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اولویت بندی عامل های مؤثر بر وقوع زمین لغزش و پهنه بندی حساسیت آن در آبخیز چریک آباد ارومیه با استفاده از مدل آنتروپی شانون | ||
| پژوهش های آبخیزداری | ||
| مقاله 4، دوره 33، شماره 4 - شماره پیاپی 129، دی 1399، صفحه 30-46 اصل مقاله (2.42 M) | ||
| نوع مقاله: پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22092/wmej.2020.128407.1280 | ||
| نویسندگان | ||
| عبدالعزیز حنیفی‌نیا1؛ حبیب نظرنژاد* 2؛ سعید نجفی3؛ آیدینگ کرنژادی4 | ||
| 1دانشجوی کارشناسی ارشد آبخیزداری، دانشگاه ارومیه | ||
| 2دانشیار دانشکده کشاورزی و منابع طبیعی دانشگاه ارومیه | ||
| 3استادیار دانشکده کشاورزی و منابع طبیعی دانشگاه ارومیه | ||
| 4دانش آموخته دکتری آبخیزداری، دانشگاه علوم کشاورزی و منابع طبیعی گرگان | ||
| چکیده | ||
| زمینلغزشها از رخدادهای طبیعی و برخی انسانساخت‌اند. ایجاد یک راه‌برد برای کاهش زیانهای ناشی از زمینلغزشها برای حفظ منابعطبیعی و انسانی، ضروری است. هدف از این پژوهش، اولویتبندی عاملهای مؤثّر بر زمینلغزشها با مدل آنتروپی شانون و روش GIS است. با بازدیدهای میدانی و تصویرهای گوگل ارث 90 نقطهی لغزشی در آبخیز چریکآباد ارومیه شناسایی شد. لایههای بارش، ارتفاع، درصد شیب، جهت شیب، سنگشناسی، کاربری زمین، شاخص پوششگیاهی تفاضلی بهنجارشده (NDVI) و عناصر خطی مانند فاصله از آبراه، فاصله از گسل، فاصله از جاده و فاصله از روستا عاملهای مؤثّر بر وقوع زمینلغزش بهکارگرفتهشد و نقشهی پراکنش زمینلغزشها در سامانهی اطلاعات جغرافیایی تهیه و رقومی شد. اولویتبندی عاملهای مؤثّر با روش آماری آنتروپی شانون نشان داد که بهترتیب لایههای کاربری زمین، فاصله از آبراه و فاصله از گسل بیشترین تأثیر را بر وقوع زمینلغزشها داشت و کمترین تأثیر مربوط به لایههای فاصله از روستا، فاصله از جاده است. پهنهبندی حساسیت زمینلغزش با مدل و ارزیابی دقت آن با منحنی عملکرد نسبی سامانه (ROC) و با سطح زیر منحنی (AUC) 0/879 بیانگر دقت خیلیخوب مدل برای منطقه بود. با توجه به این‌که حدود 32 % از آبخیز در منطقه‌هایی با حساسیت زیاد و خیلی زیاد قرار گرفته است، پیشنهاد میشود برای کاهش نسبی خطر لغزش در آن‌جا، از تغییر کاربری در منطقه‌های مستعد زمینلغزش اجتناب گردد. | ||
| کلیدواژه‌ها | ||
| آبخیز چریک آباد؛ آنتروپی شانون؛ زمین لغزش؛ سامانه اطلاعات جغرافیایی؛ منحنی عملکرد نسبی سامانه | ||
| عنوان مقاله [English] | ||
| Prioritization of Effective Factors on Landslide Occurrence and Mapping of its Sensitivity in CherikAbad Watershed, Urmia Using Shannon Entropy Model | ||
| نویسندگان [English] | ||
| Abdulaziz Hanifinia1؛ Habib Nazarnejad2؛ Saeed Najafi3؛ Aiding Kornejady4 | ||
| 1M.Sc. Student of Watershed Management, Urmia University | ||
| 2Associate Professor, Faculty of Agriculture & Natural Resources, Urmia University | ||
| 3Assistant Professor, Faculty of Agriculture & Natural Resources, Urmia University | ||
| 4Ph.D. of Watershed Management, Gorgan University of Agricultural Sciences & Natural Resources | ||
| چکیده [English] | ||
| Landslides are natural and incidentally man-made phenomena. Developing landslide risk reduction plans in order to preserve the natural and human resources are pivotal. This study sets out to prioritize landslide-controlling factors by using the Shannon’s entropy model and the GIS techniques. A total of 90 landslides were identified using extensive filed surveys and interpreting the Google Earth images. A Landslide inventory map was prepared in the GIS environment together with eleven controlling factors as inputs, namely precipitation, elevation, slope percentage, slope aspect, lithology, land use/land cover, the normalized difference vegetation index (NDVI), and the distance to streams/faults/roads/villages. The results revealed that the land use/land cover, the distance to streams, and the distance to faults were the top-ranked factors of the highest importance in landslide occurrence, while the distance to villages, and the distance to roads were of the least importance. Further, the model validation results attested to the great performance of the adopted model where the area under the receiver operating characteristic curve (AUROC) was 0.879. Considering that about 32% of the watershed is located in the high and very high sensitive area, it is recommended to avoid land use change in landslide-prone areas to reduce relative risk of landslide. | ||
| کلیدواژه‌ها [English] | ||
| Cherikabad Watershed, GIS, landslide, ROC curve, Shannon’s entropy | ||
| مراجع | ||
|
Abedini M, Tulabi S. 2018. Assessing LNRF, FR, and AHP models in landslide susceptibility mapping index: A comparative study of Nojian watershed in Lorestan province, Iran. Environmental Earth Sciences. 77(11): 1–13. Ahmadi H. 2012. Applied geomorphology. University of Tehran, 688 p. (In Persian). 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(1): 217–228. Arabameri A, Rezaei K, Shirani K, Mojtaba M. 2000. Identify areas susceptible to landslides using new synthetic method Shannon’s entropy index-information value (Case study: Sarkhon watershed). Journal of Watershed Management Research. 9 (17):132–144. (In Persian). Binesh N, Sarang A. 2014. Effect of physiographic characteristics of flood basin on flood hydrograph; Comparison of three damavand, Verdij and Casilian basins. Water Management. 3(3):65 (In Persian). Chen HX, Zhang LM, Gao L, Zhu H, Zhang S. 2015. Presenting regional shallow landslide movement on three-dimensional digital terrain. Engineering Geology. 195(1): 122–134. Crosta G.B. 2004. Introduction to the special issue on rainfall-triggered landslides and debris flows. Engineering Geology. 73: 191–192. Devkota KC, Regmi AD, Pourghasemi HR, Yoshida K, Pradhan B, Ryu IC, Dhital MR, Althuwaynee OF. 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. National Hazards. 65(1):135–165. Ebadi F, Ildoromi AR, Nouri H, Babaei Kh. 2019. Landslide hazard zoning at the Cheragh Veis Dam watershed of Kurdestan using the IRAT and LNRF models. Watershed Management Researches Journal. 32(3):37–52. (In Persian). Entezari M, Jalilian T. 2019. Ranking sub watersheds based on landside hazard in Kermanshah Province (Iran) using ELECTRE I. Hydrology journal. 5(18): 19–38. (In Persian). Gholami M, Ghanavati E, Ahmadabadi A. 2019. Landslide susceptibility mapping of Kan using index of entropy and LSM. Quantitative Geomorphological Research. 8(1):16–33 (In Persian). Hong H, Chen W, Xu C, Youssef A.M, Pradhan, B, Tien Bui D. 2017. Rainfall-induced landslide susceptibility assessment at the Chongren area (China) using frequency ratio, certainty factor, and index of entropy. Geocarto International. 32(2): 139–154. Hosmer DW, Lemeshow S. 2000. Applied logistic regression, 2nd edn. Wiley, New York, 373 pp Hungr O 1997 some methods of landslides hazard intensity mapping. In: Cruden D, Fell R (eds) Landslide risk assessment. Balkema, Rotterdam. 215–226 p. jafari H. 2017. Investigating the role of faults in the morphology of the northern plains of Zanjanrood. Geography and Environmental Planning. 28(1): 35–48. (In Persian). Joybari J, Kavian A, Mosaffaie J. 2017. Effect of land use on landslide movement in the Tavan District, Qazvin. Watershed Management Researches Journal. 30(3):29–39. (In Persian). 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. Liu X, Zhao C, Zhang Q, Peng J, Zhu W, Lu Z. 2018. Multi-Temporal loess landslide inventory mapping with C-, X-and L-Band SAR Datasets- A case study of Heifangtai loess landslides, China. Remote Sensing. 10(11): 1756–1765. Mohammadkhan Sh, Veisi A, Bagheri K. 2015. Feasibility of landslide risk using entropy model, Case study: (Shirpnah mountainous region in south west Kermanshah Province). Geographical Journal of Territory. 11(44): 89–103. (In Persian). Mohammadnejad Arough V, Asghari Sereskanrood S. 2018. Landslide risk assessment using statistical methods in Barandoz Chai basin. Quantitative Geomorphological Research. 4(4): 181–191. (In Persian). Mohammadnia M, Fallah G. 2018. Landslides susceptibility mapping using fuzzy logic and AHP. Researches in Geographical Sciences.18 (48):115–130. (In Persian). Pham, BT, Jaafari A, Prakash, I, Bui, DT. 2019. A novel hybrid intelligent model of support vector machines and the MultiBoost ensemble for landslide susceptibility modeling. Bulletin of Engineering Geology and the Environment. 78(4): 2865–2886. Pontius Jr RG, Schneider LC. 2001. Land-cover change model validation by an ROC method for the Ipswich watershed, Massachusetts, USA.Agriculture, Ecosystems & Environment.85(1–3): 239–248. Pourghasemi HR, Pradhan B, Gokceoglu C. 2012. Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran. Natural Hazards. 63(2): 965–996. RahmanZandi R, Amirahmadi A, Mohamadnia M. 2018. Use of entropy model to assess the risk of landslide in the proposed road route of Torqabeh-Dorood (Mashhad-Neyshabur). Geography and Human Relationships. 1(2):37–58. (In Persian). Ramakrishna D, Ghose MK, Chandra RV, Jeyaram A. 2005. Probabilistic techniques, GIS and remote sensing in landslide hazard mitigation: a case study from Sikkim Himalayas, India. Geocartography Int. 20 (4): 53–58. Regmi NR, Giardino JR, Vitek JD, Dangol V. 2010. Mapping landslide hazards in Western Nepal: Comparing qualitative and quantitative approaches. Environmental & Engineering Geoscience. 16(2): 127–142. Saber Chenari K, Sheikh VB, Salmani H. 2016. Assessment of LNRF model in landslide Hazard mapping using GIS at Ziarat watershed, Gorgan. Watershed Management Researches Journal. 29(3):14–23. (In Persian). Shaik I, Rao SK, Penta B. 2019. Detection of landslide using high resolution satellite data and analysis using entropy. In proceedings of international conference on remote sensing for disaster management. Springer, Cham. pp. 243–250. Shannon CE. 1948. A mathematical theory of communication. Bulletin System Technol. pp. 379–423. Sharma LP, Patel N, Ghose MK, Debnath P. 2010. Influence of Shannon’s entropy on landslide-causing parameters for vulnerability study and zonation-a case study in Sikkim, India. Arabian Journal of Geosciences. 5(3): 421–431. Sharma S, Mahajan, AK. 2019. A comparative assessment of information value, frequency ratio and analytical hierarchy process models for landslide susceptibility mapping of a Himalayan watershed, India. Bulletin of Engineering Geology and the Environment. 78(4): 2431–2448. shirani K. 2017. Modelling of landslide susceptibility zonation using shannon’s entropy index and weight of evidence model (Case study: Sarkhoon's Karoon). Journal of Water and Soil Science. 21(1): 51–68. (In Persian). Shirzadi A, Bui DT, Pham BT, Solaimani K, Chapi K, Kavian A, ... & Revhaug I. 2017. Shallow landslide susceptibility assessment using a novel hybrid intelligence approach. Environmental Earth Sciences. 76(2): 60–74. Song Y, Gong J, Gao S, Wang D, Cui T, Li Y, Wei B. 2012. Susceptibility assessment of earthquake-induced landslides using Bayesian network: a case study in Beichuan, China. Computers & Geosciences. 42(1): 189–199. Tay LT, Lateh H, Hossain MK, Kamil AA. 2014. Landslide hazard mapping using a poison distribution: a case study in Penang Island, Malaysia. In Landslide Science for a Safer Geoenvironment, Springer Cham. 521–525. Teimoori Yansari Z, Hosseinzadeh SR, Kavian A, Pourghasemi HR. 2017. Determination of sensitive areas to Landslide Occurrence Using Shannon Entropy Model (Case study: Chahardangeh Basin, Mazandaran Province). Geography and Environmental Hazards. 6(22): 183–204. (In Persian). Youssef A, Pourghasemi HR, El-Hadad BA, Dhahry BK. 2016. Landslide susceptibility maps using different probabilistic and bivariate statistical models and comparison of their performance at Wadi It wad Basin, Asir Region, Saudi Arabia. Bulletin of Engineering Geology and the Environment. 75(1): 63–87. Yufeng S, Fengxiant J. 2009. Landslide Stability Analysis Based on Generalized Information Entropy. International Conference on Environmental Science and Information Application Technology. pp. 83–85. Zare M, Pourghasemi HR, Vafakhah M, Pradhan B. 2013. Landslide susceptibility mapping at Vaz Watershed (Iran) using an artificial neural network model: a comparison between multilayer perceptron (MLP) and radial basic function (RBF) algorithms. Arabian Journal of Geosciences. 6(8): 2873–2888. Zho C, Yin K, Cao Y, Ahmed B, Li Y, Catani F, Pourghasemi HR. 2018. Landslide susceptibility modeling applying machine learning methods: A case study from Longju in the Three Gorges Reservoir area, China. Computers & Geosciences. 112(1): 23–37. | ||
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