دوره 10، شماره 20 - ( پاییز و زمستان 1398 )                   جلد 10 شماره 20 صفحات 133-144 | برگشت به فهرست نسخه ها

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Abdollahi S, Pourghasemi H R, Ghanbarian G A, Safaeian R. Spatial Simulation and Land-subsidence Susceptibility Mapping Using Maximum Entropy Model. jwmr. 2019; 10 (20) :133-144
URL: http://jwmr.sanru.ac.ir/article-1-924-fa.html
عبداللهی سحر، پورقاسمی حمیدرضا، قنبریان غلامعباس، صفائیان روجا. شبیه‌سازی مکانی و تهیه نقشه حساسیت فرونشست زمین با استفاده از مدل بیشینه آنتروپی. پ‍‍ژوهشنامه مديريت حوزه آبخيز. 1398; 10 (20) :133-144

URL: http://jwmr.sanru.ac.ir/article-1-924-fa.html

بخش مهندسی منابع طبیعی و محیط زیست، دانشکده کشاورزی، دانشگاه شیراز
چکیده:   (463 مشاهده)
هدف از پژوهش حاضر شبیه‌سازی مکانی و تهیه نقشه حساسیت فرونشست زمین با استفاده از مدل‌ بیشینه آنتروپی در شهرستان‌های جیرفت و عنبرآباد است. بدین منظور ابتدا موقعیت فرونشست­ های زمین با استفاده از بازدیدهای گسترده میدانی مشخص و پس از آن نقشه پراکنش فرونشست­ زمین منطقه مورد مطالعه در محیط سامانه اطلاعات جغرافیایی (GIS) تهیه گردید. سپس هر یک از عوامل موثر بر وقوع فرونشست زمین از قبیل درصد و جهت شیب، طبقات ارتفاعی، انحنای نیم‌رخ، انحنای سطح، شاخص رطوبت توپوگرافی، فاصله از آبراهه، واحدهای سنگ‌شناسی، داده‌های پیزومتری، کاربری اراضی و شاخص تفاضلی پوشش گیاهی نرمال­شده (NDVI) در محیط GIS تهیه و بعد از آن با استفاده از روش نسبت فراوانی (FR) وزن طبقات مربوط به هر عامل مشخص شد. نهایتاً نقشه پهنه‌بندی حساسیت فرونشست زمین با استفاده از مدل­ بیشینه آنتروپی برای منطقه مورد مطالعه تهیه گردید. نتایج ارزیابی مدل­ با استفاده از 30 درصد نقاط استفاده­نشده در فرآیند شبیه‌سازی و بر اساس منحنی تشخیص عملکرد نسبی (ROC) نشان داد، نقشه­ حساسیت فرونشست زمین تهیه­شده با استفاده از مدل­ بیشینه آنتروپی صحت بالایی (859/0) دارد. بنابراین نقشه پهنه­ بندی مذکور می­تواند نقش به ­سزایی در تعیین مناطق بحرانی از نظر بهره‌برداری آب و تخریب سفره‌های آب زیرزمینی در منطقه مورد مطالعه داشته باشد.
متن کامل [PDF 1257 kb]   (143 دریافت)    
نوع مطالعه: پژوهشي | موضوع مقاله: بلايای طبيعی (سيل، خشکسالی و حرکت های توده ای)
دریافت: 1396/11/17 | ویرایش نهایی: 1398/10/24 | پذیرش: 1397/6/5 | انتشار: 1398/10/24

فهرست منابع
1. Agrawal, D., J.K. Singh and A. Kumar. 2005. Maximum Entropy-based Conditional Probability Distribution Runoff Model. Biosystems Engineering, 90: 103-113. [DOI:10.1016/j.biosystemseng.2004.08.003]
2. Azarbagh, H. 2014. Evaluation of the amount of soil restoration in the formation and development of gaps in Jiroft plain. Master's dissertation, Islamic Azad University Zahedan, Faculty of Sciences, 87 pp (In Persian).
3. Berry, M.J. and G.S. Linoff. 2004. Data mining techniques. 2nd Edition. John Wiley and Sons.
4. Chen, W., H.R. Pourghasemi and S.A. Nagibi. 2017. A comparative study of landslide susceptibility maps produced using support vector machine with different kernel functions and entropy data mining models in China. Bulletin of Engineering Geology and the Environment, 1-18. [DOI:10.1007/s10064-017-1010-y]
5. Elmizadeh, H. 2011. Morphological analysis and gradient in relation to erosion (Case study: Basin Nechi). Geographic Information Magazine, 80: 79-83 (In Persian).
6. Ercanoglu, M. and C. Gokceoglu. 2002. Assessment of landslide susceptibility for a landslide prone area (North of Yenice, NW Turkey) by fuzzy approach. Environmental Geollogy, 41: 720-730. [DOI:10.1007/s00254-001-0454-2]
7. Jahani, S. and M. Delbrie. 2009. Estimation estimation of the maximum 24-hour rainfall in Golestan province. Water Engineering Magazine, 13-22 (In Persian).
8. Khosravi, K., H.R. Pourghasemi, K. Chapi and M. Bahri. 2016. Flash flood susceptibility analysis and its mapping using different bivariate models in Iran: a comparison between Shannon's entropy, statistical index, and weighting factor models. Environ Moint Assess, 188: 1-21. [DOI:10.1007/s10661-016-5665-9]
9. Komac, M. 2006. A landslide susceptibility model using the analytical hierarchy process method and multivariate statistics in perialpine sloveni. Geomorphology, 74: 17-28. [DOI:10.1016/j.geomorph.2005.07.005]
10. Lee, S., I. Park and T.K. Choi. 2012. Spatial prediction of ground subsidence susceptibility using an artificial neural network. Environmental Management, 49: 347-358. [DOI:10.1007/s00267-011-9766-5]
11. Li, Z., H. Zhou and Y. Xu. 2013. Research on prediction model of support vector machine based land subsidence caused by foundation pit dewatering. Advanced Materials Research, 105-108. [DOI:10.4028/www.scientific.net/AMR.671-674.105]
12. Lubis, A., T. Sato, N. Tomiyama, N. Isezaki and T. Yamanokuchi. 2011. Ground subsidence in Semarang-Indonesia investigated by ALOS-PALSAR satellite SAR interferometry. Journal of Asian Earth Sciences, 40: 1079-1088. [DOI:10.1016/j.jseaes.2010.12.001]
13. Minasny, B., A. Mcbratney and S. Blanes. 2008. Quantitative models for pedogenesis-a review. Geoderma, 144: 140-157. [DOI:10.1016/j.geoderma.2007.12.013]
14. Moghimi, A. and S. Negahban. 2012. Investigation of erosion in the watershed of Shahr Fadami River using entropy model. Journal of Natural Geography Researches, 33: 1-16 (In Persian).
15. Mohammad Khan, S.H, A. Wisey and K. Bagheri. 2014. Landslide hazard potential using entropy model, case study (Shirpanah mountainous area in southwest of Kermanshah province). Geographic Quarterly of Territory, 44: 89-103 (In Persian).
16. Ozdemir, A. 2016. Investigation of sinkholes spatial distribution using the weights of evidence method and GIS in the vicinity of Karapinar (Konya, Turkey). Geomorphology, 245: 40-50. [DOI:10.1016/j.geomorph.2015.04.034]
17. Park, I., J. Lee and S. Lee. 2014. Insemble of ground subsidence hazard maps using fuzzy logic. Center European Journal of Geosciences, 6: 207-218. [DOI:10.2478/s13533-012-0175-y]
18. Pourghasemi, H.R. and M. Beheshtirad. 2015. Assessment of a data-driven evidential belief function model and GIS for groundwater potential mapping in the Koohrang Watershed, Iran. Geocarto International, 30: 662-685. [DOI:10.1080/10106049.2014.966161]
19. Pourghasemi, H.R. and N. Kerle. 2016. Random forests and evidential belief function- based landslide susceptibility assessment in Western Mazandaran Province, Iran. Environmental Earth Sciences, 75: 1-17. [DOI:10.1007/s12665-015-4950-1]
20. Pourghasemi, H.R., H.R. Moradi and S.M. Fatemi Aghda, C. Gokceoglu and B. Pradhan. 2014. GIS-based landslide susceptibility mapping with probabilistic likelihood ratio and spatial multi-criteria evaluation models (North of Tehran, Iran). Arabian Journal of Geosciences, 7: 1857-1878. [DOI:10.1007/s12517-012-0825-x]
21. Pourghasemi, H.R., M. Mohammady and B. Pradhan. 2012. Landslide susceptibility mapping using index of entropy and conditional probability models in GIS: Safarood Basin, Iran. Catena, 97: 71-84. [DOI:10.1016/j.catena.2012.05.005]
22. Pourghasemi, H.R., S. Yousefi, A. Kornejady and A. Cerdi. 2017. Performance assessment of individual and ensemble data-mining techniques for gully erosion modeling. Science of the Total Environment, 609: 764-775. [DOI:10.1016/j.scitotenv.2017.07.198]
23. Pourhasemi, H.R., H.R. Moradi, M. Mohammadi, R. Mostafazadeh and A.S. Golijerandeh. 2013. Landslide hazard zonation using the Bayesian theory. Journal of Science and Technology of Agriculture and Natural Resources, Water and Soil Sciences, 62: 109-121 (In Persian).
24. Pradhan, B. 2011. Manifestation of an advanced fuzzy logic model coupled with geoinformation techniques coupled with geoinformation techniques for landslide susceptibility analysis. Environmental and Ecological Statistics, 18: 471-493. [DOI:10.1007/s10651-010-0147-7]
25. Pradhan, B., M.H. Abokharima, N.M. Jebur and M. Shafapour Tehrany. 2014. Land subsidence susceptibility mapping at Kinta valley (Malaysia) using the evidential belief function model in GIS. Natural Hazards, 73: 1019-1042. [DOI:10.1007/s11069-014-1128-1]
26. Rahmati, O. and H.R. Pourghasemi. 2017. Identification of critical flood prone areas in data-scarce and ungauged regions: A comparison of three data mining models. Water Resour Manage, 31: 1473-1487. [DOI:10.1007/s11269-017-1589-6]
27. Rahmati, O., H.R. Pourghasemi and A.M. Melesse. 2016. Application of GIS-based data driven random forest and maximum entropy models for groundwater potential mapping: A case study at Mehran Region, Iran. Catena, 137: 360-372. [DOI:10.1016/j.catena.2015.10.010]
28. Rahnama, H. and S. Mirasi. 2016. Analysis and Evaluation of Parameters Effecting on Earth Survival. Journal of Civil Engineering, 1: 45-53 (In Persian).
29. Rodriguez-Galiano, V. and M. Chica-Olmo. 2012. Land cover change analysis of a Mediterranean area in Spain using different sources of data: multi-seasonal Landsat images, land surface temperature, digital terrain models and texture. Applied Geography, 35: 208-218. [DOI:10.1016/j.apgeog.2012.06.014]
30. Saffari, A., F. Jafari and S.M. Tavakolisabour. 2016. Monitoring its land subsidence and its relation to groundwater extraction, Case study: Karaj-Shahriar Plain. Quantitative Geomorphology Researches, 2: 82-93 (In Persian).
31. Shadfar, S., A. Nasiri, S. Chitgar and A.S. Ahmadi. 2015. The zoning of the risk of land subsidence using the Analytical Hierarchy Process (AHP) method, the study area (Boeinzahra City). Geographical Quarterly of the Territory, Scientific-Research, 48: 101-116 (In Persian).
32. Sharificia, M. 2012. Determination of the magnitude and extent of ground subsidence by the radar interferometry (D-InSAR) method in the Nough-Bahraman plain. Planning and Approach Space, 3: 56-77 (In Persian).
33. Soleimani, K., J. Zandi and M. 2013. Habibnejad Roshan. Evaluation of the efficiency of frequency ratio methods, bivariate statistics in the preparation of a landslide sensitivity map (case study: Mazandaran Vazrood watershed). Engineering geology and environment, 94: 41-50 (In Persian).
34. Tihansky, A.B. 1999. Sinkholes, West-Central Florida, 121-141.
35. USGS (United States Geological Survey). 2011. Research and review information located, Assess on September http://water.usgs.gov/ogw/pubs/fs00165.
36. Ustun, A., E. Tusat. S. Yalvac. I. Ozkan. Y. Eren and A. Odemir. 2015. Land subsidence in Konya closed basin and its spatio-temporal detection by GPS and DINSAR. Environmental Earth Sciences, 73: 6691-6703. [DOI:10.1007/s12665-014-3890-5]
37. Yesilnacar, E.K. 2005. The application of computational intelligence to landslide susceptibility mapping in Turkey. Ph.D Thesis, Department of Geomatics the University of Melbourne, 423 pp.
38. Yilmaz, I. 2007. GIS based susceptibility mapping of karst depression in gypsum: A case study from Sivas basin (Turkey). Engineering Geology, 90: 89-103. [DOI:10.1016/j.enggeo.2006.12.004]
39. Yilmaz, I. 2013. Marschalko M, Bednarik, M. An assessment on the use of bivariate, multivariate and soft computing techniques for collapse susceptibility in GIS environ. Journal of Earth System Science, 122: 371-388. [DOI:10.1007/s12040-013-0281-3]
40. Yin, J., D. Yu and R. Wilby. 2016. Modelling the impact of land subsidence on urban pluvial flooding; Acase study of downtown Shanghai, China. Science of the Total Environment, 544: 744-753. [DOI:10.1016/j.scitotenv.2015.11.159]
41. Zare, M., H.R. Pourghasemi, M. Vafakhah and B. Pradhan. 2013. Landslide susceptibility mapping at Vaz watershed (Iran) using an artificial neural network model: a comparison between multi-layer perceptron (MLP) and radial basic function (RBF) algorithms. Arabian Journal of Geoscience, 6: 2873-2888. [DOI:10.1007/s12517-012-0610-x]
42. Zongji, Y., J. Qiao and X. Zhang. 2010. Regional landslide zonation based on entropy method in three gorges area, China. Seventh International Conference on Fuzzy System and Knowledge Discorery, (FSKD 2010).

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