Landslide susceptibility zonation with Bayes’ theorem (weight of evidence) (Case study: siyahrood catchment)

Document Type : مقاله پژوهشی

Authors

• Fariba Esfandiyari Dorabad
• Ebrahim Beheshti javid
• Mohammad Hossein Fathi

mohaghegh Ardabili University

10.22067/geo.v3i4.27293

Abstract

Introduction:
Landslides are amongst the most damaging geologic hazards in the world. They pose a threat to the safety of humankind lives as Well as the environment, resources and property. Compared with other natural hazards such as volcanic eruptions and floods, landslides cause considerable damage to human beings and massive economic losses (Guzzetti, 2005). According to preliminary estimates, about 500 billion riyals annual are caused economic damage in Iran by landslide occurrence (Hosseinzadeh et al., 1388:27). Much literature available on landslide hazard assessment methodologies broadly falls into three main Approach groups: qualitative, quantitative and artificial intelligence (AI) approaches. in general, a qualitative approach is based on the subjective judgment of an Expert or a group of experts whereas the quantitative approach is based on mathematically rigorous objective Methodologies. Artificial Intelligence (AI) techniques can make use of heuristic knowledge or pattern matching technique as opposed to solving a set of mathematical equations. The AI broadly covers Artificial Neural Networks (ANN), Expert system, and other heuristic knowledge-based or rules-based techniques. (Neaupane and Piantanakulchai 2006:281). For the Landslide Susceptibility Mapping can be used a variety of models, such as logistic regression, Analytical Hierarchy Process (AHP), Analytic Network process (ANP), artificial neural network, the bivariate statistical models, LNRF, fuzzy logic models and etc. Usually choose the most appropriate approach and model is done based on the data type, the scale of the study area, the scale of analysis and Knowledge of researcher.
Study area:
In this study siyahrood catchment has Zonation for landslide susceptibility by using weights of evidence models (Bayes' theorem). The basin is located in the province of Gilan. The catchment area is 437 km and is sub-basins of the Sefidrood River.
Materials and methods:
The weight of evidence (WofE) method (Bonham-Carteretal.,1989) has been used to evaluate shallow-landslide susceptibility and has been tested as a useful spatial data model for various applications including mass-movement studies, mineral research, and groundwater spring mapping (Mark and Ellen, 995; Poli and Sterlacchini,2007; Barbieri and Cambuli, 2009). .It takes into account the relationships existing amongst the occurrence of a supporting evidence (shallow landslides in this study) and the distribution of causal factors (shallow-landslide predisposing factors in this study) The WofE is a statistical method based on the Bayes' theorem (Denison et al., 2002). This methods first applied to mineral exploration in1988 (Bonham-Carter et al., 1988) and Subsequently, Van Western (2002) applied the method for Landslide susceptibility assessment. Bayes’ theorem can be written as:
Equation (1) P(s│B_i )= (P(B_i│s)×P(s))/(P(B_i))
Where P (Bi | s) is the conditional probability to have Bi given s, P (s) is the prior probability to find s within the study area (AS) and P (Bi) is the prior probability to find the class Bi within the study area AS. In this study landslide susceptibility zonation has been done using several natural and anthropogenic parameters Such as (lithology, distance from fault, distance from river, rainfall, land slope, aspect, land use, vegetation density (NDVI) and sediment transport index (STI) stream power index (SPI) and topographic wetness index (TWI )).
Results and discussion:
After the Weights classes were obtained using the model for each parameter, Weights was applied to each class in the Arc map software and eventually with overlay parameters was obtained landslide susceptibility maps. The final Maps was classified In 5 susceptibility class using the method of natural breaks (very low susceptibility, low susceptibility, moderate susceptibility, high susceptibility and high susceptibility). According to the results of the model and the map developed in the lithology layer, Most of the weight is allocated to Class B (old alluvial terraces and High alluvial fans). Moderate range among the different classes of land use and north and northwest directions in the aspect parameter Have the greatest impact on landslide occurrence. As well as slope of 20-10 degrees and 10-5 degrees, respectively and in the layer distance from the river, 100 meters from the river have the greatest impact in landslide occurrence.
Conclusion:
Assessment models with using landslides occurred in the area show that with increasing risk class, landslide density in the class increases And 59 % of landslide, has occurred in very high susceptibility class. While the area of this class compared to total area of the region is only 10.5 percent. Although Classes with very low susceptibility, low and moderate susceptibility are included approximately 71 percent Area of a Region, But only a small portion of the landslides occurred (16.7%) in these classes While the roughly 83. 3 percent of landslides occurred in the area are located in the fourth and fifth class (high and very high susceptibility). Due to this can be said that the model has a good functionality in the area terms of the prediction of landslides.

Keywords

• weight of evidence
• siyahrood catchment
• Bayes’ theorem
• Landslide

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References

امیر احمدی، ابوالقاسم؛ کامرانی دلیر، حمید؛ صادقی، محسن؛ 1389. پهنه‌بندی خطر زمین‌لغزش با استفاده از روش تحلیل سلسله مراتبی (AHP)، مطالعه موردی حوضه آبخیز چلاو آمل. فصلنامه علمی پژوهشی انجمن جغرافیای ایران، شماره 27. صص203-181.

پورقاسمی، حمیدرضا؛ مرادی، حمیدرضا؛ محمدی، مجید؛ مصطفی‌زاده رئوف، گلی جیرنده؛ 1391. پهنه بندی خطر زمین‌لغزش با استفاده از تئوری بیزین. مجله علوم و فنون کشاورزی و منابع طبیعی. علوم آب‌وخاک. سال شانزدهم شماره شصت و دوم. صص 121-109.

حسین زاده، محمدمهدی؛ ثروتی محمدرضا؛ منصوری عادل؛ میرباقری بابک؛ خضری سعید؛ 1388. پهنه‌بندی ریسک وقوع حرکات توده‌ای با استفاده از مدل رگرسیون لجستیک. فصلنامه زمین‌شناسی ایران. سال سوم شماره یازدهم. صفحات 27-37.

قرهی، حمیدرضا. بهلولی، بهمن؛ سیار، امیر؛ شریعت جعفری، محسن؛ 1390. تهیه نقشه حساسیت پدیده زمین‌لغزش با استفاده از تحلیل سلسله مراتبی و مدل آماری دومتغیره در مخزن سد البرز. مجله علوم زمین، پاییز 1390 سال 21 شماره 81، صفحات 93-100.

نیازی، یعقوب؛ اختصاصی، محمدرضا؛ طالبی، علی؛ آرخی، صالح؛ مختاری، محمدحسین؛ 1389. ارزیابی کارایی مدل آماری دومتغیره در پیش‌بینی خطر زمین‌لغزش در حوضه سد ایلام. مجله علوم مهندسی آبخیزداری ایران. سال 4 شماره 10. صص20-9.

یمانی، مجتبی؛ احمدآبادی، علی؛ زارع، غلامرضا؛1391. ارزیابی کارایی فنون هوش مصنوعی در مطالعات زمین‌لغزش با تأکید بر الگوریتم SVM (مطالعه موردی: حوضه آبریز درکه). فصلنامه جغرافیا و مخاطرات محیطی، سال 1، شماره 3.

Amirahmadi, A.a.gh., kamranidalir, H., sadeghi, M., 2011. Landslide hazard zonation. through hierarchical analysis method (ahp). Case Study: chelav basin. Geography 2. 182 - 203.

Barbieri, G., Cambuli, P., 2009. The weight of evidence statistical method in landslide susceptibility mapping of the Rio Pardu Valley (Sardinia, Italy). In: Anderssen, R.S., Braddock, R.D., Newham, L.T.H. (Eds.), 18th World IMACS Congress and MODSIM09 International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and NewZealand and International Association for Mathematics and Computers in Simulation. 2658–2664.

BonhamCarter, G.F., Agterberg, F.P., Wright, D.F., 1989. Weights of evidence modelling: a new approach to mapping mineral potential. In: Agterberg, F.P., Bonham-Carter. G.F. (Eds.). Statistical applications in the Earth Sciences: Geological Survey of Canada 89. 171–183.

Bonham-Carter, G.F., Agterberg, F.P. and Wright, D.F., 1988. Integration of geological datasets for gold exploration in Nova Scotia. Photogrammetry and Remote Sensing 54, 1585-1592.

Denison, D.G.T., Holmes, C.C., Mallick, B.K., Smith, A.F.M., 2002. Bayesian methods for nonlinear classification and regression. John Wiley & Sons, Chichester, west Sussex.

Gharahi h., bohlooli b., sayyar a., shariat jafari m., 2011. Landslide susceptibility mapping in the reservoir of alborz dam using analytical hierarchy process and bivariate statistics. Geosciences 81. 93 - 100.

Gorum, T., Gonencgil, B., Gokceoglu, C., Nefeslioglu, H.A., 2008. Implementation of reconstructed geomorpholog icunitsin landslide susceptibility mapping: the Melen Gorge (NWTurkey). Natural Hazards 46, 323–351.

Grabs, T., Seibert, J., Laudon, H., 2007. Modelling spatial patterns of saturated areas: a comparison of the topographic wetness index and a distributed model. Journal of Hydrology 373. 15-23.

Guzzetti, F., 2005. Landslide hazard and risk assessment. Ph. D Dissertation. Bonn.

Hosenzadeh. M.m., servatie. M.r., mansurie. A., mirbagherie. B., khezrie. S., 2009. Zoning risk of mass movements using a logistic regression model. Journal of Geology 11. 27 - 37.

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

Mohammadi, M., Pourghasemi, H.R., Pradhan, B., 2012. Landslide susceptibility mapping at Golestan Province, Iran: Acomparison Between frequency ratio, Dempster–Shafer, and weights-of-evidence models, Journal of Asian Earth Sciences 61, 221–236.

Moore, I.D., Burch, G.J., 1986. Sediment transport capacity of sheet and rill flow: application of unit stream power theory.Water Resource 22, 1350–1360.

Neaupane K.M., Piantanakulchai, M.2006: Analytic network process model for landslide hazard zonation, Engineering Geology 85, 281–294.

Nefeslioglu, H.A., Duman, T.Y., Duemaz, S., 2008. Landslide susceptibility mapping for a part of tectonic Kelkit Valley (Eastern Black Sea region of Turkey). Geomorphology 94, 401–418.

Niyazi. Y., ekhtesasie. M.r., talebi. A., saleh. a., mokhtarei. m.h., 2010. Assess the performance of the bivariate statistical model predicts landslide in Ilam dam basin. Journal of Engineering Science Watershed 10, 9 - 20.

Piacentinia, D., Troiani, F., Soldati, M., Notarnicola, C., Savelli, D., Schneiderbauer, S., Strada, C., 2012. Statistical analysis for assessing shallow-landslide susceptibility in South Tyrol (south-eastern Alps, Italy), Geomorphology 151, 196–206.

Poli, S., Sterlacchini, S., 2007. Landslide representation strategies in susceptibility Studies using weights-of-evidence modeling technique. Natural Resources Research, vol (16), 121–134.

Pourghasemi. H. R., Moradi, h. R., Mohammdi, m., Mostafazadeh, r., Goli jirandeh. a., 2013. Landslide hazard zoning using bayesian theory. Jwss - Isfahan University of technology 62, 109-120.

Sanwei, H., Peng, P., Lan, D., Haijun, W., Jiping, L., 2012. Application of kernel based Fisher discriminant analys is to map landslide Susceptibility in the Qinggan River delta, Three Gorges, China, Geomorphology 171, 30–41.

Schmidt, F., Persson, A., 2003. Comparison of DEM data capture and topographic wetness indices. Precision Agriculture vol (4), pp. 179–192.

Sorensen, R., Zinko, U., Seibert, J., 2006. On the calculation of the topographic wetnessindex: evaluation of different methods based on field observations. Hydrology and Earth System Sciences Discussions 2, 1807–1834.

Van Westen, C.J., 2002. Use of weights of evidence modeling for landslide susceptibility mapping. International Institute for Geoinformation Science and Earth Observation (ITC), Enschede, the Netherlands.

Wilson, J.P., Gallant, J.C., 2000, Digital terrain analysis. In: Wilson, J.P., Gallant, J.C. (Eds.), Terrain Analysis. John Wiley & Sons, NewYork, 1–27.

Yamani, M., Ahmadabadi, A., Zare, gh., 2012. The Zonation of landslide occurrence Using of Support Vector Machines algorithm (Case Study: Darakeh Basin), Geography and Environmental Hazards 3, 125 - 142.