Volume 9, Issue 17 (9-2018)                   J Watershed Manage Res 2018, 9(17): 132-144 | Back to browse issues page

XML Persian Abstract Print

Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Arabameri A, Rezaei K, Shirani K, Mojtaba M. (2018). Identify Areas Susceptible to Landslides using new Synthetic Method Shannon’s Entropy Index-Information Value (Case Study: Sarkhon Watershed). J Watershed Manage Res. 9(17), 132-144. doi:10.29252/jwmr.9.17.132
URL: http://jwmr.sanru.ac.ir/article-1-631-en.html
Identify Areas Susceptible to Landslides using new Synthetic Method Shannon’s Entropy Index-Information Value (Case Study: Sarkhon Watershed)
Alireza Arabameri *1, Khalil Rezaei, Kourosh Shirani, Mojtaba Mojtaba
Abstract:   (3636 Views)

     The landslides impose serious damages to the economy, environment and human throughout the world. Identification of areas susceptible to landslides is necessary to avoid risks. In This research for Landslide hazard zonation in sarkhoon karoon watershed have been used Shannon’s Entropy Index and Information Value Methods. For this purpose, at first, landslide locations were identified using satellite images and field surveys and then landslide inventory map was created for study area. In the next step, 10 Effective Factors in Landslide occurrence include altitude, slope, aspect, distance from road, distance from fault, distance from river, lithology, land use, stream power index,  topography wetness index, Plane Curvature and Profile Curvature were identified and mentioned maps will be digitized in GIS.  In order to determine the weight of factors used Shannon’s Entropy Index and to determine the weight of classes used Information Value. The final Zonation map in the five classes include potential risk of very low, low, moderate, high and very high were prepared. The ROC (Receiver operating characteristic) curves and area under the curves (AUC) for landslide susceptibility map were constructed and the areas under curves were assessed for validation purpose and its value ​​showed that the hybrid model has a higher efficiency (0.781) for landslide hazard zonation. Results showed that land use and distance to road factors have the greatest impact on landslides. According to the results of landslide maps 14.45% (11220.4 ha) of the area are ranked as very dangerous areas and 6.11% (4744.1 ha) as dangerous areas.The results of this research can help planners to choose favorable locations for development schemes, such as infrastructural, buildings, road constructions, and environmental protection.
 

Keywords: Information Value, Landslide, Shannon’s Entropy Index, Sarkhon Watershed, Zonation
Full-Text [PDF 2653 kb]   (1132 Downloads)    
Type of Study: Research | Subject: ژئومورفولوژی و زمين شناسی
Received: 2016/05/18 | Accepted: 2017/05/3
References
1. ‎1. Arabameri, A.R. and A.H. Halabian. 2015. Landslide Hazard Zonation Using Statistical Model of AHP (Case ‎Study: Zarand Saveh Basin). Physical Geomorphology, 28: 65-86.‎
2. Arabameri, A.R. and K. Shirani. 2016. Identification of Effective Factors on Landslide Occurrence and its Hazard Zonation using Dempster-Shafer theory (Case study:Vanak Basin, Isfahan Province). Journal of Watershed Engineering and Management, 8(1): 93-106.
3. Ayalew, L. and H. Ymagishi. 2001. The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakud-Yahiko Mountains, Central Japan. Geomorphology, 65: 15-31. [DOI:10.1016/j.geomorph.2004.06.010]
4. Bednarik, M., B. Magulova, M. Matys and M. Marschalko. 2010. Landslide Susceptibility Assessment of the Kralˇovany-Liptovsky' Mikulaš Railway Case Study. Physics and Chemistry of the Earth, 35: 162-171. [DOI:10.1016/j.pce.2009.12.002]
5. Bui, D.T., B. Pradhan, O. Lofman, I. Revhaug and O.B. Dick. 2012. Landslide susceptibility assessment in the HoaBinh province of Vietnam: A comparison of the Levenberg-Marquardt and Bayesian regularized neural networks. Geomorphology, 172: 12-29. [DOI:10.1016/j.geomorph.2012.04.023]
6. Can, T., H.A. Nefeslioglu, C. Gokceoglu, H. Sonmez and Y. Duman. 2005. Susceptibility assessments of shallow earth flows triggered by heavy rainfall at three catchment's by logistic regression analysis. Geomorphology, 82: 250-271. [DOI:10.1016/j.geomorph.2005.05.011]
7. Constantin, M., M. Bednarik, M.C. Jurchescu and M. Vlaicu. 2011. Landslide susceptibility assessment using the bivariate statistical analysis and the index of entropy in the Sibiciu Basin (Romania). Environmental Earth Science, 63: 397-406. [DOI:10.1007/s12665-010-0724-y]
8. Demir, G., M. Aytekin, A. Akgun, S.B. Ikizler and O. Tatar. 2009. A comparison of landslide ‎susceptibility mapping of the eastern part of the North Anatolian Fault Zone (Turkey) by ‎likelihood-frequency ratio and analytic hierarchy process methods. Nat Hazards: 121-134.
9. Fatemiaghda, M., J. Ghiomian and A. Eshgheli Farahani. 2004. Evaluation efficiency statistics methods in determined Landslide hazard potential. Journal of Geosciences, 11: 28-47.
10. Guzzetti, F., A. Mondini, C. Cardinali, F. Fiorucci, M. Santangelo and K.T. Chang. 2012. Landslide inventory maps: New tools for an old problem. Earth-Science Reviews, 112: 42-66. [DOI:10.1016/j.earscirev.2012.02.001]
11. Greco, R., M. Sorriso-Valvo and E. Catalano. 2007. Logistic regression analysis in the evaluation of mass movement's susceptibility case study: Calabria. Italy. Engineering Geology, 89: 47-66. [DOI:10.1016/j.enggeo.2006.09.006]
12. Hansen, A. 1984. Engineering geomorphology: the application of an evolutionary model of ‎Hong Kong. Zeitschrift für Gomorphologie, 51: 39-50. ‎
13. Komac, M. 2006. A landslide susceptibility model using the Analytical Hierarchy Process method and multivariate statistics in perialpine Slovenia. Geomorphology, 74: 17-28. [DOI:10.1016/j.geomorph.2005.07.005]
14. Lee, S and T. Sambath. 2006. Landslide susceptibility mapping in the Damrei Romel area, Cambodia using frequency ratio and logistic regression models. Environmental Geology, 50: 847-855. [DOI:10.1007/s00254-006-0256-7]
15. Lee, S and K. Min. 2001. Statistical analysis of landslide susceptibility at Yongin, Korea. Environmental Geology, 40: 1095-1113. [DOI:10.1007/s002540100310]
16. Maleki, A and A. Ghorbanpour. 2008. Landslide hazard zonation In Chemleh Watershed of Songhour. Journal of Geographic and Development, (12): 181-198 (In Persian).
17. Mostafai, H., M. Onagh, M. Mesdaghi and M. Shariat Jafari. 2009. Comparison between efficiency of empirical and statistical model to landslide hazard zonation (Almootrood watershed). Soil and Water Conservation Research Journal. 16: 1-16 (In Persian).
18. Mohammadi, M., H. Moradi, S. Feiznia and H. Pourghasemi. 2009. Assessment of two variate Regresion and AHP methods efficiency for Land slide hazard zoning, (A Case study: Haraz watershed). Iranian Journal of Natural Resources, 62: 539-551 (In Persian).
19. Mohammadi, M., H. Moradi, S. Feiznia and H. Pourghasemi. 2009. Ranking of effective factors on Land slide and Land slide hazard zoning maps using two variate Regresion and AHP methods, (Case study: Haraz watershed). Journal of Earth Sciences, 74: 27-32 (In Persian).
20. Mohamadi, M., H. Moradi, S. Feiznia and H. Porghasemi. 2010. Efficiency of confidence operating model, the value of information and AHP in landslide hazard zonation in Haraz watershed. Iran Natural Resources Journal, 62: 539-551. (In Persian)
21. Nasrabadi, A.H., Sh. Shatai, M. Rafatnia and M. Shariat Jafari. 2008. Assessment of efficiency to information value statistics and area density models for landslide hazard zonation. Natural Resources and Agriculture Science Journal, 15: 23-34 (In Persian).
22. Neuhauser, B. and B. Terhorst. 2006. Landslide Susceptibility Assessment Using Weights-of-Evidence Applied to a Study Area at the Jurassic Escarpment (SWGermany). Geomorphology: 1-13. [DOI:10.1016/j.geomorph.2006.08.002]
23. Pradhan, B. and S. Lee. 2010. Landslide susceptibility assessment and factor effect analysis: back propagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modeling. Environmental Modeling and Software, 25: 747-759. [DOI:10.1016/j.envsoft.2009.10.016]
24. Pourghasemi, H.R., B. Pradhan, C. Gokceoglu and K. Deylami Moezzi. 2012. A comparative assessment of prediction capabilities of Dempster-Shafer and weights-of-evidence models in landslide susceptibility mapping using GIS.Geomatics. Natural Hazards & Risk, 5:23-43. (in Persian)
25. Rasai, A., K. Khosravi, M. Habibnejad Roshan, A. Heidari and A. Mashayekh Khan. 2015. Lnadslide Hazard Zonation using Multivariate Regression in GIS Environment (Case Study: Aghmashhad Watershed, Mazandaran). Journal of Watershed Management Research, 6(12): 205-2015.
26. Shannon, C.E. 1948. A mathematical theory of communication. Bulletin System Technology Journal, 27: 379-423. [DOI:10.1002/j.1538-7305.1948.tb01338.x]
27. Zare, M., A. Moghaddamnia, S. Tali Khoshk and H. Salmani. 2015. Landslide Hazard Assessment by using Neuro-Fuzzy Technique in VazWatershed, Journal of Watershed Management Research, 6(11): 101-110 ‎(In Persian)‎.
28. Zare, M., M.H. Jouri, D. Askarizadeh, T. Salarian and M. Fakhre Ghazi. 2016. An Evaluation of Landslide Hazard in Masooleh Watershed using Dempster-Shafer Theory and GIS. Journal of Watershed Management Research, 7(13): 2010-2017 ‎(In Persian)‎. [DOI:10.18869/acadpub.jwmr.7.13.217]
29. Zhu, C. and X. Wang. 2009. Landslide susceptibility mapping: A comparison of information and weights-of evidence methods in Three Gorges Area. International Conference on Environmental Science and Information Application Technology, 187: 342-346. [DOI:10.1109/ESIAT.2009.187]
Add your comments about this article : Your username or Email:
CAPTCHA
Send email to the article author

Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

© 2024 CC BY-NC 4.0 | Journal of Watershed Management Research

Designed & Developed by : Yektaweb