Landslide Hazard Assessment and Zonation Using a Network Analysis (ANP) and the Fuzzy Logic Model (Case Study: Salavat Abad Basin Sanandaj)

Document Type : Research Article

Authors

1 Associate Professor, Department, Faculty of New Sciences and Technologies, University of Tehran, Iran

2 M.Sc. in Ecohydrology Engineering, Faculty of New Sciences and Technologies, University of Tehran, Iran

Abstract

Identifying areas susceptible to landslide through risk zoning is one of the most effective and necessary measures to reduce potential hazards and manage risks. Providing a landslide zoning map allows vulnerable areas to be identified and considered in environmental planning. The purpose of this study was to determine the landslide hazard zonation in Salavat Abad Basin of Sanandaj by providing information layers and effective factors on landslide hazard using fuzzy method and network analysis process (ANP). In order to zone the risk of landslide in Salavat Abad basin, slope, gradient, geology, land use, rainfall, distance from the river, distance from the fault, distance from the road were used for landslide risk zoning. The standard fuzzy layers are overlapped and organized in the GIS environment, and then the weight of effective factors is calculated by the ANP model and applied to the information layers for the GIS environment. By overlapping them, the landslide landslide zoning map in 5 floors is as follows: very sensitive, High, medium, low, and very low. The results of this assessment showed that the total area of ​​the Salavat Abad Basin with an average risk area of ​​804.77 ha (32.18%). Among the eight factors surveyed, the incidence of landslide was the gradient with weight (0.224) and the geological criterion with weight (0.194), the highest weight, and the distance from the fault with weight (0.036), the criterion of distance from The river with weight (0.058) and precipitation with weight (0.056) had the lowest weight for zoning the risk of landslide. The greatest risk of landslide is towards the slopes of the south, south east and east. Also, slopes higher than 10% had the highest risk of landslide.

Keywords

Main Subjects


[1].          Marrapu BM, Jakka RS. Landslide hazard zonation methods: A critical Review. Int J Civ Eng Res. 2014;5(3):215–20.
[2].          Lin W-T, Chou W-C, Lin C-Y. Earthquake-induced landslide hazard and vegetation recovery assessment using remotely sensed data and a neural network-based classifier: a case study in central Taiwan. Nat hazards. 2008;47(3):331–47.
[3].          Hussin HY, Zumpano V, Reichenbach P, Sterlacchini S, Micu M, van Westen C, et al. Different landslide sampling strategies in a grid-based bi-variate statistical susceptibility model. Geomorphology. 2016;253:508–23.
[4].          Shadman Roodposhti M, Aryal J, Shahabi H, Safarrad T. Fuzzy shannon entropy: a hybrid GIS-based landslide susceptibility mapping method. Entropy. 2016;18(10):343.
[5].          Karimi H, Naderi F, Naseri B, Salajeqeh A. Comparisons of different models for landslide susceptibility mapping in Zangvan watershed, Ilam province. J Range Watershed Manag. 2014;67(3):459–85.
[6].          Young, O. C., Cheung, K. & J-Chul UC. The Comparative Research of Landslide Susceptibility Mapping Using FR, AHP, LR, ANN. In: conference Environmental Geology San Diego, CA. 2010.
[7].          Colkesen I, Sahin EK, Kavzoglu T. Susceptibility mapping of shallow landslides using kernel-based Gaussian process, support vector machines and logistic regression. J African Earth Sci. 2016;118:53–64.
[8].          Gorsevski P V, Brown MK, Panter K, Onasch CM, Simic A, Snyder J. Landslide detection and susceptibility mapping using LiDAR and an artificial neural network approach: a case study in the Cuyahoga Valley National Park, Ohio. Landslides. 2016;13(3):467–84.
[9].          Bui DT, Pradhan B, Revhaug I, Nguyen DB, Pham HV, Bui QN. A novel hybrid evidential belief function-based fuzzy logic model in spatial prediction of rainfall-induced shallow landslides in the Lang Son city area (Vietnam). Geomatics, Nat Hazards Risk. 2015;6(3):243–71.
[10].        Lian C, Zeng Z, Yao W, Tang H, Chen CLP. Landslide displacement prediction with uncertainty based on neural networks with random hidden weights. IEEE Trans neural networks Learn Syst. 2016;27(12):2683–95.
[11].        Chen W, He B, Zhang L, Nover D. Developing an integrated 2D and 3D WebGIS-based platform for effective landslide hazard management. Int J Disaster Risk Reduct. 2016;20:26–38.
[12].        Wang Y, Seijmonsbergen AC, Bouten W, Chen Q. Using statistical learning algorithms in regional landslide susceptibility zonation with limited landslide field data. J Mt Sci. 2015;12(2):268–88.
[13].        Moghimi, E., Yamani, M., and Vahimi S. Landslide hazard assessment and zoning in Roodbar using network analysis process. Quant Res. 2013;4:103–18.
[14].        Safaeepoor M, Shojaeean A, Atashafrooz N. Landslide Using AHP Model In GIS Case Study: Valley Village Qalandar Flour City Dehdez. 2016;9(31):105–18.
[15].        Nazmfar H, Behesti A. Application of Combined model analytical network process and fuzzy logic models in Landslide susceptibility zonation (Case Study: chellichay Catchment). Geogr Environ Plan. 2016;27(1):53–68.
[16].        Masomeh Rajabi, KHalil Valizaeh kamran H abedi gheshlaghi. Evaluation and zoning landslide hazard by using the analysis network process and artificial neural network (case study Azarshahr Chay basin). Quant Geomorphol Res. 2018;5(1):60–74.
[17].        Goetz JN, Brenning A, Petschko H, Leopold P. Evaluating machine learning and statistical prediction techniques for landslide susceptibility modeling. Comput Geosci. 2015;81:1–11.
[18].        Shahram Rostaei LKh. Assessment of Analysis Network Process and Logistic Regression in the Investigation of Landslide Potential in the Axis Range and Reservoir Dam (Case Study: Ghalea Chai Dam). Quant Geomorphol Res. 2018;5(3):67–80.
[19].        Hadi Nayeri, Mohammadreza Karami MS. Landslide hazard zonation by evaluating environmental variables using network analysis (case study: Bijar city). Quant Geomorphol Res. 2018;5(4):121–36.
[20].        Ali Akbar Matkan ARSP. Urban Waste Iandfill Site Selection by GIS (Case Study: Tabriz City). Environ Sci. 2009;6(2):121–32.
[21].        Salari M, Moazed H RF. Site Selection for Solid Waste by GIS & AHP-FUZZY Logic (Case Study: Shiraz City). J Toloo-e-behdasht. 2012;11(1):96–109.
[22].        M. Abedini, SH. Rostaeai MF. Landslides susceptibility mapping using hybrid model of Bayes’ theorem & ANP, Case Study: Ahar drainage basin South boundary (From Nasirabad to Sattar Khan dam). Quant Geomorphol Res. 2018;5(1):142–59.
Volume 6, Issue 4
January 2020
Pages 993-1002
  • Receive Date: 09 February 2019
  • Revise Date: 21 July 2019
  • Accept Date: 21 July 2019
  • First Publish Date: 22 December 2019
  • Publish Date: 22 December 2019