General Department of Natural Resources and Watershed Management, Mazandaran, Sari, Iran
Subject Areas : Forests and natural resources
1 - Natural Resources and Watershed Organization of the country
Keywords: Slope, Distance from road, Jackknife, Maximum Entropy, Validation ,
Abstract :
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.
References:
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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.
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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.
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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
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