| تعداد نشریات | 282 |
| تعداد نشریات سازمان | 80 |
| تعداد شمارهها | 3,208 |
| تعداد مقالات | 35,560 |
| تعداد مشاهده مقاله | 53,002,028 |
| تعداد دریافت فایل اصل مقاله | 92,287,669 |
ارزیابی کارآیی مدل های پیش بینی حساسیت وقوع زمین لغزش در آبخیز بار نیشابور | ||
| پژوهش های آبخیزداری | ||
| مقاله 3، دوره 33، شماره 2 - شماره پیاپی 127، تیر 1399، صفحه 20-30 اصل مقاله (803.77 K) | ||
| نوع مقاله: پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22092/wmej.2019.126950.1241 | ||
| نویسندگان | ||
| مریم حلاجی* 1؛ محمدعلی زنگنه اسدی2؛ ابوالقاسم امیراحمدی3 | ||
| 1دانشجوی دکتری ژئومورفولوژی دانشگاه حکیم سبزواری | ||
| 2دانشیار گروه آب و هواشناسی و ژئومورفولوژی دانشگاه حکیم سبزواری | ||
| 3استاد گروه آب و هواشناسی و ژئومورفولوژی دانشگاه حکیم سبزواری | ||
| چکیده | ||
| زمینلغزش یکی از انواع ویرانگر فرسایش در دامنهها است که موجب واردشدن زیانهای مالی و جانی فراوانی میشود. شناسایی عاملهای موثر در رخداد زمینلغزش و تهیهی نقشهی پهنهبندی حساسیت آن از ابزارهای اساسی کاهشدادن زیانهای احتمالی است. در این مقاله خطر زمینلغزش در آبخیز بار نیشابور با روشهای ماشین بردار پشتیبان، بیشینهی آنتروپی و الگوریتم جنگل تصادفی پهنهبندی شد. با جمعآوری اطلاعات از پراکندگی زمینلغزشها در منطقه، نقشهی پراکنش زمینلغزشها، و 12 لایهی اطلاعاتی شامل درجهی شیب، جهت شیب، انحنای سطح، انحنای نیمرخ، ارتفاع، کاربری زمین، زمینشناسی، فاصله از جاده، فاصله از آبراه، فاصله از گسل، شاخص رطوبت پستیوبلندی و تراکم زهکشی در سامانهی اطلاعات جغرافیایی تهیه شد. نقشهی حساسیت زمینلغزش منطقه با سه روش جنگل تصادفی، بیشینهی آنتروپی و ماشین بردار پشتیبان تهیه و با منحنی تشخیص عملکرد نسبی و 30% نقطههای لغزشی بهکارنرفته در فرآیند مدلسازی، صحتسنجی شد. نتیجهی ارزیابی مدلها نشان داد که دقت نقشههای برآوردشده با روشهای ماشین بردار پشتیبان، بیشینهی آنتروپی و الگوریتم جنگل تصادفی بهترتیب 86، 75 و 82% است. نقشههای دادهشده ممکن است در شناخت منطقههای ناپایدار و نیز در اجرای برنامههای عمرانی بهخصوص جادهسازی نقش بسزایی داشته باشد. توانمندی گردشگری در حوزهی بار زیاد است، و توجه به امکان زمینلغزش در آن ضروری بهنظر میرسد. | ||
| کلیدواژهها | ||
| حساسیت زمینلغزش؛ پهنهبندی؛ جنگل تصادفی؛ ماشین بردار پشتیبان؛ منحنی تشخیص عملکرد نسبی | ||
| عنوان مقاله [English] | ||
| An Assessment of the Landslide Susceptibility Prediction Models in the Bar Watershed- Neyshabur | ||
| نویسندگان [English] | ||
| Maryam Hallaji1؛ MohamadAli Zangane Asadi2؛ Abolghasem Amirahmadi3 | ||
| 1PhD., Student of Geomorphology- Department of Physical Geography- Hakim Sabzevari University | ||
| 2Associate Professor- Department of Physical Geography- Hakim Sabzevari University | ||
| 3Professor- Department of Physical Geography- Hakim Sabzevari University | ||
| چکیده [English] | ||
| Landslide is one of the most destructive types of erosion of slopes, causing substantial financial losses. Identification of causative factors in the landslide occurrence and providing a zoning map of the susceptible areas is one of the basic tools for minimizing the possible damages. In this research, the landslide susceptibility map in the Neyshabur Watershed was prepared using three algorithm including, Support Vector Machine, Maximum Entropy, and Random Forest Algorithm. A map of landslide distribution was prepared along with 12 thematic layers including slope, aspect, plan curvature, profile curvature, elevation, land use, geology, distance from the road, distance from the rivers, distance from the fault, topographic wetness index, and drainage density in the GIS environment. The landslide susceptibility map of the studied area was prepared using three methods of random forest algorithm, maximum entropy, and support vector machine algorithm, and using Receiver Operating Characteristics and 30% of unused landslide points in the modeling process. The results of an assessment of the models indicated that the accuracy of the estimated maps prepared by the Support Vector Machine, Maximum Entropy, and Random Forest Algorithm were 86, 75, and 82 percent, respectively. Therefore, it can be stated that the presented maps can play an important role in identifying the slide-prone areas as well as in the implementation of development plans, especially road construction in the studied area. Given the potential of tourism in the catchment area, it is necessary to pay attention to the landslide potential in the catchment. | ||
| کلیدواژهها [English] | ||
| Landslide susceptibility, zoning, random forest, support vector machine, receiver operating characteristics curve | ||
| مراجع | ||
|
Armin M, Mostafaie J, Ghorban Nai Kheybari V, Kheyri A. 2019. Landslide zoning and its risk control management program in Kohkiloyeh and Boyerahmad Province using Haieri-Samiei model. Quantitative Geomorphology Research. 7: 176–196. (In Persian). Asghar Pour MJ. 2006. Multi-criteria decision making. Tehran: Tehran University Press. Fourth edition. 400 p. (In Persian). Breiman LJ, HFriedman RA, Olshen CJ. Stone. 1984. Classification and regression trees, P368. Davis J, Blesiu L. 2015. A hybird physical and maximum-entropy landslide susceptibility model. Entropy Journal. 17(6): 4271–4292. Emami N, Ghayomian J. 2003. A survey on landslide mechanism on slopes (Case study of Affsar Abad Chahar Mahal and Bakhtiari Province). Proceedings of the Third Conference on Engineering and Environmental Geology of Iran, Bu-Ali Sina University of Hamedan. (3):113–126. (In Persian). Fatemi Aghda M, Ghiomian J, Eshgheli Farahani A. 2006. Investigation landslide hazard using fuzzy logic (Case study: Roudbar Area). Journal of science, Tehran university. 31(1): 43–64. Ghasemian B, Abedini M, Rural Sh, Shirzadi A .2017. Landslide Sensitivity Evaluation Using Vector Backup Machine Algorithm (Case Study: Kamyaran City, Kurdistan Province). Quantitative Geomorphology Researches, 6(3):36–15. (In Persian). Hastie T, Tibshirani R, Friedman J.H. 2001. The elements of statistical learning: Data mining. Inference and Prediction. Springer Verlag, New York. 745 p. General Department of Natural Resources of Khorasan Razavi Province. 2018. Waterway and Road Map. Geological Survey of Iran1/100000, 1994. Index Map: Neyshabor. Hattanji T, Moriwaki H. 2009. Morphometric analysis of relic landslides using detailed landslide distribution maps. Implications for forecasting travel distance of future landslides. Journal of Geomorphology. (103):447–454. Hong Haghib SA, Pourghasemi HR. 2016. GIS-based landslide spatial modeling in Ganzhou City. China, Arab J Geosci Journal. 9(2):112–138. Khaldi Sh, Derafshi Kh, Mehrjoonejad A, Gharechahi S, Khalidi Sh. 2012. Evaluation of effective factors in landslide event and its zoning using logistic regression model in GIS environment (Case study of Taleghan watershed). Journal of Geography and Environmental Hazards. 1(1): 65–82. (In Persian). Lieb M, Glaser B, Huwe B. 2012. Uncertainty in the spatial prediction of soil tex-ture: Comparison of regression tree and Random Forest models. Geoderma. 170(27): 70–79. Mohammadnia M, Amir Ahmadi A, Solgi L. 2015. Preparation of landslite sensitivity map using an integrated model of frequency ratio and analytical hierarchy process. Two Quarterly Journal of Geomorphology of Iran. 3(5):58–45. (In Persian). Mohammadi M, Pourghasemi HR. 2017. Prioritizing the factors affecting landslide occurrence and preparing its susceptibility map using a new randomized-stock forest (Case study: Part of Golestan province). Journal of Management of Watershed. 8 (15):170–161. (In Persian). Moghimi E, Bagheri Seyyed Shokri S, Safarrud T. 2012. Landslide hazard zonation by entropy model (Case study: Nazar Ancestor, Northwestern Zagros). Natural Geography Research. 79: 77 –90. (In Persian). National Mapping Organization. 1990. Index Map 7762, Neyshabor. Neuhauser B, Terhorst B. 2007. Landslide susceptibility assessment using weights-of-evidence applied to a study area at the Jurassic escarpment (SW. Germany). Geomorphology. 86(1–2):12–24. Peters J, Verhoest N, Samson R, Boeckx P, De Baets B. 2008. Wetland vegetation distribution modelling for the identification of constraining environmental variables. Landscape Ecology. 23(9):1049–1065. Pradhan B. 2011. An assessment of the use of an advanced neural network model with five different training strategies for the preparation of land slide susceptibility maps. Jornal of Data Science. (9):65–81. Razavi Termeh SV, SHirani K, Soltani Rabie M. 2019. Evaluation and preparation of underground water potential map using combined model of control weight and logistic regression (Case study of Nahavand City). Journal of Science Water and Soil. 23(2):27–43. (In Persian). Scholkoph B, Smola AJ, Williamson RC, Bartlett PL. 2000. New support vector algorithms. Neural Computation. 12(5): 1207–1245. Shekari Badi A, Motamedirad M, Mohammadnia M. 2015. ANP modeling and shannon entropy index in estimating effective factors in the occurrence and land covering of landslide risk (Case study: Faroob Roman Basin of Neyshabur). Geographical Studies Quarterly of Regions Dry. 6(22): 103–89. (In Persian). Simpson GL, Birks HJB. 2012. Tracking environmental change using lake sediments, Springer Publication. 5: 673 p. Teymori Yansari Z, Hosseyn Zade SR, Kaviav A, pourghasemi HR. 2017. Preparation of landslide sensitive zones using shannon entropy method (Case study: Chardandeh-Mazandaran Province Watershed). Geography Quarterly and Environmental Threats. 6(22):204–183. (In Persian). Vorpahl P, Elsenbeer H, Marker M, Schroder B. 2012. How can statistical models help to determine driving factors of landslides ecological modelling. C (239): 27–39. Yamani M, Ahmadabadi A, Zare R. 2012. The Zonation of landslide occurrence using of support vector machines algorithm, case study: Darakeh Basin. Geography and Environmental Hazards. 3: 125–142. (In Persian). Yufeng S, Fengxiant J. 2009. Landslide stability analysis based on generalized information entropy. International Conference on Environmental Science and Information Application Technology. (3):83–85. Youssef AM, 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 Itwad Basin, Asir Region, Saudi Arabia. Bull Eng Geol Environ. 75(1):63–87. Zarrabi A, Divsalar A, Kanaani MR. 2012. Spatial analysis of urban settlements based on environmental abilities (Case study: Mazandaran Province). Planning and Space Planning. 16 (2):100–78. Zamiri M, Nastaran M, Mohamadzadeh Titkanlo H. 2012. An analysis of the shape and process of spatial and physical expansion of Bojnourd City in the 1380s (Using shannon entropy, Moran coefficient and coefficient). Environment Based Territorial Planning. 6(23):167–180. (In Persian). Ziari K, Asadi S, Ashnooyi A. 2014. Balancing the distribution of cities of Guilan province using the entropy model. Geographic Outlook, Zagros Outlook. 6(20):20–27. (In Persian). Zongji Y, Jianping Q, Xiaogang ZH. 2010. "Regional landslide zonation based on entropy method in three Gorges Area, China", Fuzzy Systems and Knowledge Discovery (FSKD). Seventh International Conference on Fuzzy Systems and Knowledge Discovery. (7)1336– 1339. Zhu C, Wang X. 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. Wuhan, China. (3): 342–346. | ||
|
آمار تعداد مشاهده مقاله: 652 تعداد دریافت فایل اصل مقاله: 836 |
||