Hazard zoning for spatial planning using GIS-based landslide susceptibility assessment: a new hybrid integrated data-driven and knowledge-based model

Arabian Journal of Geosciences - Tập 12 Số 4 - 2019
Qadir Ashournejad1, ‌Ali Hosseini2, Biswajeet Pradhan3, Seyed Javad Hosseini4
1Department of Remote Sensing and GIS, University of Tehran, Tehran, 1417854151, Iran
2Department of Geography and Urban Planning, University of Tehran, Enghelab Avenue, Tehran, 1417854151, Iran
3The Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and IT, University of Technology Sydney, Sydney, NSW, 2007, Australia
4Department of Physical Geography, University of Tehran, Tehran, 1417854151, Iran

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Aghda SF, Bagheri V (2015) Evaluation of earthquake-induced landslides hazard zonation methods: a case study of Sarein, Iran, earthquake (1997). Arab J Geosci 8(9):7207–7227

Akgün A, Türk N (2011) Mapping erosion susceptibility by a multivariate statistical method: a case study from the Ayvalık region, NW Turkey. Computers & geosciences 37(9):1515–1524

Alkhasawneh MS, Ngah UK, Tay LT, Isa M, Ashidi N, Al-Batah MS (2014) Modeling and testing landslide hazard using decision tree. J Appl Math 2014:1–9

Alvioli M, Marchesini I, Reichenbach P, Rossi M, Ardizzone F, Fiorucci F, Guzzetti F (2016) Automatic delineation of geomorphological slope units with r. slopeunits v1. 0 and their optimization for landslide susceptibility modeling. Geosci Model Dev 9(11):3975–3991

Atkinson PM, Massari R (1998) Generalised linear modelling of susceptibility to landsliding in the central Apennines, Italy. Comput Geosci 24(4):373–385

Atkinson PM, Massari R (2011) Autologistic modelling of susceptibility to landsliding in the central Apennines, Italy. Geomorphology 130(1–2):55–64

Avalon Cullen C, Al-Suhili R, Khanbilvardi R (2016) Guidance index for shallow landslide hazard analysis. Remote Sens 8(10):866

Ayalew L, Yamagishi H, Ugawa N (2004) Landslide susceptibility mapping using GIS-based weighted linear combination, the case in Tsugawa area of Agano River, Niigata Prefecture, Japan. Landslides 1(1):73–81

Ayenew T, Barbieri G (2005) Inventory of landslides and susceptibility mapping in the Dessie area, northern Ethiopia. Eng Geol 77(1–2):1–15

Bai SB, Wang J, Lu GN, Zhou PG, Hou SS, Xu SN (2009) GIS-based and data-driven bivariate landslide-susceptibility mapping in the Three Gorges area, China. Pedosphere 19(1):14–20

Bai SB, Wang J, Lü GN, Zhou PG, Hou SS, Xu SN (2010) GIS-based logistic regression for landslide susceptibility mapping of the Zhongxian segment in the Three Gorges area, China. Geomorphology 115(1–2):23–31

Basnet BB, Apan AA, Raine SR (2001) Selecting suitable sites for animal waste application using a raster GIS. Environ Manag 28(4):519–531

Behnia P (2007) Application of radial basis functional link networks to exploration for Proterozoic mineral deposits in Central Iran. Nat Resour Res 16(2):147–155

Bibalani GH, Majnonian B, Adeli E, Sanii H (2006) Slope stabilization with Gleditshia caspica and Parrotia persica. Int J Environ Sci Technol 2(4):381–385

Bui DT, Ho TC, Pradhan B, Pham BT, Nhu VH, Revhaug I (2016) GIS-based modeling of rainfall-induced landslides using data mining-based functional trees classifier with AdaBoost, Bagging, and MultiBoost ensemble frameworks. Environ Earth Sci 75(14):1101

Bui DT, Nguyen QP, Hoang ND, Klempe H (2017) A novel fuzzy K-nearest neighbor inference model with differential evolution for spatial prediction of rainfall-induced shallow landslides in a tropical hilly area using GIS. Landslides 14(1):1–17

Chalkias C, Ferentinou M, Polykretis C (2014) GIS-based landslide susceptibility mapping on the Peloponnese Peninsula, Greece. Geosciences 4(3):176–190

Chen Z, Wang J (2007) Landslide hazard mapping using logistic regression model in Mackenzie Valley, Canada. Nat Hazards 42(1):75–89

Chen W, Panahi M, Pourghasemi HR (2017a) 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

Chen W, Pourghasemi HR, Zhao Z (2017b) A GIS-based comparative study of Dempster-Shafer, logistic regression and artificial neural network models for landslide susceptibility mapping. Geocarto International 32(4):367–385

Chen W, Peng J, Hong H, Shahabi H, Pradhan B, Liu J, Zhu AX, Pei X, Duan Z (2018a) Landslide susceptibility modelling using GIS-based machine learning techniques for Chongren County, Jiangxi Province, China. Sci Total Environ 626:1121–1135

Chen W, Xie X, Peng J, Shahabi H, Hong H, Bui DT, Li S, Zhu AX (2018b) GIS-based landslide susceptibility evaluation using a novel hybrid integration approach of bivariate statistical based random forest method. Catena 164:135–149

Conforti M, Pascale S, Robustelli G, Sdao F (2014) Evaluation of prediction capability of the artificial neural networks for mapping landslide susceptibility in the Turbolo River catchment (northern Calabria, Italy). Catena 113:236–250

Constantin M, Bednarik M, Jurchescu MC, Vlaicu M (2011) Landslide susceptibility assessment using the bivariate statistical analysis and the index of entropy in the Sibiciu Basin (Romania). Environmental earth sciences 63(2):397–406

Corominas J, & Mavrouli J (2011). Living with landslide risk in Europe: assessment, effects of global change, and risk management strategies. Guidelines for landslide susceptibility, hazard and risk zoning. Documento técnico, SafeLand. 7th Framework Programme Cooperation Theme 6 Environment (including climate change) Sub-Activity 6.1.3 Natural Hazards

Corsini A, Cervi F, Ronchetti F (2009) Weight of evidence and artificial neural networks for potential groundwater spring mapping: an application to the Mt. Modino area (Northern Apennines, Italy). Geomorphology 111(1–2):79–87

Couture R (2011) Landslide terminology—national technical guidelines and best practices on landslides. Geological Survey of Canada, p. 12

Daneshvar MRM, Bagherzadeh A (2011) Landslide hazard zonation assessment using GIS analysis at Golmakan watershed, northeast of Iran. Front Earth Sci 5(1):70–81

Erener A, Mutlu A, Düzgün HS (2016) A comparative study for landslide susceptibility mapping using GIS-based multi-criteria decision analysis (MCDA), logistic regression (LR) and association rule mining (ARM). Eng Geol 203:45–55

Fabbri AG, Chung CJF, Cendrero A, Remondo J (2003) Is prediction of future landslides possible with a GIS? Nat Hazards 30(3):487–503

Feizizadeh B, Roodposhti MS, Jankowski P, Blaschke T (2014) A GIS-based extended fuzzy multi-criteria evaluation for landslide susceptibility mapping. Comput Geosci 73:208–221

Fell R, Corominas J, Bonnard C, Cascini L, Leroi E, Savage WZ (2008) Guidelines for landslide susceptibility, hazard and risk zoning for land use planning. Eng Geol 102(3):85–98

Fernández DS, Lutz MA (2010) Urban flood hazard zoning in Tucumán Province, Argentina, using GIS and multicriteria decision analysis. Eng Geol 111(1–4):90–98

Gupta RP, Joshi BC (1990) Landslide hazard zoning using the GIS approach—a case study from the Ramganga catchment, Himalayas. Eng Geol 28(1–2):119–131

Guzzetti F, Reichenbach P, Ardizzone F, Cardinali M, Galli M (2006) Estimating the quality of landslide susceptibility models. Geomorphology 81(1–2):166–184

Hamza T, Raghuvanshi TK (2017) GIS based landslide hazard evaluation and zonation—a case from Jeldu District, Central Ethiopia. J King Saud Univ-Sci 29(2):151–165

Hong Y, Adler R, Huffman G (2007) Use of satellite remote sensing data in the mapping of global landslide susceptibility. Nat Hazards 43(2):245–256

Huabin W, Gangjun L, Weiya X, Gonghui W (2005) GIS-based landslide hazard assessment: an overview. Prog Phys Geogr 29(4):548–567

Jaafari A, Najafi A, Pourghasemi HR, Rezaeian J, Sattarian A (2014) GIS-based frequency ratio and index of entropy models for landslide susceptibility assessment in the Caspian forest, northern Iran. Int J Environ Sci Technol 11(4):909–926

Kavzoglu T, Sahin EK, Colkesen I (2014) Landslide susceptibility mapping using GIS-based multi-criteria decision analysis, support vector machines, and logistic regression. Landslides 11(3):425–439

Kayastha P, Dhital MR, De Smedt F (2013) Application of the analytical hierarchy process (AHP) for landslide susceptibility mapping: a case study from the Tinau watershed, west Nepal. Comput Geosci 52:398–408

Kıncal C, Akgun A, Koca MY (2009) Landslide susceptibility assessment in the Izmir (West Anatolia, Turkey) city center and its near vicinity by the logistic regression method. Environ Earth Sci 59(4):745–756

Kuncheva LI, Bezdek JC, Duin RP (2001) Decision templates for multiple classifier fusion: an experimental comparison. Pattern Recogn 34(2):299–314

Lan HX, Zhou CH, Wang LJ, Zhang HY, Li RH (2004) Landslide hazard spatial analysis and prediction using GIS in the Xiaojiang watershed, Yunnan, China. Eng Geol 76(1–2):109–128

Lee S (2007) Application and verification of fuzzy algebraic operators to landslide susceptibility mapping. Environ Geol 52(4):615–623

Lee S, Min K (2001) Statistical analysis of landslide susceptibility at Yongin, Korea. Environ Geol 40(9):1095–1113

Lee S, Ryu JH, Won JS, Park HJ (2004) Determination and application of the weights for landslide susceptibility mapping using an artificial neural network. Eng Geol 71(3–4):289–302

Leite EP, de Souza Filho CR (2009) Probabilistic neural networks applied to mineral potential mapping for platinum group elements in the Serra Leste region, Carajás Mineral Province, Brazil. Comput Geosci 35(3):675–687

Lepore C, Kamal SA, Shanahan P, Bras RL (2012) Rainfall-induced landslide susceptibility zonation of Puerto Rico. Environ Earth Sci 66(6):1667–1681

Ließ M, Glaser B, Huwe B (2012) Uncertainty in the spatial prediction of soil texture: comparison of regression tree and Random Forest models. Geoderma 170:70–79

Looney CG (2002) Radial basis functional link nets and fuzzy reasoning. Neurocomputing 48(1–4):489–509

Machiwal D, Rangi N, Sharma A (2015) Integrated knowledge-and data-driven approaches for groundwater potential zoning using GIS and multi-criteria decision making techniques on hard-rock terrain of Ahar catchment, Rajasthan, India. Environ Earth Sci 73(4):1871–1892

Maharaj RJ (1993) Landslide processes and landslide susceptibility analysis from an upland watershed: a case study from St. Andrew, Jamaica, West Indies. Eng Geol 34(1–2):53–79

Marinoni O (2004) Implementation of the analytical hierarchy process with VBA in ArcGIS. Comput Geosci 30(6):637–646

Melchiorre C, Matteucci M, Azzoni A, Zanchi A (2008) Artificial neural networks and cluster analysis in landslide susceptibility zonation. Geomorphology 94(3–4):379–400

Nazmfar H, Behesti A (2016) Application of combined model analytical network process and fuzzy logic models in landslide susceptibility zonation (case study: Chellichay catchment). Geogr Environ Plan 27(1):53–68

Nefeslioglu HA, Sezer E, Gokceoglu C, Bozkir AS, Duman TY (2010) Assessment of landslide susceptibility by decision trees in the metropolitan area of Istanbul, Turkey. Math Probl Eng 2010:1–15

Neuhäuser B, Damm B, Terhorst B (2012) GIS-based assessment of landslide susceptibility on the base of the weights-of-evidence model. Landslides 9(4):511–528

Nykänen V (2008) Radial basis functional link nets used as a prospectivity mapping tool for orogenic gold deposits within the Central Lapland Greenstone Belt, Northern Fennoscandian Shield. Nat Resour Res 17(1):29–48

Osna T, Sezer EA, Akgun A (2014) GeoFIS: an integrated tool for the assessment of landslide susceptibility. Comput Geosci 66:20–30

Ozdemir A, Altural T (2013) A comparative study of frequency ratio, weights of evidence and logistic regression methods for landslide susceptibility mapping: Sultan Mountains, SW Turkey. J Asian Earth Sci 64:180–197

Park I, Lee S (2014) Spatial prediction of landslide susceptibility using a decision tree approach: a case study of the Pyeongchang area, Korea. Int J Remote Sens 35(16):6089–6112

Perotto-Baldiviezo HL, Thurow TL, Smith CT, Fisher RF, Wu XB (2004) GIS-based spatial analysis and modeling for landslide hazard assessment in steeplands, southern Honduras. Agric Ecosyst Environ 103(1):165–176

Pham BT, Prakash I, Bui DT (2018) Spatial prediction of landslides using a hybrid machine learning approach based on random subspace and classification and regression trees. Geomorphology 303:256–270

Pourghasemi HR, Pradhan B, & Gokceoglu C (2012). Remote sensing data derived parameters and its use in landslide susceptibility assessment using Shannon’s entropy and GIS. In Applied Mechanics and Materials. Trans Tech Publications (Vol. 225, pp. 486–491)

Pradhan B (2011) Use of GIS-based fuzzy logic relations and its cross application to produce landslide susceptibility maps in three test areas in Malaysia. Environ Earth Sci 63(2):329–349

Radiarta IN, Saitoh SI, Miyazono A (2008) GIS-based multi-criteria evaluation models for identifying suitable sites for Japanese scallop (Mizuhopecten yessoensis) aquaculture in Funka Bay, southwestern Hokkaido, Japan. Aquaculture 284(1–4):127–135

Raja NB, Çiçek I, Türkoğlu N, Aydin O, Kawasaki A (2017) Landslide susceptibility mapping of the Sera River Basin using logistic regression model. Nat Hazards 85(3):1323–1346

Reichenbach P, Mondini AC, Rossi M (2014) The influence of land use change on landslide susceptibility zonation: the Briga catchment test site (Messina, Italy). Environ Manag 54(6):1372–1384

Reichenbach P, Rossi M, Malamud B, Mihir M, Guzzetti F (2018) A review of statistically-based landslide susceptibility models. Earth Sci Rev 180:60–91

Roodposhti MS, Rahimi S, Beglou MJ (2014) PROMETHEE II and fuzzy AHP: an enhanced GIS-based landslide susceptibility mapping. Nat Hazards 73(1):77–95

Ruff M, Czurda K (2008) Landslide susceptibility analysis with a heuristic approach in the Eastern Alps (Vorarlberg, Austria). Geomorphology 94(3):314–324

Saaty TL (2003) Decision-making with the AHP: why is the principal eigenvector necessary. Eur J Oper Res 145(1):85–91

Saaty TL, & Vargas LG (1991). Prediction, projection, and forecasting: applications of the analytic hierarchy process in economics, finance, politics, games, and sports. Kluwer Academic

Sabokbar HF, Roodposhti MS, Tazik E (2014) Landslide susceptibility mapping using geographically-weighted principal component analysis. Geomorphology 226:15–24

Sadeghi B, Khalajmasoumi M (2015) A futuristic review for evaluation of geothermal potentials using fuzzy logic and binary index overlay in GIS environment. Renew Sust Energ Rev 43:818–831

Samodra G, Chen G, Sartohadi J, Kasama K (2017) Comparing data-driven landslide susceptibility models based on participatory landslide inventory mapping in Purwosari area, Yogyakarta, Java. Environ Earth Sci 76(4):184

Schuster RL, 1996. Socioeconomic significance of landslides. In: Turner AK, Schuster RL (Eds.), Landslides: investigation and mitigation, Chapter 2, special report 247. Transportation Research Board, National Research Council. National Academy Press, Washington, DC, pp. 12–35

Shahabi H, Hashim M (2015) Landslide susceptibility mapping using GIS-based statistical models and remote sensing data in tropical environment. Sci Rep 5:9899

Smith K (2013). Environmental hazards: assessing risk and reducing disaster. Routledge

Srivastava V, Srivastava HB, Lakhera RC (2010) Fuzzy gamma based geomatic modelling for landslide hazard susceptibility in a part of Tons river valley, northwest Himalaya, India. Geomat Nat Haz Risk 1(3):225–242

Stavrakoudis DG, Dragozi E, Gitas IZ, Karydas CG (2014) Decision fusion based on hyperspectral and multispectral satellite imagery for accurate forest species mapping. Remote Sensing 6(8):6897–6928

Süzen ML, Doyuran V (2004) Data driven bivariate landslide susceptibility assessment using geographical information systems: a method and application to Asarsuyu catchment, Turkey. Eng Geol 71(3–4):303–321

Tangestani MH (2004) Landslide susceptibility mapping using the fuzzy gamma approach in a GIS, Kakan catchment area, southwest Iran. Aust J Earth Sci 51(3):439–450

Thanh LN, De Smedt F (2012) Application of an analytical hierarchical process approach for landslide susceptibility mapping in A Luoi district, Thua Thien Hue Province, Vietnam. Environ Earth Sci 66(7):1739–1752

Tissari S, Nykänen V, Lerssi J, Kolehmainen M (2007) Classification of soil groups using weights-of-evidence-method and RBFLN-neural nets. Nat Resour Res 16(2):159–169

Vahidnia MH, Alesheikh AA, Alimohammadi A, Hosseinali F (2010) A GIS-based neuro-fuzzy procedure for integrating knowledge and data in landslide susceptibility mapping. Comput Geosci 36(9):1101–1114

Van Den Eeckhaut M, Marre A, Poesen J (2010) Comparison of two landslide susceptibility assessments in the Champagne–Ardenne region (France). Geomorphology 115(1–2):141–155

Van Westen CJ, Van Asch TW, Soeters R (2006) Landslide hazard and risk zonation—why is it still so difficult? Bull Eng Geol Environ 65(2):167–184

Wang G, Zhang S, Yan C, Song Y, Sun Y, Li D, Xu F (2011) Mineral potential targeting and resource assessment based on 3D geological modeling in Luanchuan region, China. Comput Geosci 37(12):1976–1988

Weirich F, Blesius L (2007) Comparison of satellite and air photo based landslide susceptibility maps. Geomorphology 87(4):352–364

Yalcin A (2008) GIS-based landslide susceptibility mapping using analytical hierarchy process and bivariate statistics in Ardesen (Turkey): comparisons of results and confirmations. Catena 72(1):1–12

Yesilnacar E, Topal T (2005) Landslide susceptibility mapping: a comparison of logistic regression and neural networks methods in a medium scale study, Hendek region (Turkey). Eng Geol 79(3–4):251–266

Yilmaz I (2009) Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: a case study from Kat landslides (Tokat—Turkey). Comput Geosci 35(6):1125–1138

Yoshimatsu H, Abe S (2006) A review of landslide hazards in Japan and assessment of their susceptibility using an analytical hierarchic process (AHP) method. Landslides 3(2):149–158

Yu X, Wang Y, Niu R, Hu Y (2016) A combination of geographically weighted regression, particle swarm optimization and support vector machine for landslide susceptibility mapping: a case study at Wanzhou in the Three Gorges Area, China. Int J Environ Res Public Health 13(5):487

Zêzere JL, Pereira S, Melo R, Oliveira SC, Garcia RAC (2017) Mapping landslide susceptibility using data-driven methods. Sci Total Environ 589:250–267

Zhang K, Wu X, Niu R, Yang K, Zhao L (2017) The assessment of landslide susceptibility mapping using random forest and decision tree methods in the Three Gorges Reservoir area, China. Environ Earth Sci 76(11):405

Zhao H, Yao L, Mei G, Liu T, Ning Y (2017) A fuzzy comprehensive evaluation method based on AHP and entropy for a landslide susceptibility map. Entropy 19(8):396

Zhu AX, Wang RX, Qiao JP, Qin CZ, Chen YB, Liu J, Du F, Yang L, Zhu TX (2014) An expert knowledge-based model to landslide susceptibility mapping using GIS and fuzzy logic. Geomorphology 214:128–138

Zhu AX, Miao Y, Wang R, Zhu T, Deng Y, Liu J, Yang L, Qin CZ, Hong H (2018) A comparative study of an expert knowledge-based model and two data-driven models for landslide susceptibility mapping. Catena 166:317–327