مدل‌سازی خطر وقوع زمین لغزش با استفاده از مدل رگرسیون لجستیک (مطالعه موردی: استان چهار محال و بختیاری)

نوع مقاله : مقاله پژوهشی

نویسندگان

گروه مهندسی آب، دانشکده کشاورزی، دانشگاه شهرکرد، شهرکرد، ایران

چکیده

مقدمه: زمین‌لغزش به حرکت لایه‌های رسوبی غیر متراکم و متراکم بر روی سطح شیب‌دار ناپایدار گفته می­شود. عامل حرکت توده رسوبی می­تواند عوامل مختلفی باشد که هر ساله نقش مهمی در تخریب جاده­های ارتباطی و منازل مسکونی، از بین رفتن مراتع و باغ­ها و نیز فرسایش و انتقال رسوب در حوضه­های کشورها را دارند. پژوهش­های مختلفی در خارج  و داخل کشور در مورد پهنه­بندی خطر زمین لغزش با استفاده از مدل رگرسیون لجستیک انجام شده است که عواملی همچون فاصله از گسل، شیب، جهت شیب، کاربری اراضی، بارندگی منطقه، سنگ­شناسی، طبقات ارتفاعی و فاصله از رودخانه و جاده به عنوان مهم­ترین عوامل موثر بر زمین لغزش گزارش شدند.
مواد و روش­ها: در این مطالعه استان چهارمحال و بختیاری که منطقه­ای کوهستانی است مورد مطالعه قرار گرفت. با بررسی تحقیقات گذشته و از بین عوامل مختلف تاثیرگذار بر روی زمین لغزش، 9 عامل اولیه موثر شامل فاصله از گسل، شیب، جهت شیب، کاربری اراضی، نقشه هم بارش، سنگ­شناسی، طبقات ارتفاعی و فاصله از رودخانه و جاده به عنوان متغیر مستقل شناخته شدند. برای تحلیل داده­ها ابتدا 200 منطقه­ی دارای زمین لغزش به صورت تصادفی انتخاب شد، همچنین 200 نقطه دیگر در کل منطقه­ و بدون زمین لغزش نیز به­طور تصادفی انتخاب شد. سپس با اجرای مدل رگرسیون لجستیک وزن و نقش هر یک از متغیرهای مورد مطالعه تعیین شد. صحت نتایج مدل نیز با استفاده از سه آماره­ R2 ناگلکرک، R2 کوکس و اسنل و فاکتور -2LL بررسی گردید.
نتایج و بحث: با اجرای مدل رگرسیون لجستیک نتایج نشان داد که از بین متغیرهای مورد بررسی، به ترتیب جنس و شیب زمین را می­توان موثرترین عوامل در ایجاد زمین لغزش­ در منطقه دانست. پس از آن فاصله از جاده­­های ارتباطی و در انتها میزان بارش بیشترین تاثیر را بر ایجاد زمین لغزش­ در منطقه دارند. نتایج ارزیابی نشان داد که صحت کلی مدل 9/90 درصد و قابل قبول است. پس از آن فاصله از جاده­­های ارتباطی عامل موثر در ایجاد این پدیده است و در انتها میزان بارش باران بر روی این پدیده موثر است. نتایج ارزیابی صحت مدل رگرسیون لجستیک ارائه شده نشان می­دهد که فاکتور -2LL در آخرین تکرار مدل برابر 893/117 و ضرایب R2 ناگلکرک و R2 کوکس و اسنل به ترتیب 65/0 و 42/0 می­باشند که نشان دهنده دقت مدل می­باشد. در انتها با استفاده از نتایج به دست آمده نقشه پهنه­بندی خطر زمین لغزش در منطقه مورد مطالعه به دست آمد.
نتیجه­ گیری: با توجه به تعداد بالای زمین لغزش­های ثبت شده در استان چهار محال و بختیاری و خطر وقوع زمین لغزش­های جدید، این مطالعه­ بر روی منطقه مورد نظر انجام گرفت تا عواملی که تاثیر بیشتری بر روی این پدیده دارند و مناطق با خطر بالای زمین لغزش مشخص شوند. براساس نتایج به دست آمده در این پژوهش می­توان اظهار کرد که علاوه بر عوامل طبیعی(جنس زمین، شیب زمین و میزان بارندگی) برخی عوامل انسانی از جمله جاده­سازی غیر اصولی نقش مهمی را بر وقوع لغزش دارد که این عوامل باعث شده­اند 8/13 درصد استان چهارمحال و بختیاری در معرض خطر شدید زمین لغزش قرار بگیرد. برای کاهش خطرات ناشی از آن باید از تغییر اکوسیستم و کاربری اراضی مناطق تا حد امکان اجتناب کرد.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Landslide risk modeling using logistics regression model (Case study: Chaharmahal and Bakhtiari province)

نویسندگان [English]

  • Hadiseh Seddighi
  • Ahmad reza Ghasemi
Department of Water Engineering, Faculty of Agriculture, Shahrekord University, Shahrekord, Iran
چکیده [English]

Introduction: Landslide is defined as the movement of a mass of rock, debris, or earth down a slope. Sedimentary mass movement can be various factors that play an important role in the destruction of communication roads and residential houses, the destruction of pastures and gardens, as well as erosion and sediment transport in the basins. Various studies have been conducted on landslide risk zoning using logistic regression model. Which factors such as distance from fault, slope, slope direction, land use, rainfall, lithology, elevation and distance from river and road were reported as the most important factors affecting landslides.
Materials and methods: In this study, Chaharmahal and Bakhtiari province as an important mountainous region in the country was studied. Reviewing past research showed that among the various factors affecting landslides, 9 factors including distance from fault, slope, slope direction, land use, precipitation, lithology, elevation classes and distance from river and road as independent variables, are the most important factors. To analyze the data, first 200 landslide areas were randomly selected, and another 200 points in the whole area without landslides were also randomly selected. After preparing the layers, the logistic regression model was performed to investigate the role and weight of each of the 9 independent variables. The accuracy of the model results was checked using three statistics, R2 Naglerk, R2 Cox and Snell and factor-2LL.
Results and discussion: The results of running the logistic regression model, showed that among the studied variables, the texture and slope of the land, respectively, can be considered as the most effective factors in creating landslides in the region. After that, the distance from roads and finally the amount of rainfall has the greatest impact on landslides in the studied region. The evaluation results of the model obtained from these five parameters, showed that the overall accuracy of the model is 90.9% and acceptable. After that, the distance from the roads is an effective factor in creating this phenomenon, and in the end, the amount of rainfall is effective on the landslides. The results of evaluating the correctness of the logistic regression model show that the factor -2LL in the last iteration of the model is equal to 117.893 and the coefficients of R2 Naglerk, R2 Cox and Snell are 0.65 and 0.42, respectively, which indicate the accuracy of the model. Based on the obtained results, a landslide risk zoning map was prepared for the study area.
Conclusion: Due to the high number of landslides recorded in Chaharmahal and Bakhtiari province and the risk of new landslides, this study was conducted on the area to determine the factors that have the greatest impact on this phenomenon and areas with a high risk of landslides. Based on the results, it can be stated that in addition to natural factors (land type, land slope and rainfall), some human factors, including unprincipled road construction, have an important role in landslides. These factors have caused 13.8% of Chaharmahal and Bakhtiari province is at high risk of landslides. To reduce the risks, ecosystem change and land use of the regions should be avoided as much as possible.

کلیدواژه‌ها [English]

  • Risk zoning
  • Logistic regression
  • Landslide
  • Geographic information system

مراجع

-Abedini, M., Ghasemian, B. and Shirzadi, A., 2013. Landslide Risk Modeling Using Logistic Regression Statistical Model (Case Study of Kurdistan Province, Bijar), Geography and Development Quarterly, v. 12, p. 85-102 (in Persian).
-Armin, M. and Ghorban Niakhibri, V., 2018. Comprehensive management of landslide risk monitoring, Disaster Prevention and Management Knowledge (quarterly), v. 9, p. 179-192 (in Persian).
-Asadi, S., Sharqi, A. and Atefi, M., 2018. Zoning of physical - infrastructural Vulnerabilities to landslides using GIS, case study: settlements in Tehran and Alborz provinces, Disaster Prevention and Management Knowledge (quarterly), v. 4, p. 329-340 (in Persian).
-Ayalew, L. and Ymagishi H., 2005. The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakud-Yahiko Mountains, Central Japan: Geomorphology, v. 65, p. 15-31.
-Broghni, M., Porfashmi, S. and Zanganeh, M., 2017. Landslide risk and damage assessment in the Baqi watershed using the methods of certainty factor and logistic regression, Geographical planning of space quarterly journal, v. 8, 1-18 (in Persian).
-Chau, K.T. and Chan, J.E., 2005. Regional bias of landslide data in generating susceptibility maps using logistic regression for Hong Kong Island: Landslides, v. 2, p. 280-290.
-Dai, F.C. and Lee, C.F., 2002. Landslide characteristics and slope instability modeling using GIS, Lantau Island, Hong Kong: Geomorphology, v. 42, p. 213-228.
-Emami, R., Rezapour, M. and Faraji, M., 1401. Study of landslide potential in Chaybagh region with 2D electrical resistivity tomography method, Iranian Geophysical Journal, v. 3, 237-223 (in Persian).
-Emami, S., 2018. The effect of soil physical and chemical variables on the occurrence of landslides, the first national conference on earth sciences, weather and climate change, Tehran, Mehrarvand Institute of Higher Education (in Persian).
-Enteziri, M., Zakirinejad, R. and Zakari, G., 2018. Investigating the effective causes of landslides in Semiram region, 14th Congress of the Geographical Society of Iran, Tehran, Geographical Society of Iran (in Persian).
-Fallah, M., Vafai-Najad, A., Al-Sheikh, A. and Madiri, M., 2018. Landslide probability zoning using Shannon entropy models and the value of information in GIS environment, a case study: Eastern Alamut River section, Qazvin province, Scientific Research Quarterly of Geographical Data, v. 28, p. 123-136 (in Persian).
-Hasali, H., Rangali, R., Deshapriya, N. and Samarakoon, L., 2018. Landslide susceptibility mapping using logistic regression model: ProcediaEngineering, v. 212, p. 1046-1053. https://doi.org/10.1016/j.proeng.2018.01.135
-Habibpour, K. and Safari, R., 2018. A comprehensive guide to the use of spss in survey research, Motafkaran Publications, Tehran, 861 p (in Persian).
-Hasan Shahi, M., 2017. Estimated Demand for Medical Services Case Study of Shiraz and Arsanjan Cities "Generalized Logit and ANN Method, Journal of Healthcare Management, v. 9, p. 16-32. (in Persian).
-Ilderami, A. and Sepehri, M., 1402. Accuracy of landslide potential hazard maps of Kurdistan dam watershed using Full Consistency Method (FUCOM), BestWorst Method (BWM) and Analytic Hierarchy Process (AHP) methods, Hydrogeomorphology, http://dx.doi.org/10.22034/hyd. 2023.55538.1682  (in Persian).
-Karam, A. and Mahmoudi, F., 1384. Quantitative modeling and landslide risk zoning in folded Zagros (Serkhon watershed, Chaharmahal and Bakhtiari province), Geographical researches, v. 51, p. 1-14 (in Persian).
-Kardan, R., Qobadi, M. and Mirsanei, R., 1386. Landslides of the country based on aerial photographs, the 5th Conference on Engineering Geology and Environment of Iran, Tehran, Engineering Geology Society of Iran, Natural Disasters Research Institute (in Persian).
-Khalidi, Sh., Darfashi, Kh., Mehrjunjad, A. and Qara Chahi, S., 2013. Evaluation of factors affecting the landslide event and its zoning using logistic regression model in GIS environment: Quarterly Journal of Geography and Environmental Hazards, v. 1, p. 65-82 (in Persian).
-Khosravi, M. and Jamali, A., 2017. Prediction of landslide changes in North Qochan region according to factors affecting landslides using neural network, Cellular-Markov automata and logistic regression, Geography and Environmental Hazards, v. 27, p. 1-17 (in Persian).
-Kiani, Sh., Karimkhani, A. and Muzhesi, A., 1400. Landslide risk zoning using logistic and ANFIS regression model in Hashtjin catchment area of Ardabil province, Geography and Environmental Sustainability, v. 9, p. 55-73 (in Persian).
-Lin, G., Ming, J., Ya, C. and Jui, Y., 2017. Assessment of susceptibility to rainfall-induced landslides using improved self-organizing linear output map, support vector machine, and logistic regression: Engineering Geology, v. 224, p. 62-74. https://doi.org/10.1016/j.enggeo.2017.05.009
-Majdbavi, A. and Momipour, M., 1400. Zoning of areas prone to landslides in Shahid Abbaspur Dam, Geography and Environmental Hazards, v. 37, p. 65-80 (in Persian).
-Mousavi-Khatir, Z., Kavian, A. and Soleimani, K., 1389. Preparation of landslide susceptibility map, Sejarood watershed using logistic regression model, Journal of Agricultural Sciences and Techniques and Natural Resources, v. 53, 111-99 (in Persian).
-Nofarsti, H., Viskarmi, A. and Rahim-Del, M., 2018. Analysis and investigation of landslide potential using numerical modeling (case study of Qain-Afin axis of South Khorasan province), Civil and Environmental Research Quarterly, v. 5. p. 88-77 (in Persian).
-Qaidsharf, A., Talai, R. and Mokhbari, M., 2017. Landslide Risk Assessment in Damavand Basin by Logistic Regression Method, 13th National Conference on Watershed Science and Engineering and 3rd National Conference on Protection of Natural Resources and Environment, Mohaghegh Ardabili University, Ardabil (in Persian).
-Saha, A., Gupta, R. and Arora, M., 2002. GIS-based landslide hazard zonation in the Bhagirathi (Ganga) valley, Himalayas: International Journal of Remote Senescing, v. 23, p. 357-369.
-Shah Zaidi, S. and Hayati Zadeh, R., 2018. Investigation of landslides in Poshtkouh region of Fereydoun Shahr using entropy model, Geography and Development Quarterly, v. 17, p. 37-50 (in Persian).
-Sharifi, M., Shirani, K. and Shirani, M., 1400. Prioritization of factors affecting the occurrence of landslides and its sensitivity zoning using the multivariate linear regression method of a case study of the Vohrgan Watershed-West of Isfahan province, Hydrogeomorphology, v. 26, p. 163-139 (in Persian).
-Yaclin, A., 2008. GIS-based landslide susceptibility mapping using analytical hierarchy process and bivariate statistics in Ardesen (Turkey) comparisons of results and confirmations: Catena, v. 72, p. 1-12.
-Yang, J., Song, C., Yang, Y., Xu, C., Guo, F. and Xie, L., 2019. New method for landslide susceptibility mapping supported by spatial logistic regression and GeoDetector: A case study of Duwen Highway Basin, Sichuan Province, China: Geomorphology, v. 324, p. 62-71.
-Zhao, Y., Wang, R., Jiang, Y., Liu, H. and Wei, Z., 2019. GIS-based logistic regression for rainfall-induced landslide susceptibility mapping under different grid sizes in Yueqing, Southeastern China: Engineering Geology, v. 259, p. 105147.
https://doi.org/10.1016/j.enggeo.2019.105147.
 

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