بررسی عوامل مؤثر بر زمین‌لغزش در حوزه آبخیز ساحلی بهشهر-گلوگاه با استفاده از مکسنت در استان مازندران

چکیده مقاله :

مطالعه حاضر بررسی مهم‌ترین عوامل تأثیرگذار بر زمین‌لغزش درحوزه آبخیز ساحلی بهشهر-گلوگاه است. با پیمایش میدانی در منطقه مورد مطالعه نقاط حضور زمین‌لغزش با GPS ثبت شد، هفت عامل شیب، فاصله از جاده، فاصله از گسل، زمین­شناسی، فاصله از رودخانه، متوسط بارش سالانه و تغییرات کاربری اراضی به‌عنوان مهم‌ترین عوامل تأثیرگذار به‌عنوان متغیرهای مستقل با استفاده از سیستم اطلاعات جغرافیایی (GIS) تهیه و وارد مدل شد. برای مدل‌سازی و محاسبات از مدل حداکثر آنتروپی در محیط نرم‌افزار Maxent استفاده شد. نتایج حاصل از نمودار جک نایف نشان داد که 3 عامل شیب، فاصله از جاده و زمین­شناسی بیشترین اثر را بر زمین‌لغزش منطقه مورد مطالعه دارند. به‌طوری‌که با افزایش شیب (20-40 درصد) زمین‌لغزش بیشتر می­شود همچنین زمین‌لغزش با فاصله از جاده رابطه عکس داشتند در عامل زمین­شناسی نیز  درون واحدهای PZq.d و Qra، بیشترین احتمال زمین‌لغزش وجود دارد. اعتبارسنجی مدل با استفاده سطح زیر منحنی ROC با مقدار 77/0 نشان داد که این مدل در منطقه مورد مطالعه قابل قبول است. از طرفی نتایج حاصل از طبقه­بندی خطر زمین‌لغزش در منطقه مورد مطالعه نشان­دهنده­ی پتانسیل 40 درصدی آن است. یافته‌های این مطالعه پایه و اساس مهمی را برای کمک به تصمیم‌گیران در مورد پیش‌گیری از فاجعه و کاهش آن ارائه می‌کند. همچنین به درک بهتر خطرات زمین‌لغزش کمک می­کند، که برای توسعه اقدامات مدیریت خطر مناسب بسیار مفید است بنابراین پیشنهاد می­گردد که در مناطق مختلف نیز به‌منظور شناخت مناطق پرخطر اقدام شود. 

چکیده انگلیسی :

The aim of the present study is to investigate the most important factors affecting landslides in Behshahr-Galogah coastal watershed. For this purpose, by field survey in the study area, the locations of landslides were recorded with GPS. 7 factors including, distance from the road, distance from the fault, geology, distance from the river, the average of annual rainfall, and land use changes as the most important influencing factors and independent variables were prepared using Geographical Information System (GIS) and entered into the model. For modeling and calculations, the maximum entropy model was used in the Maxent software environment. The results of the Jackknife diagram showed that the three factors of slope, distance from the road and geology have the greatest effect on landslides in the study area. As, by increasing the slope (20-40 percent), landslides increase; also, landslides have an inverse relationship with the distance from the road. For the geological factor, there is the highest probability of landslides within PZq.d and Qra units. The validation of the model using the area under the ROC curve showed that this model is acceptable in the study area with a value of 0.77. On the other hand, the results of landslide risk classification in the studied area showed that 40% of the area has landslide potential. The findings of this study provide an important foundation to assist decision makers in disaster prevention and mitigation and also, it helps to better understand the dangers of landslides which is very useful for the development of risk management measures, so it is suggested to take action in different regions in order to identify high-risk areas.

منابع و مأخذ:

ذاکری¬نژاد، ر. و عمو شاهی، ه. 1401. ارزیابی خطر زمین¬لغزش با استفاده از داده های سنجش از دور ومدل حداکثر آنتروپی (منطقه مورد مطالعه: حوضه آبخیز کمه، جنوب استان اصفهان). پژوهش¬هاي ژئومورفولوژي كمّي، 2 (1): 128-149.
رضوی ترمه، و. و شرانی، ک. 1397. پهنه بندي خطر وقوع زمين لغزش با استفاده از روش¬هاي نسبت فراواني، آنتروپي و روش تصميم¬گيري تاپسيس (مطالعة موردي: حوزه فهليان، فارس). سنجش از دور و سامانه اطلاعات جغرافیایی، 9 (4): 119-138.
صفامهر، م.، صالحی، م.، نصری، م. رهنما، م.، بور، ح. 1396. رفتارسنجی و پایدارسازي زمین لغزش براساس برداشت¬هاي نقشه برداري و داده¬هاي زمین¬شناسی مهندسی (مطالعه موردي: زمین لغزش ُنقُل در منطقه پادناي سمیرم). مسکن و محیط روستا، 162: 145-157.
قبادی، م. بهزادتبار، پ.ر، خدابخش، س. ایزدی کیان، ل. 1393. مطالعه عوامل زمین شناسی موثر بر وقوع زمین لغزش¬های منطقه زمان آباد، جنوب شرق همدان. مجله انجمن زمین شناسی مهندسی ایران، 3 (4): 1-14.
عابدینی، م.، قاسمیان، ب.، شیرزادی، ع.، 1393. مدل¬سازی خطر وقوع زمین لغزش با استفاده از مدل آماری رگرسیون لجستیک مطالعه موردی : استان کردستان، شهرستان بیجار. جغرافیا و توسعه. 12 (37): 85-102.
عرب عامري، ع.، ك. شيراني و خ. رضايي. 1396 . پهنه¬بندي استعداد اراضي نسبت به وقوع زمين لغزش با روش¬هاي دمپستر- شيفر و نسبت فراواني در حوزه سرخون كارون. مجله پژوهش¬هاي حفاظت آبوخاك، 18(27): 41-57.
نوجوان، م.، سادات شاه زیدی، س،. د داودی، م.، امین الرعایایی، ه، 1398. پهنه بندی خطر زمین لغزش با استفاده از تلفیق دو مدل فرآیند تحلیل سلسله مراتبی و فازی (مطالعه موردی: حوضه آبخیز کمه)، استان اصفهان پژوهش¬های ژئومورفولوژی کمی، 7 (28): 142-159.
Alexander, D. E. 2008. “A Brief Survey of GIS in Mass-movement Studies, with Reflections on Theory and Methods.” Geomorphology 94 (3–4): 261–267. doi:10.1016/j.geomorph.2006.09.022.
Arnone, E. Caracciolo, D. Noto, C.D. F. Preti, and R. L. Bras,“Modeling the hydrological and mechanical effect of roots on shallow landslides,” Water Resources Research, vol. 52, no. 11, pp. 8590–8612, 2016.
Bíl, M., Vodák, R., Kubeˇcek, J., Bílová, M., and Sedoník, J.: Evaluating road network damage caused by natural disasters in the Czech Republic between 1997 and 2010, Transport. Res. A.-Pol., 80, 90–103, https://doi.org/10.1016/j.tra.2015.07.006, 2015.
Blahut, J., Westen, C., and Sterlacchini, S.: Analysis of landslide inventories for accurate prediction of debris-flow source areas, Geomorphology, 119, 36–51, 2010.
Chen, Y., Zhang, Y., Wang, L., Wang, Sh., Tian, D and Zhang, L. 2022. Influencing factors, deformation mechanism and failure process prediction for reservoir rock landslides: Tanjiahe landslide, three gorges reservoir area. Frontiers in Earth Science, 15: 1-19.
Cui YF, Zhou XJ, Guo CX (2017) Experimental study on the moving characteristics of fine grains in wide grading unconsolidated soil under heavy rainfall. J Mt Sci 14(3):417–431
Cui Y, Cheng D, Choi CE, Jin W, Lei Y, Kargel JS (2019a) The cost of rapid and haphazard urbanization: lessons learned from the Freetown landslide disaster. Landslides 16:1167–1176
Fan X, Scaringi G, Korup O, West AJ, Westen CJ, Tanyas H, Hovius N, Hales TC, Jibson RW, Allstadt KE, Zhang L, Evans SG, Xu C, Li G, Pei X, Xu Q, Huang R (2019) Earthquake-induced chains of geologic hazards: patterns, mechanisms, and impacts. Rev Geophys 57(2):421–503
Gao, J., Shi, X., Li, L., Zhou, Z and Wang, J. 2022. Assessment of Landslide Susceptibility Using Different Machine Learning Methods in Longnan City, China. Sustainability, 14: 16716.
García-Rodríguez, M. J., J. A. Malpica, B. Benito, and M. Díaz. 2008. “Susceptibility Assessment of Earthquake-triggered Landslides in El Salvador Using Logistic Regression.” Geomorphology 95 (3–4): 172–191. doi:10.1016/j.geomorph.2007.06.001.
Gonzalez-Ollauri A. and Mickovski, S. B. “Plant-soil reinforcement response under different soil hydrological regimes,” Geoderma, vol. 285, pp. 141–150, 2017.
Guo, J., Yi, Sh., Yin, Y., Ciu, Y., Quin, M., Li, T and Wang, Ch. 2020. The effect of topography on landslide kinematics: a case study of the Jichang town landslide in Guizhou, China. Landslides, 9-16.
Guzzetti, F., Mondini, A. C., Cardinali, M., Fiorucci, F., Santangelo, M., and Chang, K. T.: Landslide inventory maps: New tools for an old problem, Earth-Sci. Rev., 112, 42–66, https://doi.org/10.1016/j.earscirev.2012.02.001, 2012.
Hilker, N., Badoux, A., and Hegg, C.: The Swiss flood and landslide damage database 1972–2007, Nat. Hazards Earth Syst. Sci., 9, 913–925, 2015.
Huang, G., Zheng, M., Peng, J. 2021. Effect of Vegetation Roots on the Threshold of Slope Instability Induced by Rainfall and Runoff. Hindawi Geofluids, Article ID 6682113, 19 pages
Hong, H., Kornejady, A., Soltani, A., Termeh, S.V.R., Liu, J., Zhu, A.X., Ahmad, B.B., and Wang, Y. Landslide suscep¬tibility assessment in the Anfu County, China: comparing different statistical and probabilistic models considering the new topo-hydrological factor (HAND). Earth Science Informatics, 2018, 11(4): 605-622. DOI: https://link.springer.com/article/10.1007/ s12145-018-0352-8
Javadinejad, S., Dara, R., Jafari, F. 2019. Effect of Precipitation Characteristics on Spatial and Temporal Varia¬tions of Landslide in Kermanshah Province in Iran. Journal of Geographical Research. 2 (4): 7-14
Li, Z., W. Shi, L. Yan, L. Yan, Q. Wang, and Z. Miao. 2016b. “Landslide Mapping from Aerial Photographs Using Change Detection-based Markov Random Field.” Remote Sensing of Environment 187: 76–90. doi:10.1016/j.rse.2016.10.008.
Liao, M., Wen, H., Yang, L. 2022. Identifying the essential conditioning factors of landslide susceptibility models under different grid resolutions using hybrid machine learning: A case of Wushan and Wuxi counties, China. Catena, 217: 106428.
Lin, M.L., Wu, Y.T., Wang, K.L., Hsieh, Y.M. Mon¬itoring of the Deep-seated Landslide using MEMS-a Case Study of Lantai Landslide, Taiwan. In EGU General Assembly Conference Abstracts, 2018, 20: 12531. DOI: http://adsabs.harvard.edu/abs/2018EGU¬GA. 2012531L
Liu, J., K. Hsiao, and P. Shih. 2012. “A Geomorphological Model for Landslide Detection Using Airborne Lidar Data.” Journal of Marine Science and Technology 20 (6): 629–638. doi:10.6119/JMST- 012-0412-1.
Liu, H. W. Feng, S. and Ng C. W. W., “Analytical analysis of hydraulic effect of vegetation on shallow slope stability with different root architectures,” Computers and Geotechnics, vol. 80, pp. 115–120, 2016.
Lu N, Godt JW, Wu DT (2010) A closed-form equation for effective stress in unsaturated soil. Water Resour Res 46:567–573
Melillo, M., Brunetti, M.T., Peruccacci, S., Gariano, S.L., Guzzetti, F. Rainfall thresholds for the possi¬ble landslide occurrence in Sicily (Southern Italy) based on the automatic reconstruction of rainfall events. Landslides, 2016, 13(1): 165-172. DOI: https://link.springer.com/article/10.1007/ s10346-015-0630-1
Menenser, B., Pereira, S., Reis, E. 2019. Effects of different land use and land cover data on the landslide susceptibility zonation of road networks. Nat. Hazards Earth Syst. Sci., 19, 471–487 Ngo, P.T.T.; Panahi, M.; Khosravi, K.; Ghorbanzadeh, O.; Kariminejad, N.; Cerda, A.; Lee, S. Evaluation of deep learning algori thms for national scale landslide susceptibility mapping of Iran. Geosci. Front. 2021, 12, 505–519.
Nuth M, Laloui L (2010) Effective stress concept in unsaturated soils: clarification and validation of unified framework. Int J Numer Anal Methods Geomech 32:771–801
Peng J, Fan Z, Wu D, Zhuang J, Dai F, Chen W, Zhao C (2015) Heavy rainfall triggered loess–mudstone landslide and subsequent debris flow in Tianshui, China. Eng Geol 186:79–90
Pereira, S., Zêzere, J. L., and Quaresma, I.: Landslide Societal Risk in Portugal in the Period 1865–2015, in: Advancing Culture of Living with Landslides, edited by: Mikoš, M., Vilímek, V., Yin, Y., and Sassa, K., 491–499, Springer International Publishing, Slovenia, 2017.
Pham, B.T., Bui, D.T., Pham, H.V., Le, H.Q., Prakash, I., Dholakia, M.B. Landslide hazard assess¬ment using random subspace fuzzy rules based clas¬sifier ensemble and probability analysis of rainfall data: a case study at Mu Cang Chai District, Yen Bai Province (Viet Nam). Journal of the Indian Society of Remote Sensing, 2017, 45(4): 673-683. DOI: https://link.springer.com/article/10.1007/ s12524-016-0620-3
Pham, B.T.; Nguyen-Thoi, T.; Qi, C.; Phong, T.V.; Dou, J.; Ho, L.S.; Le, H.V. Prakash, I. Coupling RBF neural network with ensemble learning techniques for landslide susceptibility mapping. Catena 2020, 195, 104805. [CrossRef]
Rossi, M., Luciani, S., Valigi, D., Kirschbaum, D., Brunetti, M.T., Peruccacci, S., Guzzetti, F. Statisti¬cal approaches for the definition of landslide rainfall thresholds and their uncertainty using rain gauge and satellite data. Geomorphology, 2017, 285: 16-27. DOI: https://www.sciencedirect.com/science/article/ pii/S0169555X17300855
Sassa K, Takagawa T (2018) Liquefied gravity flow-induced tsunami: first evidence and comparison from the 2018 Indonesia Sulawesi earthquake and tsunami disasters. Landslides 16:195–200
Sun, D.;Wen, H.; Zhang, Y.; Xue, M. An optimal sample selection-based logistic regression model of slope physical resistance against rainfall-induced landslide. Nat. Hazards 2021, 105, 1255–1279. [CrossRef] Takahashi T (2014) Debris flow: mechanics, prediction, and countermeasures, 2nd edn. CRC press, London
Tang HM, Liao PW, Wang LF, Chen HK (2013) Experimental study on gravel soils of matrix suction. Appl Mech Mater 275-277:310–315
Tukhtamirzaevich, M.A and Akhmadjanovich, T.A. 2022. Causes the occurrence of landslides and measures for its prevention. Международныйнаучныйжурнал, 100 (2): 2149-2156.
Wang, D., Hao, M., Chen, Sh., Meng, Z., Jiang, D and Ding, F., 2021. Assessment of landslide susceptibility and risk factors in China. Natural Hazards, 1-15.
Wang, G., J. Joyce, D. Phillips, R. Shrestha, and W. Carter. 2013. “Delineating and Defining the Boundaries of an Active Landslide in the Rainforest of Puerto Rico Using a Combination of Airborne and Terrestrial LIDAR Data.” Landslides 10 (4): 503–513. doi:10.1007/s10346-013-0400-x
Wen, M., H. Chen, M. Zhang, H. Chu, W. Wang, N. Hang, and Z. Huang. 2017. “Characteristics and Formation Mechanism Analysis of the “6·24” Catastrophic Landslide of the June 24 of 2017 at Maoxian, Sichuan.” The Chinese Journal of Geological Hazard and Control 28 (3): 1–7. In Chinese with English abstract. doi:10.16031/j.cnki.issn.1003-8035.2017.03.01.
Winter, M. G., Shearer, B., Palmer, D., Peeling, D., Harmer, C., and Sharpe, J.: The Economic Impact of Landslides and Floods on the Road Network, Procedia Eng., 143, 1425–1434 https://doi.org/10.1016/j.proeng.2016.06.168, 2016.
Wu Y, Ke Y, Chen Z, Liang S, Zhao H, Hong H (2020) Application of alternating decision tree with Ada- Boost and bagging ensembles for landslide susceptibility mapping. Catena. doi. org/ 10. 1016/j. catena. 2019. 104396
Zevenbergen, L.W., Thorne, C.R. (1987). Quantitative Analysis of Land Surface Topography. Earth Surface Processes and Landforms, 12, 47-56.
Zhang, C., Yin, Y., Yan, H., Li, H., Dai, Z., Zhang, N. 2021. Reactivation characteristics and hydrological inducing factors of a massive ancient landslide in the three Gorges Reservoir, China. Engineering Geology, 292: 106273.
Zhao WF, Li L, Xiao YH (2014) Experimental research of matrix suction of the unsaturated red clay. Adv Mater Res 919-921:835–838
Zhou, X.;Wen, H.; Zhang, Y.; Xu, J.; Zhang, W. Landslide susceptibility mapping using hybrid random forest with GeoDetector and RFE for factor optimization. Geosci. Front. 2021, 12, 101211. [CrossRef] Zhong, Ch., Liu, Y., Gao, P., Chen, W., Li, H., Huo, Y., Nuremanguli, T and Ma, H. 2019. Landslide mapping with remote sensing: challenges and opportunities. International Journal of Remote Sensing, VOL. 41, NO. 4, 1555–1581