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
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Akgun A (2011) A comparison of landslide susceptibility maps produced by logistic regression, multi-criteria decision, and likelihood ratio methods: a case study at İzmir, Turkey. Landslides, doi: 10.1007/s10346-011-0283-7
Akgun A, Bulut F (2007) GIS-based landslide susceptibility for Arsin-Yomra (Trabzon, North Turkey) region. Environ Geol 51(8):1377–1387
Akgun A, Turk N (2010) Landslide susceptibility mapping for Ayvalik (Western Turkey) and its vicinity by multi-criteria decision analysis. Environ Earth Sci 61(3):595–611
Akgun A, Dag S, Bulut F (2008) Landslide susceptibility mapping for a landslide-prone area (Findikli, NE of Turkey) by likelihood frequency ratio and weighted linear combination models. Environ Geol 54(6):1127–1143
Akgun A, Kıncal C, Pradhan B (2011) Application of remote sensing data and GIS for landslide risk assessment as an environmental threat to Izmir city (west Turkey). Environ Monit Assess. doi: 10.1007/s10661-011-2352-8
Akgun A, Sezer EA, Nefeslioglu HA, Gokceoglu C, Pradhan B (2012) An easy-to-use MATLAB program (MamLand) for the assessment of landslide susceptibility using a Mamdani fuzzy algorithm. Comput Geosci 38(1):23–34
Aleotti P, Chowdhury R (1999) Landslide hazard assessment: summary review and new perspectives. Bull Eng Geol Environ 58:21–44
Althuwaynee OF, Pradhan B, Lee S (2012) Application of an evidential belief function model in landslide susceptibility mapping. Comput Geosci. doi: 10.1016/j.cageo.2012.03.003
Ayalew L, Yamagishi H (2005) The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan. Geomorphology 65:15–31
Ayalew L, Yamagishi H, Marui H, Kanno T (2005) Landslide in Sado Island of Japan: Part II. GIS-based susceptibility mapping with comparison of results from two methods and verifications. Eng Geol 81:432–445
Bai S, Lu G, Wang J, Zhou P, Ding L (2010) GIS-based rare events logistic regression for landslide-susceptibility mapping of Lianyungang, China. Environ Earth Sci 62(1):139–149
Basheer IA, Hajmeer M (2000) Artificial neural networks: fundamentals, computing, design and application. J Microb Meth 43:3–31
Bednarik M, Magulova B, Matys M, Marschalko M (2010) Landslide susceptibility assessment of the Kralovany–Liptovsky Mikulaš railway case study. Phys Chem Earth 35:162–171
Bui DT, Pradhan B, Lofman O, Revhaug I, Dick OB (2011) Landslide susceptibility mapping at Hoa Binh Province (Vietnam) using an adaptive neuro-fuzzy inference system and GIS. Comput Geosci. doi: 10.1016/j.cageo.2011.10.031
Bui DT, Pradhan B, Lofman O, Revhaug I, Dick OB (2012a) Landslide susceptibility assessment in the Hoa Binh Province of Vietnam: A comparison of the Levenberg-Marquardt and Bayesian regularized neural networks. Geomorphology. doi: 10.1016/j.geomorph.2012.04.023
Bui DT, Pradhan B, Lofman O, Revhaug I, Dick OB (2012b) Spatial prediction of landslide hazards in Hoa Binh province (Vietnam): a comparative assessment of the efficacy of evidential belief functions and fuzzy logic models. Catena 96:28–40
Can T, Nefeslioglu HA, Gokceoglu C, Snomez H, Duman TY (2005) Susceptibility assessment of shallow earth flows triggered by heavy rainfall at three sub catchments by logistic regression analyses. Geomorphology 72:250–271
Caniani D, Pascale S, Sado F, Sole A (2008) Neural networks and landslide susceptibility: a case study of the urban area of Potenza. Nat Hazards 45:55–72
Champati Ray DP, Dimri S, Lakhera RC, Sati S (2007) Fuzzy-based method for landslide hazard assessment in active seismic zone of Himalaya. Landslides 4:101–111
Chauhan S, Sharma M, Arora M, Gupta N (2010) Landslide susceptibility zonation through ratings derived from artificial neural network. Intl J Appl Earth Observ Geoinf 12:340–350
Chung CJF, Fabbri AG (2003) Validation of spatial prediction models for landslide hazard mapping. Nat Hazards 30:451–472
Congalton RG (1991) A review of assessing the accuracy of classifications of remotely sensed data. Remote Sens Environ 37:35–46
Constantin M, Bednarik M, Jurchescu MC, Vlaicu M (2010) Landslide susceptibility assessment using the bivariate statistical analysis and the index of entropy in the Sibiciu Basin (Romania). Environ Earth Sci. doi: 10.1007/s12665-010-0724-y
Dahal RK, Hasegawa S, Nonomura S, Yamanaka M, Masuda T, Nishino K (2008) GIS-based weights-of-evidence modelling of rainfall-induced landslides in small catchments for landslide susceptibility mapping. Environ Geol 54(2):314–324
Dai FC, Lee CF (2002) Landslide characteristics and slope instability modeling using GIS, Lantau Island, Hong Kong. Geomorphology 42:213–228
Dowla FU, Rogers LL (1995) Solving problems in environmental engineering and geosciences with artificial neural networks. MIT, Cambridge
Duman TY, Can T, Gokceoglu C, Nefeslioglu HA, Sonmez H (2006) Application of logistic regression for landslide susceptibility zoning of Cekmece Area, Istanbul, Turkey. Environ Geol 51:241–256
Ercanoglu M, Gokceoglu C (2002) Assessment of landslide susceptibility for a landslide-prone area (north of Yenice, NW Turkey) by fuzzy approach. Environ Geol 41:720–730
Ercanoglu M, Gokceoglu C (2004) Use of fuzzy relation to produce landslide susceptibility map of a landslide prone area (West Black Sea Region, Turkey). Eng Geol 75:229–250
Ermini L, Catani F, Casagli N (2005) Artificial neural networks applied to landslide susceptibility assessment. Geomorphology 66:327–343
Erner A, Sebnem H, Duzgun B (2010) Improvement of statistical landslide susceptibility mapping by using spatial and global regression methods in the case of More and Romsdal (Norway). Landslides 7:55–68
Falaschi F, Giacomelli F, Federici PR, Puccinelli A, D’Amato Avanzi G, Pochini A, Ribolini A (2009) Logistic regression versus artificial neural networks: landslide susceptibility evaluation in a sample area of the Serchio River valley, Italy. Nat Hazards 50:551–569
Felicisimo A, Cuartero A, Remondo J, Quiros E (2012) Mapping landslide susceptibility with logistic regression, multiple adaptive regression splines, classification and regression trees, and maximum entropy methods: a comparative study. Landslides. doi: 10.1007/s10346-012-0320-1
Fell R, Corominas J, Bonnard C, Cascini L, Leroi E, Savage P (2008) Guidelines for landslide susceptibility, hazard and risk zonation for land-use planning. Eng Geol 102:99–111
Garrett J (1994) Where and why artificial neural networks are applicable in civil engineering. Civil Eng 8:129–130
Gokceoglu C, Sonmez H, Nefeslioglu HA, Duman TY, Can T (2005) The 17 March 2005 Kuzulu landslide (Sivas, Turkey) and landslide susceptibility map of its near vicinity. Eng Geol 81:65–83
Gomez H, Kavzoglu T (2005) Assessment of shallow landslide susceptibility using artificial neural networks in Jabonosa River Basin, Venezuela. Eng Geol 78:11–27
Gong P (1996) Integrated analysis of spatial data for multiple sources: using evidential reasoning and artificial neural network techniques for geological mapping. Photogram Eng Rem Sens 62:513–523
Gorsevski PV, Jankowski P (2010) An optimized solution of multi-criteria evaluation analysis of landslide susceptibility using fuzzy sets and Kalman filter. Comput Geosci 36:1005–1020
Gorsevski PV, Gessler PE, Foltz RB, Elliot WJ (2006) Spatial prediction of landslide hazard using logistic regression and ROC analysis. Trans GIS 10(3):395–415
Gritzner ML, Marcus WA, Aspinall R, Custer SG (2001) Assessing landslide potential using GIS, soil wetness modeling and topographic attributes, Payette River, Idaho. Geomorphology 37:149–165
Guzzeti F, Carrara A, Cardinalli M, Reichenbach P (1999) Landslide hazard evaluation: a review of current techniques and their application in a multi-case study, central Italy. Geomorphology 31:181–216
Haykin S (1994) Neural networks: a comprehensive foundation. Macmillan, New York
Kanungo DP, Arora MK, Sarkar S, Gupta RP (2006) A comparative study of conventional, ANN black box, fuzzy and combined neural and fuzzy weighting procedures for landslide susceptibility zonation in Darjeeling Himalayas. Eng Geol 85:347–366
Kanungo DP, Arora MK, Gupta RP, Sarkar S (2008) Landslide risk assessment using concepts of danger pixels and fuzzy set theory in Darjeeling Himalayas. Landslides 5:407–416
Kavzoglu T, Mather PM (2000) Using feature selection techniques to produce smaller neural networks with better generalization capabilities. Proc IEEE 2000 Int Geosci Rem Sens Symp, Hawaii 3:3069–3071
Kawabata D, Bandibas J (2009) Landslide susceptibility mapping using geological data, a DEM from ASTER images and an artificial neural network (ANN). Geomorphology 113:97–109
Lee S, Evangelista DG (2006) Earthquake-induced landslide-susceptibility mapping using an artificial neural network. Nat Hazards Earth Syst Sci 6:687–695
Lee S, Sambath T (2006) Landslide susceptibility mapping in the Damrei Romel area, Cambodia using frequency ratio and logistic regression models. Environ Geol 50(6):847–855
Lee S, Ryu JH, Kim IS (2007) Landslide susceptibility analysis and its verification using likelihood ratio, logistic regression, and artificial neural network models: case study of Youngin, Korea. Landslides 4(4):327–338
Lee S, Choi J, Oh H (2009) Landslide susceptibility mapping using a neuro-fuzzy. Abstract presented at American Geophysical Union, Fall Meeting 2009, abstract #NH53A-1075
Lee MJ, Choi JW, Oh HJ, Won JS, Park I, Lee, S (2012) Ensemble-based landslide susceptibility maps in Jinbu area, Korea. Environ Earth Sci (article in press)
Lek S, Guiresse M, Giraudel JL (1999) Predicting stream nitrogen concentration from watershed features using neural network. Wat Res 33(16):3469–3478
Looney CG (1996) Advances in feed forward neural networks: demystifying knowledge acquiring black boxes. IEEE Trans Knowl Data Eng 8(2):211–226
Marjanović M, Kovačević M, Bajat B, Voženílek V (2011) Landslide susceptibility assessment using SVM machine learning algorithm. Eng Geol 123:225–234
Masters T (1994) Practical neural network recipes in C++. Academic, Boston
Neaupane KM, Achet SH (2004) Use of back propagation neural network for landslide monitoring: a case study in the higher Himalaya. Eng Geol 74(3–4):213–226
Nefeslioglu HA, Gokceoglu C, Sonmez H (2008) An assessment on the use of logistic regression and artificial neural networks with different sampling strategies for the preparation of landslide susceptibility maps. Eng Geol 97:171–191
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. doi: 10.1155/2010/901095 , Article ID 901095
Negnevitsky M (2002) Artificial intelligence—a guide to intelligent systems. Addison-Wesley Co, Great Britain
Nelson M, Illingworth WT (1990) A practical guide to neural nets. Addison-Wesley, Reading
Nilsen TH, Wrigth RH, Vlasic TC, Spangle WE (1979) Relative slope stability and land-use planning in the San Francisco Bay region, California. US Geological Survey Professional Paper 944
Oh HJ, Pradhan B (2011) Application of a neuro-fuzzy model to landslide-susceptibility mapping for shallow landslides in a tropical hilly area. Comput Geosci 37:1264–1276
Oh HJ, Park NW, Lee SS, Lee S (2012) Extraction of landslide-related factors from ASTER imagery and its application to landslide susceptibility mapping. Int J Rem Sens 33(10):3211–3231
Palani S, Liong S, Tkalich P (2008) An ANN application for water quality forecasting. Mar Poll Bull 56:1586–1597
Paola JD, Schowengerdt RA (1995) A review and analysis of back propagation neural networks for classification of remotely sensed multi-spectral imagery. Int J Rem Sens 16:3033–3058
Park J, Sandberg IW (1993) Approximation and radial basis function networks. Neural Comput 5:305–316
Pourghasemi HR, Pradhan B, Gokceoglu C, Mohammadi M, Moradi HR (2012a) Application of weights-of-evidence and certainty factor models and their comparison in landslide susceptibility mapping at Haraz watershed, Iran. Arab J Geosci. doi: 10.1007/s12517-012-0532-7
Pourghasemi HR, Pradhan B, Gokceoglu C, Deylami Moezzi K (2012b) A comparative assessment of prediction capabilities of Dempster-Shafer and weights-of-evidence models in landslide susceptibility mapping using GIS. Geomat, Nat Hazards Risk. doi: 10.1080/19475705.2012.662915
Pourghasemi HR, Pradhan B, Gokceoglu C (2012c) Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran. Nat Hazard. doi: 10.1007/s11069-012-0217-2
Pourghasemi HR, Gokceoglu C, Pradhan B, Deylami Moezzi K (2012d) Landslide susceptibility mapping using a spatial multi criteria evaluation model at Haraz Watershed. In: Pradhan IB, Buchroithner M (eds) Terrigenous mass movements. Springer, Berlin, pp 23–49. doi: 10.1007/978-3-642-25495-6-2
Pourghasemi HR, Mohammady M, Pradhan B (2012e) Landslide susceptibility mapping using index of entropy and conditional probability models in GIS: Safarood Basin, Iran. Catena. doi: 10.1016/j.catena.2012.05.005
Pradhan B (2010) Application of an advanced fuzzy logic model for landslide susceptibility analysis. Int J Comput Intel Syst 3(3):370–381
Pradhan B (2011a) 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
Pradhan B (2011b) Manifestation of an advanced fuzzy logic model coupled with geoinformation techniques for landslide susceptibility analysis. Environ Ecol Stat 18(3):471–493
Pradhan B, Buchroithner MF (2010) Comparison and validation of landslide susceptibility maps using an artificial neural network model for three test areas in Malaysia. Environ Eng Geosci 16(2):107–126. doi: 10.2113/gseegeosci.16.2.107
Pradhan B, Lee S (2010a) Delineation of landslide hazard areas on Penang Island, Malaysia, by using frequency ratio, logistic regression, and artificial neural network models. Environ Earth Sci 60(5):1037–1054
Pradhan B, Lee S (2010b) Landslide susceptibility assessment and factor effect analysis: back-propagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modeling. Environ Modell Softw 25(6):747–759
Pradhan B, Lee S (2010c) Regional landslide susceptibility analysis using back-propagation neural network model at Cameron Highland, Malaysia. Landslides 7(1):13–30
Pradhan B, Pirasteh S (2010) Comparison between prediction capabilities of neural network and fuzzy logic techniques for landslide susceptibility mapping. Disaster Adv 3(2):26–34
Pradhan B, Youssef AM (2010) Manifestation of remote sensing data and GIS on landslide hazard analysis using spatial-based statistical models. Arab J Geosci 3(3):319–326
Pradhan B, Oh HJ, Buchroithner MF (2010) Weights-of-evidence model applied to landslide susceptibility mapping in a tropical hilly area. Geomat, Nat Hazards Risk 1(3):199–223
Saito H, Nakayama D, Matsuyama H (2009) Comparison of landslide susceptibility based on a decision-tree model and actual landslide occurrence: the Akaishi Mountains, Japan. Geomorphology 109:108–121
Sezer EA, Pradhan B, Gokceoglu C (2011) Manifestation of an adaptive neuro-fuzzy model on landslide susceptibility mapping: Klang Valley, Malaysia. Exp Syst Appl 38:8208–8219
Soeters R, Van Westen CJ (1994) Slope stability: recognition, analysis and zonation. In: Turner AK, Shuster RL (eds) “Landslides: investigation and mitigation”. Transportation research Board–National Research Council. Special Report 247:129–177
Song KY, Oh HJ, Choi J, Park I, Lee C, Lee S (2012) Prediction of landslide using ASTER imagery and data mining models. Adv Space Res 49:978–993
Swingler K (1996) Applying neural networks: a practical guide. Academic, New York
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 36:1101–1114
Van Westen CJ, Rengers N, Soeters R (2003) Use of geomorphological information in indirect landslide susceptibility assessment. Nat Hazards 30:399–419
Varnes DJ (1978) Slope movement types and processes. In: Schuster RL, Krizek RJ (eds) Landslides analysis and control. Special Report, vol 176. Transportation Research Board, National Academy of Sciences, New York, pp 12–33
Varnes DJ (1984) Landslide hazard zonation—a review of principles and practice. IAEG Commission on Landslides. UNESCO, Paris
Wan S (2009) A spatial decision support system for extracting the core factors and thresholds for landslide susceptibility map. Eng Geol 108:237–251
Wijesinghe P, Dias D (2008) Novel approach for RSS calibration in DCM-based mobile positioning using propagation models. 978-1-4244-2900-4/08/$25.00 c 2008 IEEE. 6pp
Xu C, Dai F, Xu X, Lee YH (2012) GIS-based support vector machine modeling of earthquake-triggered landslide susceptibility in the Jianjiang River watershed, China. Geomorphology. doi: 10.1016/j.geomorph.2011.12.040
Yao X, Tham LG, Dai FC (2008) Landslide susceptibility mapping based on support vector machine: a case study on natural slopes of Hong Kong, China. Geomorphology 101:572–582
Yeon YK, Han JG, Ryu KH (2010) Landslide susceptibility mapping in Injae, Korea, using a decision tree. Eng Geol 116:274–283
Yilmaz I (2009) A case study from Koyulhisar (Sivas-Turkey) for landslide susceptibility mapping by artificial neural networks. Bull Eng Geol Environ 68(3):297–306
Yılmaz 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
Yilmaz I (2010a) The effect of the sampling strategies on the landslide susceptibility mapping by conditional probability (CP) and artificial neural network (ANN). Environ Earth Sci 60:505–519
Yilmaz I (2010b) Comparison of landslide susceptibility mapping methodologies for Koyulhisar, Turkey: conditional probability, logistic regression, artificial neural networks, and support vector machine. Environ Earth Sci 61:821–836
Yilmaz I, Marschalko M, Bednarik M, Kaynar O, Fojtova L (2011) Neural computing models for prediction of permeability coefficient of coarse grained soils. Neural Comput Appl. doi: 10.1007/s00521-011-0535-4