Investigating the Efficiency of Neuro-Fuzzy Inference System Models in Landslide Susceptibility Mapping (Case Study: Sardarabad Watershed, Lorestan Province)

جغرافیا و مخاطرات محیطی

Article 3, Volume 7, Issue 1 - Serial Number 25, January 2018, Pages 75-96   

Document Type: مقاله پژوهشی

DOI: 10.22067/geo.v7i1.61008

Authors

Sahar Goudarzi¹; Ali Talebi^{* 1}; Hamidreza Pourghasemi²

¹Yazd University

²University of Shiraz

Abstract

. Introduction
Landslide is introduced as one of the most important natural dangers in mountainous regions and induces heavy life and financial damages in life of mankind (Bui et al, 2012). Landslides in Iran are taken into account as one of the most common natural dangers, too and annually, induce many life and financial damages to the country and induces high cost to rebuild damaged regions on budget of the country (Zare et al, 2011). In view of undesired effect that this phenomenon has on social, economical and natural systems, knowing talented regions of slide is very necessary in level of the country. In view of dominant territory on the region, geology, being mountainous of Sardarabad watershed, non- principle building of roads and existence of 15 villages having inhabitants, Sardarabad watershed is such as talented watersheds of landslide and in view of landslides than other natural disasters like flood, volcano, and earthquake have capability of more management, so preparing map of zoning of landslide of the region is taken into account as one of important factors and tools in management and control of landslide danger. Using results of this research can prevent to further occur landslide and damage due to it in the region. Goal of the present research is to zone sensitivity of landslide by using adaptive neuro-fuzzy inference system with structure of fuzzy clustering inference system in Sardarabad watershed of Lorestan province.
2. Materials and Methods
The study area is the Sardarbad watershed in Khoram-Abad city, Lorestan Province. In order to carry out this research, first maps and basic images were obtained from the Natural Resources Office of Lorestan province. Spacing spots were recorded using aerial photographs (scale of 1: 20000) and field observations (using GPS) and entered the ArcGIS 10.2 software to provide land slide mapping in the area. Information layers for slope, altitude, gradient, distance from the road, distance from fault, distance from the waterway, rainfall, lithology and land use (as the affecting factors for landslide occurrence) were provided based on the basic maps (topography, geology and satellite imagery). The land use information layer was prepared from the Office of Natural Resources and Watershed of Lorestan Province. The geologic map and the distance from the fault are also provided by the geological map of the region (scale of 1: 100,000). The waterway layer was extracted from the topographic map of the study area. The gradient map was provided by topography map (1: 25000) and its digitizing was done in ArcGIS10.2 environment in three classes (concave, flat and convex).
In order to determine the amount of rainfall in the basin, all stations have been used with 30 years data. Due to the importance of rainfall in the occurrence of landslide, the rainfall map was prepared using rainfall data from existing stations from 1981 to 2011. In the Sardarabad watershed, the land use map was extracted from the ETM satellite data and completed by field operations (according to the report of the Natural Resources Department of Lorestan Province). In total, 109 sliding positions were identified using the database of the Office of Natural Resources and Watershed Management of the province, Google Earth images and field observations in the study area. After determining the effective factors, the mentioned maps and providing the data bank, landslide susceptibility map was obtained using the Anfis Model in ArcGIS (SAGA-GIS).
3. Results and Discussion
Based on the frequency ratio equation, the obtained weights were applied in 10 effective factors in landslide occurrence. Finally, using this equation, the level of correlation between the landslides and the effective factors (with a cell size of 30 meters) were obtained and was considered as the input weight of the Anfis model. After providing the weighted maps based on the model's equations, the landslide sensitivity map was obtained.
Finally, the landslide sensitivity map was obtained from the ANFIS method. Based on the natural fractures, landslide sensitivity map was classified to four classes (low, moderate, high sensitivity, Too much). A rock curve was used to obtain a suitable regional model for Sardarabad watershed. As the points are closer to the top and left (closer to 1), the model result is more appropriate and the predictions of model is closer to reality (Yesilnacar, 2005).
Overall, the division of 0.9-0.1 is excellent, 0.9-0.8, very good; 0.8-0.7, good; 0.6-0.7, moderate and 0.6-0.5; weak; (Zhu et al., 2009; Yesilnacar,2005). For this purpose, among the 109 identified landslides, 32 cases have been prepared for the model evaluation. According to Rock's results, the surface area under the curve of the study area using the adaptive neuro-fuzzy inference system is 89.11%, which indicates that the model lies in a very good class.
4. Conclusions
In this study, a nero-fuzzy model was used for landslide hazard zonation. The results of the Roc-Curve assessment in gaussian membership function showed that the surface under the curve is equal to 0.891. This means that the model sensitivity (89.1%) is correct. Also, due to the fact that the surface area under the curve ranged from 0.8 to 0.9, the accuracy of the model was in very good class. Overall, the study area has a high potential for landslide occurrence and without preventing and managing, these landslides annually cause damage to the road, residential areas and other resources in the region.
Geological factors, geomorphologic characteristics and the network of the canals are constant, and the only way to prevent their damage is bypassing these areas and not provoking these areas. Road factors and land use are one of the most effective factors in the occurrence of landslides in the area. They have more management ability. By constructing a road based on the conditions of the ecosystem and preventing the construction of unconventional roads and proper use in these areas, it is possible to prevent from their movement in the study area. The obtained map showed that the adaptive neuro-fuzzy inference system model has a high performance to provide the landslide sensitivity map.

Keywords

ANFIS; Land slide; ROC curve; Sardarabad Watershed

References

ابراهیمی، افسانه؛ شاد، روزبه؛ قائمی، مرجان؛ 1394. پیش‌بینی خطر زمین‌لغزش با استفاده از مدل سیستم استنتاج عصبی- فازی تطبیقی و GIS . نخستین همایش و نمایشگاه بین‌المللی ایمنی امنیت و مدیریت بحران در سوانح طبیعی.

اسفندیاری درآباد، فریبا؛ محمدی سلطان آباد، زهرا ؛ گل دوست، اکبر؛ 1393. اعتبارسنجی سیستم استنتاج تطبیقی عصبی- فازی (ANFIS) در برآورد فرسایش و رسوب (مطالعه موردی: حوضه نوران). اولین کنفرانس ملی جغرافیا، گردشگری، منابع طبیعی و توسعه پایدار، تهران، موسسه ایرانیان، قطب علمی برنامه ریزی وتوسعه پایدار گردشگری دانشگاه تهران،

پور قاسمی، حمیدرضا؛ 1386. پهنه‌بندی خطر زمین‌لغزش با استفاده از منطق فازی. پایان‌نامه کارشناسی ارشد آبخیزداری، دانشگاه تربیت مدرس، 115 ص.

پور قاسمی، حمیدرضا؛ مرادی، حمید رضا؛ فاطمی عقدا، سیدمحمود؛ 1391. تهیه نقشه حساسیت زمین‌لغزش با استفاده از سیستم استنتاج عصبی-فازی تطبیقی در شمال شهر تهران. فصلنامه علمی-پژوهشی پژوهش‌های دانش زمین، تهران، دانشگاه شهید بهشتی.

جوکار سرهنگی، عیسی؛ امیر احمدی، ابوالقاسم؛ سلمایان، حسین؛ 1376. پهنه‌بندی خطر زمین‌لغزش در حوزه آبخیز صفارود با استفاده از سیستم اطلاعات جغرافیایی. جغرافیا و توسعه ناحیه‌ای، شماره 9، 92-79.

زارع، محمد؛ احمدی، حسن؛ غلامی، شعبانلی؛ 1390. پهنه‌بندی و ارزیابی خطر زمین‌لغزش با استفاده از مدل‌های عامل اطمینان، ارزش اطلاعات و تحلیل سلسله مراتبی (مطالعه موردی: حوزه آبخیز واز). مجله علوم و مهندسی آبخیزداری ایران، سال پنجم، شماره 17،17-22.

شادفر، صمد؛ یمانی، مجتبی؛ 1386. پهنه‌بندی خطر زمین‌لغزش در حوزه آبخیز جلیسان با استفاده از مدل LNRF. پژوهش‌های جغرافیایی، شماره 39، 11-23.

صفاری، امیر ؛ اخدر، آرش؛ 1391. مقایسه مدل نسبت فراوانی و توابع عضویت فازی در پهنه‌بندی خطر زمین‌لغزش (مطالعه موردی: جاده ارتباطی مریوان - سنندج(، مجله جغرافیا و مخاطرات محیطی فردوسی مشهد، شماره 5، 79-96.

علیجانی، بهلول؛ قهرودی، منیژه ؛ امیر احمدی، ابوالقاسم؛ 1386. پهنه‌بندی خطر وقوع زمین‌لغزش در دامنه‌های شمالی شاه جهان با استفاده از GIS. فصلنامه تحقیقات جغرافیایی، شماره 84، 116-131.

فیض‌الله‌پور، مهدی؛ 1396. پهنه بندی مناطق مستعد به زمین لغزش با استفاده از سیستم استنتاجی فازی عصبی(ANFIS)(مطالعه موردی: حوضه رودخانه سنگورچای). مجله مخاطرات محیط طبیعی دانشگاه سیستان و بلوچستان، دوره 7،شماره 17، 155-174.

کشوری، فرامرز؛ شمس نیا، سید امیر؛ 1393. ارزیابی عملکرد مدل‌های هوش مصنوعی ( شبکه‌های عصبی مصنوعی و سیستم استنتاج فازی- عصبی تطبیقی (ANFIS)) در پیش بینی دبی ماهانه جریان رودخانه (مطالعه موردی : بند بهمن رودخانه قره آغاج). دومین همایش ملی بحران آب (تغییر اقلیم، آب و محیط زیست)، شهرکرد، دانشگاه شهرکرد.

مقدم نیا، علیرضا؛ زارع، محمد؛ تالی خشک، صادق؛ سلمانی، حسین؛ 1394. پهنه‌بندی حساسیت خطر زمین‌لغزش با استفاده از مدل نروفازی در حوزه آبخیز از. پژوهشنامه مدیریت حوزه آبخیز،. شماره 11 ، 101-110.

مقیمی، ابراهیم؛ علوی پناه، سید. کاظم؛ جعفری، تیمور؛ 1387. ارزیابی و پهنه‌بندی عوامل مؤثر در وقوع زمین‌لغزش دامنه‌های شمالی آلاداغ. پژوهش‌های جغرافیایی، شماره 64،53-75.

Aghdam, I.N., Varzandeh, M.H.M. & Pradhan, B., 2016. Landslide susceptibility mapping using an ensemble statistical index (Wi) and adaptive neuro-fuzzy inference system (ANFIS) model at Alborz Mountains (Iran. Environmental Earth Sciences, 75: 553.

Bui, D. T., Pradhan, B., Lofman, O., Revhaug, I., & Dick, O., 2012. Landslide susceptibility assessment in the HoaBinh province of Vietnam: A comparison of the Levenberg–Marquardt and Bayesian regularized neural networks. Geomorphology, 172(1), 12-29.

Chen, W., Panahi. M., & Pourghasemi., H.R., 2017. Performance evaluation of GIS-based new ensemble data mining techniques of adaptive neuro-fuzzy inference system (ANFIS) with genetic algorithm (GA), differential evolution (DE), and particle swarm optimization (PSO) for landslide spatial modelling. CATENA, 157, 310-324

Duman, TY., Can, T., Gokceoglu, C., Nefeslioglu, H.A., & Sonmez, H., 2006. Application of logistic regression for landslide susceptibility zoning of Cekmece Area. Istanbul Turkey, Environmental Geology, 51, 241-256.

Ercanoglu, M., & Candan, G. P., 2004. Use of fuzzy relation to produce landslide susceptibility map of a landslide prone area(West Black Sea Region, Turkey. Engineering Geology, 75, 229-250.

Feizizadeh, B., Shadman Roodposhti, M., Jankowski, P., & Blaschke,T., 2014. A GIS-based extended fuzzy multi-criteria evaluation for landslide susceptibility mapping. Journal Computers & Geosciences,73, 208–221.

Jang, J.S., 1993. ANFIS: adaptive-network-based fuzzy inference system, IEEE Trans System Manage Cybernet, 762-767.

Jouri, M.H., Zare, M., Askarizadeh, D., Fakhre Ghazi, M., Salarian, T., & Miarrostami, S., 2013. Landslide Susceptibility Mapping for Subalpine Grassland Using Frequency Ratio and Landslide Index Model (Case Study: Masoleh Watershed, Iran). Journal of Rangeland Science, 3(1),21-30.

Kartalopoulos, S.V., 1996. Understanding Neural Networks and Fuzzy Logic- Basic Concepts and Applications, Prentice Hall. New-Delhi Environmental Geology, 52, 615-623

Kayastha, P., 2012. Application of fuzzy logic lsndslide susceptibility mapping in Garuwa sub-basinEast Nepal. 6(4),420-432.

Komac, M.A., 2006. Landslide susceptibility model using the analytical hierarchy process method and multivariate statistics in perialpine Sloveni. Geomorphology, 17-28.

Lee, S., 2007. Application and verification of fuzzy algebraic operators landslide susceptibility mapping. Environmental Geology, 52, 615-623.

Leonardi, G. Palamara, R. Cirianni,F., 2016. Landslide Susceptibility Mapping Using a Fuzzy Approach. Procedia Engineering, 380–387.

Mathew, J., Jha,V., & Rawat, G. )2007( .Weights of evidence modeling for landslide hazard zonation mapping in part of Bhagirathi valley, Uttarakhand. Current Science, 92(5), 628-638.

Oh, H.J., &Pradhan, B. )2011(. Application of a neuro-fuzzy model to landslide-susceptibility mapping for shallow landslides in a tropical hilly area. Computers and Geosciences, 37(2), 1264-1276.

Polykretis, Ch., Chalkias, Ch., & Ferentinou, M., 2017. Adaptive neuro-fuzzy inference system (ANFIS) modeling for landslide susceptibility assessment in a Mediterranean hilly area. Bulletin of Engineering Geology and the Environment, 1–15.

Pourghasemi, H.R., Mohammady, M., & Pradhan, B., 2012. Landslide susceptibility mapping using index of entropy and conditional probability models in GIS: Safarood Basin Iran. Catena, 71–84.

Pradhan, B., 2010. Remote sensing and GIS-based landslide hazard analysis and cross validation using multivariate logistic regression model on three test areas in Malaysia. Advances in space research, 1244-1256.

Pradhan, B., 2013. A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS. Computer and Geosciences, 51, 350-365.

Salarian, T., Zare, M., Jouri, M.H., Miarrostami, S., & Mahmoudi, M., 2014. Evaluation of shallow landslides hazard using artificial neural network of Multi-Layer Perceptron method in Subalpine Grassland (Case study: Glandrood watershed - Mazandaran). International Journal of Agriculture and Crop Sciences, 795-804.

Sezer, E.A., Pradhan, B., & Gokceoglum, C., 2011. Manifestation of an adaptive neuro-fuzzy model on landslide susceptibility mapping: Klang valley, Malaysia. Expert Systems with Applications, 38(1),8208-8219

Swets, J. A., 1988. Measuring the accuracy of diagnostic systems. Science. 240. 1285–93.

Talebi, A., Troch, P. A., & Uijlenhoet, R., 2008. A steady-state analytical hillslope stability model for complex hillslops. Hydrological Procecces, 21(10).68-81.

Williams, C.J.S.S., Lee. R., Fisher, A., & Dickerman, L. H., 1999. A comparison of statistical methods for prenatal screening for Down syndrome. Applied Stochastic Models and Data Analysis, 89–101.

Yalcin, A., 2008. GIS-based landslide susceptibility mapping using analytical hierarchy process and bivariate statistics in Ardesen (Turkey): comparisons of results and confirmations. Catena, 1–12.

Yesilnacar, E.K., 2005. The application of computational intelligence to landslide susceptibility mapping in Turkey, Ph.D Thesis. Department of Geomatics the University of Melbourne.

Zare, M., Pourghasemi, H.R., Vafakhah, M., & Pradhan, B., 2012. Landslide susceptibility mapping at Vaz Watershed (Iran) using an artificial neural network model: a comparison between multilayer perceptron (MLP) and radial basic function (RBF) algorithms. Arabian Journal of Geosciences, 2873–2888.

Zhu, C., &Wang, X., 2009. Landslide susceptibility mapping: A comparison of information and weights-of evidence methods in Three Gorges Area. International Conference on Environmental Science and Information Application Technology. 187(10). 342-346