Assessing the risk of landslide in Taleghanrood watershed based on Viktor optimization algorithm

Document Type : مقاله پژوهشی

Authors

• Mojgan Entezary ¹
• Fatemeh Khodadadi ²
• Akbar Rostami ³

¹ Isfahan univercity

² Isfahan University

³ Shahid Beheshti

10.22067/geo.v5i1.46582

Abstract

. Introduction
If environmental hazards, including those with atmospheric origin and terrestrial origin cause economic and human life losses for human societies, they would be considered as disasters. Terrestrial hazards, which mostly occur as catastrophic, may have economic and human life damages when they occur in a small scale but with high intensity. Mass movements are among the most important terrestrial hazards in areas susceptible to environmental hazards. Land sliding is one of the most important mass movements. In fact, it causes thousands of deaths and financial losses to residential areas annually. It has been classified as terrestrial hazards which has a high frequency rate in Iran and its influences have been observed in susceptible half-dry and wet domains of the country.
Sliding is an integrated and often rapid motion of volumes of sediments along the domains. Land sliding is one of the natural disasters which causes extensive human life and property losses in mountainous, rainy, and seismic regions every year. Since predicting the time of land sliding occurrence is beyond the current knowledge and ability of mankind, the dangers of landslides could be partly prevented through identifying sensitive regions to landslides and ranking these regions. Most of the previous studies have been conducted on the zoning of slide danger and less attention has been paid to zoning the risk of slides. In the current study, the sensitivity map of the sub-basins under study to the occurrence of land sliding is created using one of the unranked methods called Viktor adaptive optimization method which is based on measuring maximum usefulness and minimum regret.
Viktor methodology is one of the multi-criteria problem solving methods for problems with disproportionate and incompatible criteria for which the decision-maker needs a solution near to the ideal solution and all the options are assessed based on the criteria. Also, in situations in which the decision-maker is not able to identify and express the advantages of a solution at the time of designing, this method can be an efficient tool for decision-making.

2. Study area
Taleghan drainage basin is one of the important sub-basins of Sefidrood drainage basin and it is located in the southern range of Alborz Mountain ranges and in the 100 km distance from the northeast of Qazvin and 120 km distance from Tehran in the northwest. The area of the studied region is 374.92 km2. The average altitude of the basin was 2307 m above the sea level. Taleghan River in the center of this basin emanates in the west of Kandivan and flows towards the west. After receiving high water level branches such as Alizan, Mehran, etc., it joins Alamoot River and after that, pours into the lake of Sefidrood dam. The average annual rainfall of Taleghan drainage basin is 660.41 mm and its average temperature is 10.5 C. Therefore, based on Domart-en’s climate classification, Taleghan basin falls into the sub-humid group.

3. Material and Methods
In order to create the land sliding sensitivity map in Taleghanrood’s drainage basins, first, the primary information such as topography (1:50000), geological information (1:100000), aerial photos and existing satellite images, the basic maps in the studied area including the slope map, geology map and geomorphology map were created. After providing the information and basic maps, the study was conducted in two stages. The first stage consisted of determining the main criteria affecting the susceptibility of different regions to land sliding and creating the selected criteria maps. The second stage of this research consisted of determining the importance ratio of criteria and implementing the Viktor algorithm for reaching the sensitivity to land sliding map and classifying the basins according to their degree of sensitivity.

4. Results and Discussion
At first, according to the condition of the region and the experts’ comments, nine main criteria related to the susceptibility of the basin to land sliding and affecting the occurrence of land sliding were considered in the studied area. After selecting the main criteria, the next stage was to draw the maps pertaining to each of the selected criteria for weighing and evaluating the sub-basins in the GIS environment.
Considering the effect of the above mentioned criteria on the occurrence of land sliding, the degree of plant coverage and distance from faults have a decreasing effect and the other seven criteria have an additive effect. After creating the map of the selected criteria, the importance ratio of the criteria from the viewpoint of importance in the occurrence of land sliding and the susceptibility of the basins to this phenomenon were determined with the help of AHP hierarchal algorithm.
The advantage of the Viktor model is that evaluating all the criteria does not necessitate expert investigation, rather, raw data may be used. For instance, regarding slope parameters, altitude classifications, drainage density, and rainfall, the mean of slopes, average altitude, drainage density and rainfall of the sub-basin were used and the exact values were inserted into the matrix. However, since the criteria of distance from fault, land use, plant coverage, type of soil and lithology did not have any raw data, they were evaluated on a scale from 1 to 10. In the current study, in order to weigh the options according to the role of each criterion in the specific option, the definite weighing period from 1 to 10 has been used, such that weight 1 shows the lowest impact and weight 10 indicates the highest impact on land sliding danger.

Among the nine factors which have an impact on the sensitivity of the basin to sliding, seven criteria (slope, altitudinal classes, drainage density, land use, rainfall, type of soil and lithology) have an increasing effect and two criteria (distance from fault and plant coverage) have a decreasing impact on the susceptibility of the basin to land sliding. After measuring the overall weights of the criteria having an increasing effect for each of the studied sub-basins, it was found that Zidasht 1, 2 and Danbalid sub-basins have the highest value and Shahrak, Navizak and Hasanjan sub-basins have the lowest values. In other words, Zidasht 1, 2 and Danbalid sub-basins have the highest sensitivity to the occurrence of land sliding. The sensitivity of Shahrak, Navizak and Hasanjan sub-basins is minimum.
Based on the sensitivity map of the sub-basins of Taleghanrood, less than 40% of the area of the studied basin consists of sub-basins with low sensitivity or low susceptibility to the occurrence of land sliding. This finding could be partly related to suitable topographic condition, plant coverage, and the hydrogeomorphic condition of this region in increasing the capability of these sub-basins against land sliding. In contrast, around 60% of the area of the basin are surrounded by highly sensitive basins regarding the occurrence of land sliding. Sub-basins such as Zidasht 1, 2 and Danbalid have a high susceptibility for land sliding. Since many residential and rural areas are embedded in these sub-basins, more attention and better crisis management is necessary for these areas. Unfortunately, intense changes in land use and destruction of plant coverage in recent years which has occurred due to the development of tourist areas and personal promenades has converted these areas into the critical center of land sliding in Taleghan basin. Retaining plant coverage, preventing intense changes in land use, reconstructing and increasing the moisture power of the soil and stabilizing the slopes in sensitive ranges of the studied area are recommended.

5. Conclusion
This research was one of the first to use Viktor adaptive optimization method to investigate and create the sensitivity map of a region to land sliding. After field investigations and selecting the effective criteria in the ranges sensitive to sliding, Viktor algorithm was performed for investigating the sensitivity degree of regions to land sliding and it was shown that Zidasht 2 sub-basin has the highest sensitivity to the occurrence of land sliding in the studied drainage basin. Also, Navizak basin has the lowest sensitivity or susceptibility to the occurrence of land sliding with the maximum optimization index (Q) and maximum distance from the ideal amount.
Investigation of the sensitivity map of Taleghanrood basin to land sliding and also field visits confirmed the good performance of Viktor algorithm in ranking the susceptibility of basins to land sliding. The results of this study and the suggested method could be of use in future studies and it could be compared with other methods of estimating sensitivity to land sliding to better understand its strong and weak points.

Keywords

• Risk of Land Sliding
• Viktor adaptive optimization algorithm
• Sensitivity
• Taleghan

---

References

بهنیانفر، الف؛ قنبرزاده، ه؛ منصوری دانشور، م. ر؛ 1388. پهنه‌بندی خطر زمین‌لغزش و ناپایداری دامنه‌ای به روش‌های AHP و احتمال در حوضة آبریز رودخانه کنگ، دامنه‌های شمالی بینالود. مجلة علمی پژوهشی فضای جغرافیایی. سال نهم. شماره 27. صص. 78 - 55.

بیاتی خطیبی، م؛ 1386. تعیین حساسیت بالقوة سطوح شیبدار در حوضه‌های کوهستانی نسبت به وقوع زمین‌لغزش‌ها با استفاده از روش تعیین عامل ویژه، مطالعة موردی: حوضه قرنقوچای واقع در دامنه شرقی کوهستان سهند. فصلنامة مدرس علوم انسانی. سال یازدهم. شماره 4. صص. 55 - 29.

زارع، م؛ احمدی، ح؛ غلامی، ش؛ 1390. پهنه‌بندی و ارزیابی خطر زمین‌لغزش با استفاده از مدل‌های عامل اطمینان، ارزش اطلاعات و تحلیل سلسله مراتبی در حوضة آبخیز واز. مجلة علمی پژوهشی علوم و مهندسی آبخیزداری ایران. سال پنجم. شماره 17. صص. 22 - 15.

فتوحی، ص؛ کیایی، س؛ 1393. مدیریت بحران و ارزیابی ریسک شهر قاین بر اساس مدیریت ریسک. فصل‌نامه برنامه‌ریزی محیطی. سال چهارم. شماره 14. صص 103-120

سپهر، ع؛ بهنیافر، ا؛ محمدیان، ع؛ عبدالهی، ا؛ 1392. تهیه نقشه حساسیت پذیری زمین لغزش دامنه‌های شمالی بینالود بر پایه الگوریتم بهینه‌سازی توافقی ویکور. مجله پژوهش‌های ژئومورفولوژی کمی. سال دوم. شماره 1. صص19-36

محمدی، م؛ مرادی، ح. ر؛ فیض نیا، س؛ پورقاسمی، ح. ر؛ 1388. اولویت‌بندی عوامل مؤثر بر زمین‌لغزش و تهیة نقشة خطر آن با استفاده از مدل‌های ارزش اطلاعاتی و فرایند تحلیل سلسله مراتبی (مطالعة موردی: حوضة آبخیز هراز). مجلة علوم زمین. سال نوزدهم. شماره 74. صص. 32- 27.

موسوی خطیر، س. ز؛ کاویان، ع. الف؛ سلیمانی، ک؛ 1389. تهیة نقشة حساسیت به وقوع زمین‌لغزش درحوضة آبخیز سجارود با استفاده از رگرسیون لجستیک. مجلة علوم و فنون کشاورزی و منابع طبیعی. علوم آب وخاک. سال چهاردهم. شماره 53. صص. 111 - 99.

Akgun, A. (2012). A comparison of landslide susceptibility maps produced by logistic regression, multi-criteria decision, and likelihood ratio methods: A case study atİzmir, Turkey. Landslide, 9(1), 93-106.

Ayalew, L., Yamagishi, H., Marui, H., & Kano, T. (2005). Landslides in Sado Island of Japan: Part II. GIS-based susceptibility mapping with comparisons of results from two methods and verifications. Engineering Geology, 81(4), 432-445.

Davalillo, J.G., Herrera, G., Notti, D., Strozzi, T., & Álvarez-Fernandez, I. (2012). DinSAR analysis of ALOS PALSAR images for the assessment of very slow landslides: The Tena Valley case study. Landslide, 11(2), 225-246.

Diop, S., Forbes, C., & Chiliza, G.S. (2010). Landslide inventorization and susceptibility mapping in South Africa. Landslide, 7(2), 207-210.

Felicisimo, A., Cuartero, A., Remondo, J., & Quiros, E. (2013). Mapping landslide susceptibility with logistic regression, multiple adaptive regression splines, classification and regression trees, and maximum entropy methods: A Comparative study. Journal of Landslide, 10(2), 175-189.

Kavzoglu, T., Kutlug Sahin, E., & Colkesen, E. (2013). Landslide susceptibility mapping using GIS-based multi-criteria decision analysis, support vector machines, and logistic regression. Landslide, 11(3), 425-439. DOI 10.1007/S10346-013-0391-7.

Komac, M. (2006). A landslide susceptibility model using the analytical hierarchy process method and multivariate statistics in Perialpine, Slovenia. Geomorphology, 74(1-4), 17-28.

Lee, E.M., & Jones, D.K.C. (2004). Landslide risk assessment. London: Thomas Telford.

Pellicani, R., Van Westen, C.J., & Spilotro, G. (2013). Assessing landslide exposure in areas with limited landslide information. Landslide, 11(3), 463-480.

Regmi, N.R., Giardino, J.R., Mcdonald, E.V., & Vitek, J.D. (2012). A comparison of logistic regression-based models of susceptibility to landslides in Western Colorado, USA. Landslide, 11(2), 247-262.