Assessment of the Effective Risk Factors of Landslides in the Latian Dam using Entropy and Fuzzy Assessment Methods

Document Type : Research Article

Authors

1 University of Tehran

2 University of Kharazmi

Abstract

Introduction

Landslide is one of the catastrophic hazards which occurs in many parts of the world, causing hundreds of billions of dollars of economic damage and hundreds of thousands of deaths and injuries each year (Aleotti & Chowdhur, 1999). Landslide susceptibility assessment can be tricky, because the evaluation of both the spatial and temporal distribution of past events for large areas is difficult mainly due to the limitations and gaps of historical records and geographic information (Leonardia et al., 2016). Therefore, the identification of susceptible areas plays an important role in the assessment of environmental hazards and management of the catchment area (Sakar et al., 1995). Latian Dam is located in the northeastern of Tehran; this basin is naturally susceptible to landslide due to topographic topography, tectonic activity and seismicity, various geological, and climatic conditions. On the other hand, the dangers caused by landslide and the filling of the dam caused by sliding movements make it clear that it is necessary to study the landslide in this area. Therefore, this area was selected as the study area, and damage zoning was determined through the entropy and the fuzzy model.

Study Area

The study area is located in the northeastern of Tehran, the capital of Iran. It is 192.52. The coordinates are '29 ° 51 'to 51 ° 51' eastern longitude and '47 ° 35 'to 56 ° 35' north latitude (see pic. 1). Latian Dam lies in the southern end of the basin. Latian basin is placed in the southern slopes of Central Alborz which its main river is Jayrud. It matches with Mosha-Fasham which is located in the zone of earthquake risk in a severely degraded area. Geologically, it belongs to the first, the second and the third era. The petrographic composition of this area is mainly shale-sandstone-basalt-tuff-marl and alluvial deposits. Geomorphologically, it is mainly in the form of rock mass - stone outcrop. The rainfall is more than 500 mm per year in the study area. That is more than half of the precipitations in the form of snow.
Figure 1. Case study: Latian basin in in the upstream of the Latian Dam.

Materials and Methods

To begin with, the landslide area was prepared through the map of geomorphologic faces, Google earth images, and geological map. Next, the factors affecting the slip were identified according to the region conditions, and the expert scores were given to the classes of each level, which indicated its importance in creating a slide. Then, each map was analyzed using the ARC GIS 10 software. Finally, the entropy and fuzzy methods were used to assess the risk of slid in the area, It (risk area map obtaining from fuzzy and entropy model) was overlaid with landslides occurred. Fuzzy logic was first introduced by Lotfi Zadeh (1965),  an Iranian scientist at the University of California in 1965 (as cited in Amini Feshoody, 2005).

Results and Discussion

The regional model of erosion occurrence in the Latian basin is depicted in the following:
 
 
The abbreviations used here include: T: texture, Df: distance from fault, V: vegetation, DR: distance from river, LU: land use, S: slope, I: lithology, A: Aspect, E: Elevation, and R: rainfall.
High-risk regions include the following features: texture as an effective factor in the landslide occurrence in the region under study shares 17.65 %, distance from fault shares 14.42 %, vegetation 13.35 %, distance from river11.73 %, land use 11.53 %, and etc. The aspect, elevation and rainfall are the lowest impact percentage and their scores are equal. That is, most of the slides are in the same aspect, elevation, and rainfall. Base on entropy model, about 107 square kilometers, which is equivalent to 53.87 percent of the study area is situated at a higher than average risk. Only one landslide in the medium-range range has occurred. Based on the fuzzy model, landslides are distributed throughout the entire range of danger zones from very little to very large. The distribution of landslides in relation to hazard limits is not logical and It has not been concentrated in a particular area.

Conclusion

Landslide is one of the catastrophic hazards occurring in many parts of the world and causing hundreds of billions of dollars of economic damages and hundreds of thousands of deaths and injuries each year. Latian Dam lies in the southern end of the Latian basin. And more than ten landslides with a total area of 2.53 square kilometers were identified on top of Latian Dam. They could be considered as threatening factor for Latian Dam by increasing sediment load entering the dam. In this research, ten factors affecting landslides were identified. They were prioritized with the entropy model after the matrix was prepared. Then, using this model and the fuzzy method, high risk areas were identified. The map of landslide distribution overlapped with the hazard map and the contribution of each of the risk areas was evaluated for the occurrence of landslide. The results indicated that texture as an effective factor in landslide occurrence in the investigated region shares 17.65 %, distance from fault shares 14.42 %, vegetation 13.35 %, distance from river11.73 %, land use 11.53 %, and etc. Furthermore, in the entropy model the number of landslide incidents is more reasonable than the risk zones as compared with the fuzzy model.

Keywords


اسفندیاری درآبادی، فریبا؛ بهشتی جاوید، ابراهیم؛ 1395. پهنه بندی حساسیت وقوع زمین‌لغزش با استفاده از مدل هیبریدی قضیه‌ی بیز ANP (مطالعه موردی: گردنه حیران). نشریه هیدروژئومورفولوژی . سال دوم، شماره 8.
اصغرپور، محمدجواد؛ 1392. تصمیم‌گیری‌های چند معیاره. چاپ 11. تهران، انتشارات دانشگاه تهران.
امینی‌فسخودی، عباس؛ 1384. کاربرد استنتاج منطق فازی در مطالعات برنامه‌ریزی و توسعه منطقه‌ای. مجله دانش و توسعه، شماره 17.
بهشتی‌فر، سارا؛ مسگری، محمدسعدی؛ ولدان زوج، محمدجواد؛ کریمی، محمد؛ 1389.استفاده از منطق فازی در محیط GIS به‌منظور مکان‌یابی نیروگاه‌های گازی. دانشکده فنی: نشریه مهندسی عمران و نقشه‌برداری. دوره 44. شماره 4.
پوراحمد، احمد؛ حبیبی، کیومرث؛ محمدزهرایی، سجاد؛ نظری علوی، سعید؛ 1386. استفاده از الگوریتم‌های فازی و GISبرای مکان‌یابی تجهیزات شهری: مطالعه موردی محل دفن زباله شهر بابلسر. مجله محیط‌شناسی، سال سی و سوم، شماره 42.
پورقاسمی، حمیدرضا؛ مرادی، حمیدرضا؛ فاطمی‌عقدا، سیدمحمود؛ 1393. اولویت بندی عوامل مؤثر بر وقوع زمین‌لغزش و پهنه بندی حساسیت آن. نشریه علوم آب و خاک (علوم و فنون کشاورزی و منابع طبیعی) . سال هجدهم. شماره 4.
درویش‌زاده، علی؛ 1370. زمین‌شناسی ایران، انتشارات نشر دانش امروز (وابسته به انتشارات امیرکبیر). صفحه 901.
سازمان جنگل‌ها، مراتع و آبخیزداری، وزارت جهاد کشاورزی، 1389.
فرهودی، رحمت‌الله؛ حبیبی، کیومرث؛ زندی‌بختیاری، پروانه؛ 1384. مکان‌یابی محل دفن مواد زائد جامد شهری با استفاده از منطق فازی (Fuzzy Logic) در محیط GIS: مطالعه موردی شهر سنندج. نشریه هنرهای زیبا، شماره 23.
قنبری، ابوالفضل؛ کرمی، فریبا؛ سالکی، محمدعلی؛ 1396. ارزیابی استعداد بروز زمین‌لغزش‌های احتمالی در محدوده شهر تبریز،نشریه تحلیل فضایی مخاطرات محیطی، سال چهارم، شماره 1.
محمدخان، شیرین؛ 1388. برآورد کمی فرسایش و رسوب به روش ژئومورفولوژی (مطالعه موردی حوزه آبخیز لتیان)، رساله دکتری، دانشکده منابع طبیعی، دانشگاه تهران.
مقیمی، ابراهیم؛ باقری‌سیدلشکری، سجاد؛ صفرراد، طاهر؛ 1391. پهنه‌بندی خطر زمین‌لغزش‌ با استفاده از مدل آنتروپی (مطالعه موردی: تاقدیس نسار زاگرس شمال غربی)، مجله پژوهش‌ها‌ی جغرافیای طبیعی، سال چهل و چهار، شماره 79.
Aleotti, P., Chowdhury, R., 1999. Landslide hazard assessment: summary review and new perspectives. Bull Eng Geol Environ, No. 58, pp. 21–44.
Barille, V., Cirianni, F., Giovanni, l., Rocca, P., 2017. A Fuzzy-base methodology for landslide susceptibility mapping. Procedia - Social and Behavioral Sciences 223, pp. 896–902.
Bonham- Carter, G. F., 1994. Geographic Information Systems for Geoscientists: Modelling with GIS. 1stEd. Pergamo Press, Oxford, UK.
Dieu Bui, T., Pradhan, B., Lofman, O., Revhaug, I., Dick, O., 2012. Spatial prediction of landslide hazards in Hoa Binh province (Vietnam): A comparative assessment of the efficacy of evidential belief functions and fuzzy logic models. Catena, pp. 28–40.
Feizizadeh, B., Blaschke, T., Tiede, D., Rezaei Moghaddam, M. H. 2017. Evaluating fuzzy operators of an object-based image analysis for detecting landslides and their changes. Geomorphology 293, pp. 240–254.
Klir, J. G., Bo, Y., 1995. Fuzzy Sets Fuzzy Logic: Theory and Application, Prentice Hall, PTR.
Kumar Dahal, R., 2008. Predictive Modeling of Rainfall-induced Landslide Hazard in the Lesser Himalaya of Nepal Based on Weights-of-evidence, Geomorphology 102, pp. 496-510.
Leonardia, D., Palamaraa, R., Ciriannia, F., 2016. Landslide Susceptibility Mapping Using a Fuzzy Approach. Procedia Engineering 161, pp. 380 – 387.
Lin, H., kao, J., Li, K., Hwang, H. H., 1996. Fuzzy GIS assisted landfill siting analysis. proceedings of International Conference on solid waste technology and management. System Theory. Brooklyn, NY: Polytechnic Press.
Paulov, J., 1991. The Zone-Size-Dependent Entropy Formula and Spatial Interaction Modeling: A Note on Some Implications. Environment and Planning 23: pp. 557-570.
Pradhan, B., Lee, S., 2010. Landslide susceptibility assessment and factor effect analysis: backpropagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modeling. Environmental Modelling & Software 25, pp. 747–759.
Sakar, S., Kanungo, D.P., Mehrotar, G.S., 1995. Landslide Zonation: A Case Study in Garhwal Himalaya, India. Mountain Research and Development, Vol. 15, No. 4, PP.301-309
CAPTCHA Image