Evaluating the Vulnerability of Agricultural Land Use to the Landslide Risk in Rural Areas (Case Study: Tarom County)

Document Type : Original Article

Authors

Kharazmi University

10.22067/JRRP.V10I2.82863

Abstract

Purpose- Landslides are major hazards to human activities, which often wreak havoc on economic resources, damaging properties and facilities in rural areas. The present study, considering that a prerequisite of any development and planning is the recognition of the geographical features in an area, investigated the risk of landslide due to the expansion of agricultural land uses in rural areas.
Design/Methodology/Approach- This is applied research that sought to examine the research background and select the most appropriate methods. Accordingly, it adopted a mixture of quantitative methods (fuzzy Delphi and fuzzy best-worst method), GIS, and remote sensing techniques to achieve the research goal.
Findings- According to the research findings, with increasing height, slope, and vicinity to the fault lines, the risk of landslides rises in the study areas. These areas are mostly located in the highlands and the eastern and western regions, where rural areas are chiefly distributed. However, the majority of rural areas are distributed in the middle areas, which have better access to water resources and are in more favorable conditions due to topographic factors. Meanwhile, agricultural lands, due to the use of river water resources, have been distributed in the middle areas, which are classified as low-risk areas in terms of landslides. In contrast, due to the limited flatlands in highlands, agricultural gardens have developed in highlands with a moderate slope, which subsequently poses the risk of landslide. Therefore, the regular monitoring of land use development to increase the safety factor in new housing construction and agricultural lands is one of the planning requirements for land use development in mountainous rural areas.

Keywords


  1. Achour, Y., Boumezbeur, A., Hadji, R., Chouabbi, A., Cavaleiro, V., & Bendaoud, E. A. (2017). Landslide susceptibility mapping using analytic hierarchy process and information value methods along a highway road section in Constantine, Algeria. Arabian Journal of Geosciences, 10(8), 194. https://doi.org/10.1007/s12517-017-2980-6.
  2. Akgun, A. (2012). A comparison of landslide susceptibility maps produced by logistic regression, multi-criteria decision, and likelihood ratio methods: a case study at İzmir, Turkey. Landslides, 9(1), 93-106. https://doi.org/10.1007/s10346-011-0283-7.
  3. Ambrosi, C., Strozzi, T., Scapozza, C., & Wegmüller, U. (2018). Landslide hazard assessment in the Himalayas (Nepal and Bhutan) based on Earth-Observation data. Engineering Geology, 237, 217-228. https://doi.org/10.1016/j.enggeo.2018.02.020.
  4. Arab Ameri, A., Rezaei, Kh., & Shirani, K. (2018). Zoning and landslide risk assessment using models of reliability factor, surface density and hierarchical analysis (Case study: Vanak Basin, Isfahan Province). Journal of Geographical Space, 18 (62), 116-93. [In Persian] https://www.sid.ir/fa/journal/ViewPaper.aspx?ID=490212
  5. Bălteanu, D., Chendeş, V., Sima, M., & Enciu, P. (2010). A country-wide spatial assessment of landslide susceptibility in Romania. Geomorphology, 124(3-4), 102-112. https:// doi.org/ 10.1016/ j.geomorph. 2010.03.005.
  6. Basharat, M., Shah, H. R., & Hameed, N. (2016). Landslide susceptibility mapping using GIS and weighted overlay method: a case study from NW Himalayas, Pakistan. Arabian Journal of Geosciences, 9(4), 1-19. https://doi.org/10.1007/s12517-016-2308-y.
  7. Blahut, J., van Westen, C. J., & Sterlacchini, S. (2010). Analysis of landslide inventories for accurate prediction of debris-flow source areas. Geomorphology, 119(1-2), 36-51. https:// doi.org/ 10.1016/ j.geomorph.2010.02.017.
  8. Bui, D. T., Tuan, T. A., Klempe, H., Pradhan, B., & Revhaug, I. (2016). Spatial prediction models for shallow landslide hazards: a comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree. Landslides, 13(2), 361-378. https://doi.org/10.1007/s10346-015-0557-6.
  9. Chen, W., Pourghasemi, H. R., & Zhao, Z. (2017). A GIS-based comparative study of Dempster-Shafer, logistic regression and artificial neural network models for landslide susceptibility mapping. Geocarto International, 32(4), 367-385. https://doi.org/10.1080/10106049.2016.1140824.
  10. Chen, W., Shahabi, H., Shirzadi, A., Li, T., Guo, C., Hong, H., ... & Bin Ahmad, B. (2018). A novel ensemble approach of bivariate statistical-based logistic model tree classifier for landslide susceptibility Geocarto International, 33(12), 1398-1420. https:// doi.org/ 10.1080/ 10106049. 2018.1425738.
  11. Chen, W., Yan, X., Zhao, Z., Hong, H., Bui, D. T., & Pradhan, B. (2019). Spatial prediction of landslide susceptibility using data mining-based kernel logistic regression, naive Bayes and RBFNetwork models for the Long County area (China). Bulletin of Engineering geology and the Environment, 78(1), 247-266. https://doi.org/10.1007/s10064-018-1256-z.
  12. Choi, J., Oh, H. J., Lee, H. J., Lee, C., & Lee, S. (2012). Combining landslide susceptibility maps obtained from frequency ratio, logistic regression, and artificial neural network models using ASTER images and GIS. Engineering Geology, 124, 12-23. https://doi.org/10.1016/j.enggeo.2011.09.011.
  13. Das, I., Sahoo, S., van Westen, C., Stein, A., & Hack, R. (2010). Landslide susceptibility assessment using logistic regression and its comparison with a rock mass classification system, along a road section in the northern Himalayas (India). Geomorphology, 114(4), 627-637. https:// doi.org/ 10.1016/ j.geomorph. 2009.09.023.
  14. Das, I., Stein, A., Kerle, N., & Dadhwal, V. K. (2012). Landslide susceptibility mapping along road corridors in the Indian Himalayas using Bayesian logistic regression models. Geomorphology, 179, 116-125. https://doi.org/10.1016/j.geomorph.2012.08.004.
  15. Feizizadeh, B., & Blaschke, T. (2014). An uncertainty and sensitivity analysis approach for GIS-based multicriteria landslide susceptibility mapping. International Journal of Geographical Information Science, 28(3), 610-638. https://doi.org/10.1080/13658816.2013.869821.
  16. Ghimire, M. (2011). Landslide occurrence and its relation with terrain factors in the Siwalik Hills, Nepal: case study of susceptibility assessment in three basins. Natural hazards, 56(1), 299-320. https://doi.org/10.1007/s11069-010-9569-7.
  17. Goetz, J. N., Guthrie, R. H., & Brenning, A. (2011). Integrating physical and empirical landslide susceptibility models using generalized additive models. Geomorphology, 129(3-4), 376-386. https://doi.org/10.1016/j.geomorph.2011.03.001.
  18. Kanungo, D., Arora, M., Sarkar, S., & Gupta, R. (2012). Landslide Susceptibility Zonation (LSZ) Mapping–A Review.
  19. Kayastha, P., Dhital, M. R., & De Smedt, F. (2013). Evaluation and comparison of GIS based landslide susceptibility mapping procedures in Kulekhani watershed, Nepal. Journal of the Geological Society of India, 81(2), 219-231. https://doi.org/10.1007/s12594-013-0025-7.
  20. Kornejady, A., Ownegh, M., Rahmati, O., & Bahremand, A. (2018). Landslide susceptibility assessment using three bivariate models considering the new topo-hydrological factor: HAND. Geocarto International, 33(11), 1155-1185. https://doi.org/10.1080/10106049.2017.1334832.
  21. Lesiv, M., Laso Bayas, J. C., See, L., Duerauer, M., Dahlia, D., Durando, N., . . . Blyshchyk, V. (2019). Estimating the global distribution of field size using crowdsourcing. Global change biology, 25(1), 174-186. https://doi.org/10.1111/gcb.14492.
  22. Lin, L., Lin, Q., & Wang, Y. (2017). Landslide susceptibility mapping on a global scale using the method of logistic regression. Natural Hazards and Earth System Sciences, 17(8), 1411-1424. https://doi.org/10.5194/nhess-17-1411-2017.
  23. Mansooi, H., Vakili Ondrai, F., & Khatib, M. (2016). Landslide hazard zoning using AHP method and Boolean logic in Bagheran mountain (in the south of Birjand). Journal of New Findings in Applied Geology, 20, 61-49. [In Persian] https://nfag.basu.ac.ir/article_1692.html
  24. Meinhardt, M., Fink, M., & Tünschel, H. (2015). Landslide susceptibility analysis in central Vietnam based on an incomplete landslide inventory: Comparison of a new method to calculate weighting factors by means of bivariate statistics. Geomorphology, 234, 80-97. https:// doi.org/ 10.1016/ j.geomorph. 2014. 12.042.
  25. Moghimi, A., Bagheri Seyed Shokri, S., & Safar Rad, T. (1390/2012). Landslide risk zoning using entropy model (Case study: northwestern Zagros anticline). Journal of Natural Geography Research, 79, 90-77. [In Persian] https://jphgr.ut.ac.ir/article_24735.html
  26. Pirasteh, S., & Li, J. (2017). Probabilistic frequency ratio (PFR) model for quality improvement of landslide susceptibility mapping from LiDAR-derived DEMs. Geoenvironmental Disasters, 4(1), 19. https://doi.org/10.1186/s40677-017-0083-z.
  27. Pirasteh, S., Li, J., & Chapman, M. (2018). Use of LiDAR-derived DEM and a stream length-gradient index approach to investigation of landslides in Zagros Mountains, Iran. Geocarto International, 33(9), 912-926. https://doi.org/10.1080/10106049.2017.1316779.
  28. Pourghasemi, H. R., Mohammady, M., & Pradhan, B. (2012). Landslide susceptibility mapping using index of entropy and conditional probability models in GIS: Safarood Basin, Iran. Catena, 97, 71-84. https://doi.org/10.1016/j.catena.2012.05.005.
  29. Razak, K. A., & Mohamad, Z. (2015). Methodological framework for landslide hazard and risk mapping using advanced geospatial technologies. Paper presented at the Special Issue for the International Symposium on Multi-Hazard and Risk 2015 (ISMHR 2015), 23-24 March 2015, Universiti Teknologi Malaysia (UTM), Kuala Lumpur, Malaysia.
  30. Regmi, A. D., Devkota, K. C., Yoshida, K., Pradhan, B., Pourghasemi, H. R., Kumamoto, T., & Akgun, A. (2014). Application of frequency ratio, statistical index, and weights-of-evidence models and their comparison in landslide susceptibility mapping in Central Nepal Himalaya. Arabian Journal of Geosciences, 7(2), 725-742. https://doi.org/10.1007/s12517-012-0807-z.
  31. Rezaei, J. (2015). Best-worst multi-criteria decision-making method. Omega, 53, 49–57. https://doi.org/10.1016/j.omega.2014.11.009.
  32. Safari, A. (2014). Analysis and evaluation of landslide vulnerability in mountainous areas of Tehran. Journal of Spatial Analysis of Environmental Hazards, 1(3), 29-44. [In Persian] https://jsaeh.khu.ac.ir/article-1-2346-fa.html
  33. Saffari, A., & Hashemi, M. (2017). Zoning of landslide susceptibility using entropy and fuzzy logic models (Case study: Kermanshah city). Journal of Natural Geography, 9(34), 43-62. [In Persian] http://jopg.iaularestan.ac.ir/article_531681.html
  34. Shahabi, H., Hashim, M., & Ahmad, B. B. (2015). Remote sensing and GIS-based landslide susceptibility mapping using frequency ratio, logistic regression, and fuzzy logic methods at the central Zab basin, Environmental Earth Sciences, 73(12), 8647-8668. https://doi.org/10.1007/s12665-015-4028-0.
  35. Shahabi, H., Khezri, S., Ahmad, B. B., & Hashim, M. (2014). Landslide susceptibility mapping at central Zab basin, Iran: a comparison between analytical hierarchy process, frequency ratio and logistic regression models. Catena, 115, 55-70. https://doi.org/10.1016/j.catena.2013.11.014.
  36. Singh, K., Mehrotra, A., & Pal, K. (2014). Landslide detection from satellite images using spectral indices and digital elevation model. Disaster Adv, 7(6), 25-32.
  37. Skilodimou, H. D., Bathrellos, G. D., Chousianitis, K., Youssef, A. M., & Pradhan, B. (2019). Multi-hazard assessment modeling via multi-criteria analysis and GIS: a case study. Environmental Earth Sciences, 78(2), https://doi.org/10.1007/s12665-018-8003-4.
  38. Skilodimou, H. D., Bathrellos, G. D., Koskeridou, E., Soukis, K., & Rozos, D. (2018). Physical and anthropogenic factors related to landslide activity in the Northern Peloponnese, Greece. Land, 7(3), 85. https://doi.org/10.3390/land7030085.
  39. Statistic Center of Iran. (2011). General Population and Housing Census of Zanjan Province. Zanjan: Statistical Center of Iran. [In Persian]
  40. Trinh, T., Wu, D., Huang, J., Luu, B., Nguyen, K., & Le, H. (2016). Application of the analytical hierarchy process (AHP) for landslide susceptibility mapping: A case study in Yen Bai province, Viet Nam. Paper presented at the Environmental Technology and Innovations: Proceedings of the 1st International Conference on Environmental Technology and Innovations (Ho Chi Minh City, Vietnam, 23-25 November 2016). https://doi.org/10.1016/j.cageo.2012.11.003.
  41. Wan, S., Lei, T., & Chou, T. (2010). A novel data mining technique of analysis and classification for landslide problems. Natural Hazards, 52(1), 211. https://doi.org/10.1007/s11069-009-9366-3.
  42. Zhang, G., Cai, Y., Zheng, Z., Zhen, J., Liu, Y., & Huang, K. (2016). Integration of the statistical index method and the analytic hierarchy process technique for the assessment of landslide susceptibility in Huizhou, China. Catena, 142, 233-244. https://doi.org/10.1016/j.catena.2016.03.028.
  43. Zhang, J., Gurung, D. R., Liu, R., Murthy, M. S. R., & Su, F. (2015). Abe Barek landslide and landslide susceptibility assessment in Badakhshan Province, Afghanistan. Landslides, 12(3), 597-609. https://doi.org/10.1007/s10346-015-0558-5.
  44. Zumpano, V., Pisano, L., Malek, Ž., Micu, M., Aucelli, P. P., Rosskopf, C. M., ... & Parise, M. (2018). Economic losses for rural land value due to landslides. Frontiers in Earth Science, 6, 97. https://doi.org/10.3389/feart.2018.00097.