Volume 10, Issue 1 (5-2023)
Journal of Spatial Analysis Environmental Hazards 2023, 10(1): 127-142 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
ahmadi A. Landslide susceptibility assessment Using Shannon Entropy Model in Folded Zagros, Case Study: Varansara basin. Journal of Spatial Analysis Environmental Hazards 2023; 10 (1) :127-142
URL: http://jsaeh.khu.ac.ir/article-1-3195-en.html
URL: http://jsaeh.khu.ac.ir/article-1-3195-en.html
1- , a.ahmadi@razi.ac.ir
Abstract: (2297 Views)
Extended abstract
Landslide risk zoning is one of the basic measures to deal with and reduce the effects of landslides. Vernesara watershed is one of the areas where many landslides have been observed in different parts of it. In this research, in order to zone the risk of landslides using the entropy index, first the ranges of landslides were determined, then the effective factors in the occurrence of range movements were prepared in the ArcGIS software environment, and a landslide susceptibility map of the studied area was prepared. . The prioritization of effective factors using Shannon's entropy index showed that the slope layers, land use, surface curvature, topographic humidity index and topographic position index had the greatest effect on the occurrence of landslides in the region. Also, zoning landslide sensitivity with the mentioned model and evaluating its accuracy using the ROC curve shows the very good accuracy of the model (79.6 percent) with a standard deviation of 0.0228 for the studied area. The zoning map shows that the low-risk areas cover only 13% of the area and more than 56% of the area is in the area with high risk of landslides, which indicates the high potential of the area in the occurrence of landslides. . Construction at a distance from fault lines, waterways and the steep Asmari Formation and safety of communication routes are the most important measures to reduce the amount of damage caused by landslides in Vernesara watershed.
Key words: natural hazards, landslide, entropy, folded Zagros.
Landslide risk zoning is one of the basic measures to deal with and reduce the effects of landslides. Vernesara watershed is one of the areas where many landslides have been observed in different parts of it. In this research, in order to zone the risk of landslides using the entropy index, first the ranges of landslides were determined, then the effective factors in the occurrence of range movements were prepared in the ArcGIS software environment, and a landslide susceptibility map of the studied area was prepared. . The prioritization of effective factors using Shannon's entropy index showed that the slope layers, land use, surface curvature, topographic humidity index and topographic position index had the greatest effect on the occurrence of landslides in the region. Also, zoning landslide sensitivity with the mentioned model and evaluating its accuracy using the ROC curve shows the very good accuracy of the model (79.6 percent) with a standard deviation of 0.0228 for the studied area. The zoning map shows that the low-risk areas cover only 13% of the area and more than 56% of the area is in the area with high risk of landslides, which indicates the high potential of the area in the occurrence of landslides. . Construction at a distance from fault lines, waterways and the steep Asmari Formation and safety of communication routes are the most important measures to reduce the amount of damage caused by landslides in Vernesara watershed.
Key words: natural hazards, landslide, entropy, folded Zagros.
Type of Study: Research |
Subject:
Special
Received: 2021/01/5 | Accepted: 2023/06/20 | Published: 2023/10/4
Received: 2021/01/5 | Accepted: 2023/06/20 | Published: 2023/10/4
References
1. رجبی، معصومه، مهدی فیض اله پور. 1393. پهنه‌بندی لغزش‌های حوضه رودخانه گیوی چای با استفاده از مدل پرسپترون چندلایه از نوع پیش‌خور پس انتشار (BP). جغرافیا و توسعه، 36: 161-180.
2. گروه مطالعه امور زمین‌لغزش .1386. فهرست زمین‌لغزش‌های کشور. سازمان جنگل‌ها، مراتع و آبخیزداری کشور، معاونت آبخیزداری.
3. محمدخان، شیرین، عبدالکریم ویسی و کیوان باقری. 1393. پتانسیل سنجی خطر زمین‌لغزش با استفاده از مدل آنتروپی مطالعه موردی: (منطقه کوهستانی شیرپناه در جنوب غرب استان کرمانشاه). مجله جغرافیایی سرزمین، 44: 89-120.
4. Baeza, C.; and J. Corominas. 2001. Assessment of shallow landslide susceptibility by means of multivariate statistical techniques. Earth Surface Processes and Landforms: The Journal of the British Geomorphological Research Group, 26:1251-1263.
5. Bednarik, M.; B. Magulová, B, M. Matys and M. Marschalko.2010. Landslide susceptibility assessment of the Kraovany Liptovsk Mikul railway case study. Physics and Chemistry of the Earth.35: 162-171.
6. Brenning, A.; M. Schwinn, A.P. Ruiz-Páez and J. Muenchow. 2014. Landslide susceptibility near highways is increased by one order of magnitude in the Andes of southern Ecuador, Loja province. Natural Hazards and Earth System Sciences Discussions, 2: 1945-1975.
7. Chen, W.; W. Li, E. Hou, H. Bai, H. Chai, D. Wang and Q.Wang. 2015. Application of frequency ratio, statistical index, and index of entropy models and their comparison in landslide susceptibility mapping for the Baozhong Region of Baoji, China. Arabian Journal of Geosciences, 8: 1829-1841.
8. Chen, W.; H.R. Pourghasemi, M. Panahi, A. Kornejady, J. Wang, X. Xi and S. Cao. 2017. Spatial prediction of landslide susceptibility using an adaptive neuro-fuzzy inference system combined with frequency ratio, generalized additive model, and support vector machine techniques. Geomorphology, 297: 69-85.
9. Coelho-Netto, A. L.; A. de Souza Avelar and W.A. Lacerda. 2009. Landslides and disasters in southeastern and southern Brazil. Developments in Earth Surface Processes, 13: 223-243.
10. Davis, J and L. Blesius. 2015. A hybrid physical and maximum-entropy landslide susceptibility model. Entropy, 17: 4271-4292.
11. Devkota, K. C.; A.D. Regmi, H.R. Pourghasemi, K. Yoshida, B. Pradhan, I.C. Ryu and O.F. Althuwaynee. 2013. Landslide susceptibility mapping using certainty factor, index of entropy and logistic regression models in GIS and their comparison at Mugling–Narayanghat road section in Nepal Himalaya. Natural hazards, 65:135-165.
12. Felicísimo, Á. M.; A. Cuartero, J. Remondo and E. Quirós. 2013. Mapping landslide susceptibility with logistic regression, multiple adaptive regression splines, classification and regression trees, and maximum entropy methods: a comparative study. Landslides, 10: 175-189.
13. Guo, C.; D.R. Montgomery, Y. Zhang, K. Wang and Z.Yang. 2015. Quantitative assessment of landslide susceptibility along the Xianshuihe fault zone, Tibetan Plateau, China. Geomorphology, 248: 93-110.
14. Guzzetti, F.; A. Carrara, M. Cardinali, M and P. Reichenbach. 1999. Landslide hazard evaluation: a review of current techniques and their application in a multi-scale study, Central Italy. Geomorphology, 31: 181-216.
15. Hong, H.; H.R. Pourghasemi and Z.S. Pourtaghi. 2016. Landslide susceptibility assessment in Lianhua County (China): a comparison between a random forest data mining technique and bivariate and multivariate statistical models. Geomorphology, 259: 105-118.
16. Kanungo. D. P.; M.K. Arora and R.Gupta. 2009. Landslide Susceptibility Zonation (LSZ) Mapping - A Review. Journal of South Asia Disaster Studies. 1: 81- 105.
17. Kornejady, A.; M. Ownegh and A. Bahremand. 2017. Landslide susceptibility assessment using maximum entropy model with two different data sampling methods. Catena, 152: 144-162.
18. Jaafari, A.; A. Najafi, H.R. Pourghasemi, J. Rezaeian and A.Sattarian. 2014. GIS based frequency ratio and index of entropy models for landslide susceptibility assessment in the Caspian forest, northern Iran. International Journal of Environmental Science and Technology, 11: 909-926.
19. Obrien, R. M., 2007. A caution regarding rules of thumb for variance inflation factors. Quality & Quantity, 41: 673-690.
20. Ozdemir, A., 2011. GIS-based groundwater spring potential mapping in the Sultan Mountains (Konya, Turkey) using frequency ratio, weights of evidence and logistic regression methods and their comparison. Journal of Hydrology, 411: 290-308.
21. Palaz, E. B.; H.S. ATLI and S.A.R.I. Selver. 2020. Landslide Susceptibility Assessment by Information Entropy Model, Uzundere NE Turkey.UMTEB Intrnational Congress Vocational & Technical Sciences, 105-112.
22. Paoletti, V.; D. Tarallo, F. Matano and A. Rapolla . 2013. susceptibility zoning on seismicinduced landslides: An application to Sannio and Irpinia areas, Southern Italy. Physics and Chemistry of the Earth.63:147–159.
23. Park, N. W. 2015. Using maximum entropy modeling for landslide susceptibility mapping with multiple geoenvironmental data sets. Environmental Earth Sciences, 73: 937-949.
24. Pourghasemi, H. R and M. Rossi. 2016. Landslide susceptibility modeling in a landslide prone area in Mazandarn Province, north of Iran: a comparison between GLM, GAM, MARS, and M-AHP methods. Theoretical and Applied Climatology,74: 1-25.
25. Pourghasemi, H. R.; H.R. Moradi and S.F. Aghda. 2013. Landslide susceptibility mapping by binary logistic regression, analytical hierarchy process, and statistical index models and assessment of their performances. Natural hazards, 69: 749- 779.
26. Regmi, A. D.; K.C. Devkota, K. Yoshida, B. Pradhan, H.R. Pourghasemi, T. Kumamoto and A. Akgun. 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.
27. Romer, C.; and M. Ferentinou. 2016. Shallow landslide susceptibility assessment in a semiarid environmentˇ A Quaternary catchment of KwaZulu-Natal, South Africa. Engineering Geology, 201:29-44.
28. Shahabi, H.; S. Khezri, B.B. Ahmad and M.Hashim. 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.
29. Tay, L. T.; H. Lateh, M.K. Hossain and A.A. Kamil. 2014. Landslide hazard mapping using a poisson distribution: a case study in Penang Island, Malaysia. Springer,114: 521-525
30. Vittorio De Blasio, F. 2011. Introduction to the physics of landslides, Springer, 13, PP.1- 38.
31. Wan, S. 2009. A spatial decision support system for extracting the core factors and thresholds for landslide susceptibility map. Engineering Geology, 108: 237-251.
32. Wang, Q.; W. Li, Y. Wu, Y. Pei and P. Xie. 2016. Application of statistical index and index of entropy methods to landslide susceptibility assessment in Gongliu (Xinjiang, China). Environmental Earth Sciences, 75: 599.
33. Yufeng, S.; andJ. Fengxiang. 2009. Landslide stability analysis based on generalized information entropy. In Environmental Science and Information Application Technology. ESIAT International Conference, 2: 83-85.
Send email to the article author
| Rights and permissions | |
 | This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
