest
The author declares no conflict of interes
Keywords

Abdulla, A., Baryannis, G., & Badi, I. 2023. An integrated machine learning and MARCOS method for supplier evaluation and selection. Decision Analytics Journal, 9, 100342, 1-11. https://doi.org/10.1016/j.dajour.2023.100342
Abedini, M., Mozafari, H. & Faalnaziri, M. 2022. Investigating and comparing the effectiveness of information value models and frequency ratio coefficient and Shannon's entropy in zoning rock fall risk (case study: Zanjan-Teham-Taram road). Journal of Geographical Studies of Mountainous Areas, 3(1), 55-75. https://doi.org/10.52547/gsma.3.1.5 (In Persian).
Ahmadpour, A., Ghaforpur Anbaran, P. & Babayyan, H. R. 2024. Landslide and Rockfall susceptibility zoning in the area of Kerend-e Gharb and Sarpol –e Zahab (the axis of Pataq pass) in Kermanshah province. Journal of Geographical Studies of Mountainous Areas, 5(2), 39-58. https://doi.org/10.22034/gsma.2024.715552  (In Persian).
Arshad, MW., Mesran, M., Setiawansyah, S., Ryan Randy Suryono, RR., & Rahmanto, Y (2023). Combination of CRITIC Weighting Method and Multi-Atributive Ideal-Real Comparative Analysis in Staff Admissions. Journal of Computer Science and Information Technology, 2 (4), 77-86. https://doi.org/10.47065/explorer. v4i2.1428
Asghari Saraskanroud, S., & Piroozi, E .2024. Identification and Zoning of Areas Prone to the Occurrence of Landslides Using the Aras Multi-Criteria Analysis Method (Study Area: Qaranqoochay Watershed in the Southeast of East Azarbaijan Province). Geography and Environmental Planning, 35(3), 65-94. https://doi.org/10.22108/gep.2024.140985.16396 (In Persian).
Asghari saraskanroud, S., & Piroozi, E. 2022. Comparative evaluation of WLC, OWA, VIKOR, and MABAC multi-criteria decision-making methods in landslide risk zoning Case study: Givi-chay watershed of Ardabil province. Physical Geography Research, 54(1), 65-94. https://doi.org/10.22059/jphgr.2022.333658.1007656 (In Persian).
Asghari Sareskanrood, S., Mohammadzadeh Shishegaram, M., & Asghari Sareskanrood, S (2022). Zoning and estimation of range movements in Hashtroud city using radar interferometry and MABAC model. Environmental Management Hazards, 9(2), 133-150. https://doi.org/10.22059/jhsci.2022.346994.736 (In Persian).
Badola, SH., Mishra, V.N., Parkash, S., & Pandey, M. 2023. Rule-based fuzzy inference system for landslide susceptibility mapping along national highway 7 in Garhwal Himalayas, India. Quaternary Science Advances, 11, 1-12. https://doi.org/10.1016/j.qsa.2023.100093
Bhattacharyya, R., & Mukherjee, S. 2021. Fuzzy Membership Function Evaluation by Non-LinearRegression: An Algorithmic Approach. Fuzzy infomation and engineering, 12 (4), 412–434. https://doi.org/10.1080/16168658.2021.1911567
Caleca, F., Scaini, C., Frodella, W., & Tofani, V. 2024. Regional-scale landslide risk assessment in Central Asia. Nat. Hazards Earth Syst. Sci., 24, 13–27. https://doi.org/10.5194/nhess-24-13-2024.
Entezari, M., Esteki, S., & Gholamhaydari, H. 2024. Investigation of State of Landslide in Tarom Watershed Using Risk-Vulnerability Superimposed Model. Journal of Geography and Planning, 28(89), 61-39. https://doi.org/10.22034/gp.2023.54607.3073 (In Persian).
Esfandyari Darabad, F., Rostami, G., Mostafazadeh, R. & Abedini, M. 2024. Spatial assessment and zoning of landslide risk in Zamkan watershed using support vector machine and logistic regression. Hydrogeomorphology, 11(40), 123-102. https://doi.org/10.22034/hyd.2024.61467.1737 (In Persian).
Esposito, G., Carabella, C., Paglia, G., & Miccadei, E. 2021. Relationships between Morphostructural/Geological Framework and Landslide Types: Historical Landslides in the Hilly Piedmont Area of Abruzzo Region (Central Italy). Land, 10(3), 287, 1-28. https://doi.org/10.3390/land10030287
Golipour, S., Hosseinzadeh, S. R. & pourali, M. 2022. Identifying landslide prone slopes and classification of its types using logistic regression model and fuzzy logic (Case study: Ghahremanlou Catchment, North Khorasan Province). Quantitative Geomorphological Research, 11(1), 209-228. https://doi.org/10.22034/gmpj.2022.336132.1343 (In Persian).
Jafarzadeh Ghoushchi, S., Shaffiee Haghshenas, S., Memarpour Ghiaci, A. Guido, G., & Vitale, A. 2023. Road safety assessment and risks prioritization using an integrated SWARA and MARCOS approach under spherical fuzzy environment. Neural Comput & Applic, 35, 4549–4567. https://doi.org/10.1007/s00521-022-07929-4
Javan, F., Atashbahar, R. and Motalebpoor, A. (2025). Zoning of Environmental Hazards in Tourism Destinations with Emphasis on Flooding (Case Study: Sarvabad County, Kurdistan Province). Journal of Environmental Research in Mountainous Regions, 1(3), 43-56. https://doi.org/10.22034/ermr.2025.144141.1025 (In Persian).
Keshavarz, S. R., Bayati Eshkaftaki, J. & Almodaresi, S. A. 2022. Spatial analysis of the amount of landslides using radar interferometric technique in order to reduce hazards (Study area: Sarbaz area in Isfahan province). Environmental Management Hazards, 9(3), 271-288. https://doi.org/10.22059/jhsci.2023.347893.741 (In Persian).
Kumar, A., Sharma, R., & Bansal, V. 2022. Spatial Prediction of Landslide Hazard using GIS-multi-criteria Decision Analysis in Kullu District of Himachal Pradesh, India. Journal of Mining and Environment, 13(4), 943-956. https://doi.org/10.22044/jme.2022.12235.2222 .
Madadi, A. & piroozi, E. 2023. Landslide risk zoning in the upstream basin of Yamchi Dam in Ardabil province, using multi-criteria decision making methods MARCOS and CODAS. Quantitative Geomorphological Research, 12(1), 73-94. https://doi.org/10.22034/gmpj.2023.370812.1390 (In Persian).
Madadi, A., Piroozi, E. & Faal Naziri, M. 2021. A Comparative Evaluation of MABAC and CODAS Multi-Criteria Decision Algorithms in Landslide Risk Zoning (Case Study: Kowsar County). Geography and Environmental Planning, 31(4), 1-24. https://doi.org/10.22108/gep.2020.124723.1348. (In Persian).
Mahmoody-Vanolya, N., Argany, M. & Jelokhani-Niaraki, M. 2021. Multi-hazard potential mapping of Mazandaran province using multi-criteria spatial decision analysis. Environmental Management Hazards, 8(4), 395-411. https://doi.org/10.22059/jhsci.2022.332933.686 (In Persian).
Materazzi, M., Bufalini, M., Gentilucci, M., Pambianchi, G., Aringoli, D., & Farabollini, P. 2021. Landslide hazard assessment in a monoclinal setting (Central Italy): Numericalvs, geomorphological approach. Land, 10 (6), 624, 1-22. https://doi.org/10.3390/land10060624.
Nanehkaran, Y. A., Chen, B., Cemiloglu, A., Chen, J., Anwar, S., Azarafza, M., & Derakhshani, R. 2023. Riverside Landslide Susceptibility Overview: Leveraging Artificial Neural Networks and Machine Learning in Accordance with the United Nations (UN) Sustainable Development Goals. Water, 15(15), 2707, 1-28. https://doi.org/10.3390/w15152707.
Nasiri, M., Mohammadzade, M., lotfalian, M., & Parsakhoo A. 2022. Zoning and Field Study of Landslid es along Forest Roads of Darabkola-Sari. J Watershed Manage Res. 13(26), 105-114. https://doi.org/10.52547/jwmr.13.26.105 (In Persian).
Ou, L., Huang, C. & Cao, Y. 2024. Research on landslide hazard assessment based on improved analytic hierarchy process optimizing multiple rainfall indicators. Discov Appl Sci, 6 (409), 1-16. https://doi.org/10.1007/s42452-024-06119-2.
Rostamizad, G., & Dastranj, A. 2024. Evaluating the sensitivity of the landslide event using the support vector machine algorithm. Water and Soil Management and Modelling, 4(4), 299-312. https://doi.org/10.22098/mmws.2023.13934.1379 (In Persian).
Sadeghi, H. and Javan, F. (2024). The Evaluation of Tourist Villages of Iran in terms of Geophysical Vulnerability using Fuzzy Scenarios. Journal of Rural Research, 15(4), 85-100. https://doi.org/10.22059/jrur.2024.383580.1993 (In Persian).
Sadeghi, H. and Javan,F. (2025). Vulnerability of Iranian tourism villages in terms of Landslide hazard using GIS. Geography, 23(84), 153-170. https://doi.org/10.22034/jiga.2025.2055364.1385 (In Persian).
Saha, A., Villuri, VGK., Bhardwaj, A., & Kumar, S.A. 2023. Multi-Criteria Decision Analysis (MCDA) Approach for Landslide Susceptibility Mapping of a Part of Darjeeling District in North-East Himalaya, India. Applied Science, 13(8), 5062, 1-23. https://doi.org/10.3390/app130850629.
Sharifi, H., Ramazanipore, M., Ebrahimi, L. & Haghzad, A. 2022. Landslide hazard zoning of Noor city using network analysis model. Economic Geography Research, 2(6), 40-55. https://dor.org/20.1001.1.27173747.1400.2.6.4.0. (In Persian).
Sim, K.B., Lee, M.L. & Wong, S.Y. A. 2022. review of landslide acceptable risk and tolerable risk. Geoenviron Disasters, 9 (3), 1-17. https://doi.org/10.1186/s40677-022-00205-6.
Singh, U., Nandan, R., & Tiwari, A. 2024. Recent Trends and Techniques in Landslide Hazard Assessment. Qeios,4, 1-12.https://doi.org/10.32388/LBYEQN .
Tesfa, CH., & Sewnet, D. 2024. GIS-based MCDM approach for landslide hazard zonation mapping in east Gojjam zone, central Ethiopia. Quaternary Science Advances, 15 (10), 1-11. https://doi.org/10.1016/j.qsa.2024.100210.
Vojtekova, J., & Vojtek, M. 2020. Assessment of landslide susceptibility at a localspatial scale applying the multi-criteria analysisand GIS: a case study from Slovaki, Geomatics. Natural Hazards and Risk, 11 (1), 131–148. https://doi.org/10.1080/19475705.2020.1713233.
Zivari, R. 2015. Karst Development Potential in Khosh yeilagh Carbonate Formation in Aliabad Katul area, Golestan Province, Master's thesis, Faculty of Earth Sciences, Applied Geology, Kharazmi University. (In Persian) https://irandoc.ac.ir/