الویت‌بندی عوامل موثر و پهنه‌بندی حساسیت نسبت به رخداد زمین‌لغزش با استفاده از مدل‌های حداکثر آنتروپی و دمپسترشفر در حوضه دوآب‌صمصامی چهارمحال و بختیاری

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشیار پژوهشکده‌ حفاظت خاک و آبخیزداری، سازمان تحقیقات، آموزش و ترویج کشاورزی، تهران، ایران

2 کارشناس ارشد مرکز تحقیقات و آموزش کشاورزی و منابع طبیعی استان اصفهان، سازمان تحقیقات، آموزش و ترویج کشاورزی، اصفهان، ایران

چکیده

هدف این پژوهش الویت‏بندی عوامل موثر، پهنه‏بندی و ارزیابی حساسیت نسبت به رخداد زمین‏لغزش با استفاده از روش‏‏های احتمالاتی دومتغیره حداکثر آنتروپی و دمپسترشفر در حوزه آبخیز دوآب صمصامی استان چهارمحال و بختیاری می‏باشد. بدین‏منظور پس از شناسایی، تهیه و آماده‌سازی نقشه 15عامل موثر بر رخداد زمین‏لغزش به عنوان متغیرهای مستقل و نقشه پراکنش زمین‏لغزش به‏عنوان متغیر وابسته با استفاده از شاخص نسبت فراوانی (FR) و نقشه پراکنش زمین‏لغزش‏ها در محیط ArcGIS®10.8 اقدام به وزن‏دهی یا کمی کردن آنها گردید. داده‏های پراکنش زمین‏لغزش به دو دسته داده آموزشی و آزمایشی با نسبت 70 و 30 درصد به‏ترتیب به منظور اجرا و اعتبارسنجی بصورت تصادفی تقسیم شدند. با استفاده از 15 عامل موثر و نقشه پراکنش زمین‏لغزش‏ها مدل‏های حداکثر آنتروپی و دمپسترشفر اجرا و نقشه‏های پهنه‏بندی حساسیت نسبت به رخداد زمین‏لغزش تهیه و هر کدام به پنج رده حساسیت از خیلی‏کم تا خیلی‏زیاد تقسیم شدند. ارزیابی دقت طبقه‏بندی‏ و اعتبارسنجی مدل‏ها به ترتیب از نمودار شاخص‏های نسبت فراوانی-سطح سلول هسته (FR&SCAI) و سطح زیر منحنی ویژگی عملکرد گیرنده (AUC-ROC) استفاده گردید. با توجه به نتایج اجرای مدل‏ حداکثر آنتروپی، عوامل بارش سالیانه، سنگ‏شناسی، فاصله از جاده و آبراهه، به ترتیب بیشترین اهمیت را در رخداد زمین‏لغزش‏ها دارند. در هر دو مدل، بیش از 50 درصد زمین‏لغزش‏ها در رده‏های حساسیت زیاد و خیلی‏زیاد رخ داده‏اند. نهایتاً نتایج اعتبارسنجی مدل‏ها نشان داد مدل دمپسترشفر با شاخص AUC-ROC معادل 95/0 و دقت طبقه‏بندی با شاخص FR&SCAI بالاتر، کارآمدی و مطلوبیت بیشتری برای پهنه‎‏بندی، مدل‏سازی و پیش‏بینی رخداد زمین‏لغزش‏ها در منطقه مورد مطالعه دارا می‏باشد.

کلیدواژه‌ها


عنوان مقاله [English]

Prioritization of effective parameters and landslide susceptibility zonation using maximum entropy and dempster shafer in Doab Samsami, Chaharmahal Bakhtiyari

نویسندگان [English]

  • Kourosh Shirani 1
  • Reza Naderi Samani 2
1 Associate Professor, Soil Conservation and Watershed Management Research Institute (SCWMRI), Agricultural Research, Education and Extension Organization (AREEO), Tehran, Iran
2 Researcher, Soil Conservation and Watershed Management Research Department, Isfahan Agricultural and Natural Resources, Research and Education Center, AREEO, Isfahan, Iran
چکیده [English]

The aim of this study is to prioritize effective factors, landslide susceptibility zonation assessment using maximum entropy (MaxEnt) and dempster shafer models in Doab Samsami watershed of Chaharmahal and Bakhtiari province. For this purpose, 15 factor maps affecting landslide occurrence as independent variables and landslide distribution map as a dependent variable were prepared and weighted using frequency ratio index (FR) and landslide distribution map in the environment ArcGIS® 10.8 . In order to implementation and validation of models, landslide distribution data were randomly divided into two categories of training and test data in the proportion of 70 and 30%, respectively. Maximum Entropy (MAXENT) and Dempster Shaffer models are performed and landslide susceptibility zonation maps are prepared and each model is divided into five very low to very high. In order to evaluate the classification accuracy and validation of the models, the frequency ratio and seed cell area index (FR&SCAI) and the area under receiver characteristic curve (AUC-ROC) were used, respectively. According to the results of the maximum entropy model, annual precipitation factors, lithology, distance to road and drainage land use are important in landslide occurrence, respectively. According to landslide susceptibility zonation maps in both models, more than 50% of landslides occurred in high and very high susceptibility categories. Finally, the validation results of the models showed that the Demester shafer model with AUC-ROC index of 0.95 and classification accuracy with higher FR & SCAI index, greater efficiency and desirability for zoning, modeling and landslide prediction in the study area.

کلیدواژه‌ها [English]

  • Landslide
  • Doab Samsami watershed
  • zonation
  • maximum entropy
  • dempster shafer
  • An, P., Moon, W.M., and Bonham-Carter, G.F. (1994). Uncertainty management in integration of exploration data using the belief function. Nonrenewable Resources, 3 (1), 60-71.
  • Arabameri, A., Shirani, K., and Heydari, F. (2018). Comparison of aomparison of artificial neural network and multivariate regression methods in landeslide hazard zonation. Watershed Engineering and Management, 9 (4), 451-64 (in Persian).
  • Baharvand, S., and Soori, S. (2016). Prioritization of landslide effective factors and its hazard mapping using fuzzy logic. Journal of Engineering Geology, 9 (4), 3093-3112 (in Persian).
  • Baboli moakher, H., Shirani, K., and Taghian. A.R. (2018). Performance of chaos theory on natural systems in landslide hazard zonation in Fahlian River Basin. Journal of Geoscience, 28 (109), 187-200 (in Persian).
  • Constantin, M., Bednarik, M., Jurchescu, M.C., and Vlaicu, M. (2010). Landslide susceptibility assessment using the bivariate statistical analysis and the index of entropy in the Sibiciu Basin Romania. Environmental Earth Science, 63 (2), 397-406.
  • Convertino, M., Troccoli, A., and Catani, F. (2013). Detecting fingerprints of landslide drivers: A MaxEnt model. JGR: Earth Surface, 118 (3), 1367-1386.
  • Davis, J., and Blesius, L. (2015). A hybrid physical and maximum-entropy landslide susceptibility model. Entropy, 17 (6), 4271-4292.
  • Dempster, A.P. (1967). Upper and lower probabilities induced by a multi valued mapping. Ann Math Stat, 38 (2), 325–339.
  • Elith, J., Phillips, S.J., Hastie, T., Dudík, M., Chee, Y.E., and Yates, C.J. (2011). A statistical explanation of MaxEnt for ecologists. Diversity and Distributions, 17 (1), 43–57.
  • Gibbs, H.K., Ruesch, A.S., Achard, F., Clayton, M.K., Holmgren, P., Ramankutty, N., and Foley, J.A. (2010). Tropical forests were the primary sources of new agricultural land in the 1980s and 1990s. Proceedings of the National Academy of Sciences, 107 (38), 16732-16737.
  • Goetz, J. N., Guthrie, R. H., and Brenning, A. (2011). Integrating physical and empirical landslide susceptibility models using generalized additive models. Geomorphology, 129 (3-4), 376-386.
  • Hong, H., Shahabi, H., Shirzadi, A., Chen, W., Chapi, K., Ahmad, B., Shadman, M., Yari, A., Tian, Y., and Bui, D. (2019). Landslide susceptibility assessment at the Wuning area, China: a comparison between multi-criteria decision making, bivariate statistical and machine learning methods. Natural Hazards, 96 (1), 173-212.
  • Jiao, Y., Zhao, D., Ding, Y., Liu, Y., Xu, Q., Qiu, Y., Liu, C., Liu, Z., and Zha, Z., Li, R. (2019). Performance evaluation for four GIS-based models purposed to predict and map landslide susceptibility: A case study at a World Heritage site in Southwest China. Catena, 183, 104221.
  • Kornejady, A., Ownegh, M., and Bahremand, A. (2017). Landslide susceptibility assessment using maximum entropy model with two different data sampling methods. Catena, 152, 144-162.
  • Karam, A., and Mahmoudi, F. (2005). the Quantitative modelling and the zonation of landslide risk in the folded Zagros (Case study of Sarkhoun Watershed–Chaharmahal and Bakhtiari province). Geographical Research Quarterly 1,1 (in Persian).
  • Landslide studies group Ministry of Jihad Sazandegi. LSG. (2000). (in Persian)
  • Mehrotra, G.S., Sarkar, S., and Dharmaraju, R. (1992). Landslide hazard assessment in Rishikesh-Tehri area, Garhwal Himalaya, India. International Symposium on Landslides, 1001–1007.
  • Mirzaei, G., Soltani, A., Soltani, M., and Darabi, M. (2018). An integrated data mining and multi-criteria decision-making approach for hazard-based object ranking with a focus on landslides and floods. Environmental Earth Sciences, 77, 581.
  • Mirsanei, S., and Rahmatullah, K. (1999). An analytical approach to the characteristics of landslides in the country. The First Iranian Conference on Engineering Geology and Environment, (in Persian).
  • Mansouri, M., Shirani., K., Ghazifard, A., and Emami, N. (2017). Application of probabilistic methods in landslide hazard zonation Mapping (Case study: Doab Samsami Region in Chaharmahal and bakhtiari province). Geosciences, 26 (102), 267–80 (in Persian).
  • Mansouri, M., Shirani, K., and Ghazifard, A. (2015). Landslide risk zoning of Doab Samsami area of Chaharmahal and Bakhtiari province by AHP method. The Second National Conference on New Horizons in Empowerment and Sustainable Development of Architecture, Civil Engineering, Tourism, Energy and Urban and Rural Environment, (in Persian).
  • Mohammadi, M., Moradi, M., Feyznia, S., and Pourghasemi, H. (2008). Effects of rangeland vegetation on slope stability in a part of haraz watershed using gis. Journal of Rangeland, 289-300 (in Persian).
  • Neuhäuser, B., and 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.
  • Nasiri, S., and Ehteshami Moinabadi. M. (2004). An Overview to Iranian Landslide. Distribution and Occurrence of Landslides in Iran. Case Study in Haraz Highway Alborz Mountain Iran, 166/2-62.
  • Pandey, V.K., Pourghasemi, H.R., and Sharma, M.C. (2018). Landslide susceptibility mapping using maximum entropy and support vector machine models along the Highway Corridor. Garhwal Himalaya. Geocarto International, 35 (2), 168-187.
  • Pournader, M., Feiznia, S., Ahmadi, H., Karimi, H., and Peirovan, H. (2020). Assessing the stability of maximum entropy prediction for rill erosion modelling. Journal of Soil and Water Resources Conservation, 9 (2), 123-139.
  • Park, N.W. (2015). Using maximum entropy modeling for landslide susceptibility mapping with multiple geoenvironmental data sets. Environmental Earth Sciences, 73 (3), 937-949.
  • Phillips, S.J., Dudík, M., and Schapire, R.E. (2004). A maximum entropy approach to species distribution modeling. Proceedings of the Twenty-First International Conference on Machine Learning, 83.
  • Phillips, S.J., Anderson, R.P., and Schapire, R.E. (2006). Maximum entropy modeling of species geographic distributions. Ecological Modelling, 190 (3/4), 231-259.
  • Phillips, S.J., and Dudík, M. (2008). Modeling of species distributions with Maxent. new extensions and a comprehensive evaluation, Ecography, 31 (2), 161-175.
  • Rajabzadeh, F., ghiasi, S., and Rahmati. O. (2019). The performance of the maximum entropy algorithm and geographic information system in shallow landslide susceptibility assessment. Journal of Soil and Water Resources Conservation, 8 (2), 57-73 (in Persian).
  • Razavi Tameh, S.V., and Shirani, K. (2019). Landslide hazard zoning using frequency ratio, entropy methods and TOPSIS decision-making methods (Case study: Fahliyan Basin, Fars). Journal of RS and GIS for Natural Resources, 9 (4), 119-138 (in Persian).
  • Shafer, G. (1976). A mathematical theory of evidence. Princeton university press, (Vol. 42).
  • Shirani, K., Seif, A., and Nasr, A. (2013). Investigation of effective’s parameters on Mass movement by using of landslide hazard zonation Maps (Case Study: Northern’s Karoon Basin). Journal of Geoscience, 23 (89) , 3-10 (in Persian).
  • Shirani, K., and Arabameri, A.R. (2015). Landslide hazard zonation using logistic regression method (Case study: Dez-e-Oulia basin). Journal of Science and Technology of Agriculture and Natural Resources, 19 (72), 321-335.
  • Shirani, K., Pasandi, M., and Arabameri, A.R. (2018). Landslide susceptibility assessment by Dempster–Shafer and Index of Entropy models, Sarkhoun basin, Southwestern Iran, Natural Hazards, 93 (3), 1379-1418.
  • Sidle, R.C., and Ochiai, H. (2006). Landslides processes, prediction, and land use.: Water Resources Monograph, 18. American Geophysical Union, Washington D.C, 322-326.
  • Swets, J.A. (1988). Measuring the accuracy of diagnostic systems. Science, 240 (4857), 1285–1293.
  • Tien Bui, D., Shahabi, H., Shirzadi, A., Chapi, K., Alizadeh, M., Chen, W., Mohammadi, A., Ahmad, B. Bin, Panahi, M., and Hong, H. (2018). Landslide detection and susceptibility mapping by airsar data using support vector machine and index of entropy models in cameron highlands, malaysia. Remote Sensing, 10 (10), 1527.
  • Teimoori, Y., Hosseinzadeh, S.R., Kavian, A., and Pourghasemi, H. (2017). Determination of Sensitive Areas to Landslide Occurrence Using Shannon Entropy Model (CaseStudy: Chahardangeh Basin, Mazandaran province). Journal of Geography and Environmental Hazards, 6 (2), 183-204 (in Persian).
  • Teimouri, M., and Asadi nalivan, O. (2020). Susceptibility zoning and prioritization of the factors affecting landslide using maxent, geographic information system and remote sensing models (Case study: Lorestan province). Hydrogeomorphology, 6 (2), 155-79 (in Persian).
  • Varnes, D.J. (1984). Landslide hazard zonation: A Review of Principles and Practice, (Issue 3).