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Rock fall Susceptibility Mapping Using Artificial Neural Network, Frequency Ratio, and Logistic Regression: A Case Study in Central Iran, Taft County | ||
| AUT Journal of Civil Engineering | ||
| مقاله 6، دوره 4، شماره 1، خرداد 2020، صفحه 63-80 اصل مقاله (2.63 M) | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.22060/ajce.2019.15368.5538 | ||
| نویسندگان | ||
| Maryam Mokhtari* 1؛ sahar Abedian2؛ Seyed Ahmad Almasi3 | ||
| 1Department of Civil Engineering, Faculty of Engineering, Yazd University, Iran | ||
| 2Instructor of University of Environmental Sciences, Department of Agriculture and Natural Resource, University of Payam e noor, Kerman branch | ||
| 3Department of Civil Engineering, Faculty of Engineering, Yazd University, Yazd, P.O. Box: 89195-741, Iran | ||
| چکیده | ||
| This study aims at evaluating and comparing the ability of Artificial Neural Network (ANN), Logistic Regression (LR), and Frequency Ratio (FR) methods to generate a rock fall susceptibility map for predicting the probability of rock fall occurrences in Taft County, a central region of Iran. The maps were prepared by assuming an association between rock fall susceptibility and nine factors including slope angle, slope aspect, elevation, land use, lithology, precipitation, distance from faults, distance from roads, and distance from streams. The performance of the methods was evaluated using the area under the operating characteristic curve (ROC), the precision of the predicted results (P), seed cell area index (SCAI) and statistical measures, including sensitivity, specificity, and accuracy. The area under the ROC was calculated for the frequency ratio, the artificial neural network, and logistic regression methods, and it was found to be 0.899, 0.893, and 0.883, respectively. An assessment of the parameter P also showed the high precision of all the three methods (particularly the frequency ratio method) for identifying high-susceptibility areas. It was also found that high-susceptibility classes had low SCAI values in all the methods, while low-susceptibility classes had higher SCAI values indicating acceptable performance of models. Overall, the results showed that the model developed by the FR method has better prediction accuracy than the ANN and LR methods. Decision makers can effectively use the findings of the present study to mitigate the financial and human cost resulting from the rockfalls. | ||
| کلیدواژه‌ها | ||
| Rock fall susceptibility map؛ Artificial neural network؛ Logistic regression؛ Frequency ratio؛ Taft County | ||
| مراجع | ||
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[1] M.J. Selby, Hillslope materials and processes, Oxford University Press, England, 1982.
[2] G. Abele, Large rockslides: their causes and movement on internal sliding planes, Mountain Research and Development, (1994) 315-320.
[3] D.J. Varnes, Landslide hazard zonation: a review of principles and practice, Unesco, Paris, 1984.
[4] B. Pradhan, An assessment of the use of an advanced neural network model with five different training strategies for the preparation of landslide susceptibility maps, Journal of data science, 9(1) (2011) 65-81.
[5] C. Keylock, U. Domaas, Evaluation of Topographic Models of Rockfall Travel Distance for Use in Hazard Applications, Arctic Antarctic and Alpine Research, 31(3) (1999) 312-320.
[6] Y. Kobayashi, E.L. Harp, T. Kagawa, Simulation of rockfalls triggered by earthquakes, Rock Mechanics and Rock Engineering, 23(1) (1990) 1-20.
[7] K.T. Chau, R.C.H. Wong, J. Liu, C.F. Lee, Rockfall hazard analysis for Hong Kong based on rockfall inventory, Rock Mech Rock Eng, 36 (2003) 383-408.
[8] K. Sassa, H. Fukuoka, G. Wang, N. Ishikawa, Undrained dynamic-loading ring-shear apparatus and application for landslide dynamics, Landslides, 1(1) (2004) 7-19.
[9] M. Fall, R. Azzam, C. Noubactep, A multi-method approach to study the stability of natural slopes and landslide susceptibility mapping, Engineering geology, 82(4) (2006) 241-263.
[10] S. Park, C. Choi, B. Kim, J. Kim, Landslide susceptibility mapping using frequency ratio, analytic hierarchy process, logistic regression, and artificial neural network methods at the Inje area, Korea, Environmental earth sciences, 68(5) (2013) 1443-1464.
[11] F. Dai, C. Lee, Terrain-based mapping of landslide susceptibility using a geographical information system: a case study, Canadian Geotechnical Journal, 38(5) (2001) 911-923.
[12] T.Y. Duman, T. Can, Ö. Emre, M. Keçer, A. Doğan, Ş. Ateş, S. Durmaz, Landslide inventory of northwestern Anatolia, Turkey, Engineering Geology, 77(1-2) (2005) 99-114.
[13] F. Baillifard, M. Jaboyedoff, M. Sartori, Rockfall hazard mapping along a mountainous road in Switzerland using a GIS-based parameter rating approach, Natural Hazards and Earth System Science, 3(5) (2003) 435- 442.
[14] F. Guzzetti, P. Reichenbach, S. Ghigi, Rockfall hazard and risk assessment along a transportation corridor in the Nera Valley, Central Italy, Environmental Management, 34(2) (2004) 191-208.
[15] I. Yilmaz, M. Yildirim, Structural and geomorphological aspects of the Kat landslides (Tokat—Turkey) and susceptibility mapping by means of GIS, Environmental Geology, 50(4) (2006) 461-472.
[16] H. Shahabi, M. Hashim, Landslide susceptibility mapping using GIS-based statistical models and Remote sensing data in tropical environment, Scientific reports, 5(1) (2015) 1-15.
[17] A. Shirzadi, K. Chapi, H. Shahabi, K. Solaimani, A. Kavian, B.B. Ahmad, Rock fall susceptibility assessment along a mountainous road: an evaluation of bivariate statistic, analytical hierarchy process and frequency ratio, Environmental Earth Sciences, 76(4) (2017) 152-169.
[18] L. Ayalew, H. Yamagishi, N. Ugawa, Landslide susceptibility mapping using GIS-based weighted linear combination, the case in Tsugawa area of Agano River, Niigata Prefecture, Japan, Landslides, 1(1) (2004) 73-81.
[19] F. Guzzetti, A. Carrara, M. Cardinali, P. Reichenbach, Landslide hazard evaluation: a review of current techniques and their application in a multi-scale study, Central Italy, Geomorphology, 31(1) (1999) 181-216.
[20] H. Kaur, S. Gupta, S. Parkash, Comparative evaluation of various approaches for landslide hazard zoning: a critical review in Indian perspectives, Spatial Information Research, 25(3) (2017) 389-398.
[21] R. Pellicani, I. Argentiero, G. Spilotro, GIS-based predictive models for regional-scale landslide susceptibility assessment and risk mapping along road corridors, Geomatics, Natural Hazards and Risk, 8(2) (2017) 1012-1033.
[22] H. Bourenane, Y. Bouhadad, M.S. Guettouche, M. Braham, GIS-based landslide susceptibility zonation using bivariate statistical and expert approaches in the city of Constantine (Northeast Algeria), Bulletin of Engineering Geology and the Environment, 74(2) (2015) 337-355.
[23] C. Baeza, J. Corominas, Assessment of shallow landslide susceptibility by means of multivariate statistical techniques, Earth Surface Processes and Landforms: The Journal of the British Geomorphological Research Group, 26(12) (2001) 1251-1263.
[24] A.M. Youssef, B. Pradhan, M.N. Jebur, H.M. El-Harbi, Landslide susceptibility mapping using ensemble bivariate and multivariate statistical models in Fayfa area, Saudi Arabia, Environmental Earth Sciences, 73(7) (2015) 3745-3761.
[25] S. Sarkar, A.K. Roy, T.R. Martha, Landslide susceptibility assessment using information value method in parts of the Darjeeling Himalayas, Journal of the Geological Society of India, 82(4) (2013) 351-362.
[26] H. Hong, W. Chen, C. Xu, A.M. Youssef, B. Pradhan, D. Tien Bui, Rainfall-induced landslide susceptibility assessment at the Chongren area (China) using frequency ratio, certainty factor, and index of entropy, Geocarto international, 32(2) (2017) 139-154.
[27] D. Kanungo, S. Sarkar, S. Sharma, Combining neural network with fuzzy, certainty factor and likelihood ratio concepts for spatial prediction of landslides, Natural hazards, 59(3) (2011) 1491-1512.
[28] K.C. Devkota, A.D. Regmi, H.R. Pourghasemi, K. Yoshida, B. Pradhan, I.C. Ryu, M.R. Dhital, O.F. Althuwaynee, Landslide susceptibility mapping using certainty factor, index of entropy and logistic regression models in GIS and their comparison at Mugling–Narayanghat road section in Nepal Himalaya, Natural hazards, 65(1) (2013) 135-165.
[29] H.Y. Hussin, V. Zumpano, P. Reichenbach, S. Sterlacchini, M. Micu, C. van Westen, D. Bălteanu, Different landslide sampling strategies in a grid-based bi-variate statistical susceptibility model, Geomorphology, 253 (2016) 508-523.
[30] H. Hong, I. Ilia, P. Tsangaratos, W. Chen, C. Xu, A hybrid fuzzy weight of evidence method in landslide susceptibility analysis on the Wuyuan area, China, Geomorphology, 290 (2017) 1-16.
[31] G.G. Iovine, R. Greco, S.L. Gariano, A.D. Pellegrino, O.G. Terranova, Shallow-landslide susceptibility in the Costa Viola mountain ridge (southern Calabria, Italy) with considerations on the role of causal factors, Natural hazards, 73(1) (2014) 111-136.
[32] H.R. Pourghasemi, B. Pradhan, C. Gokceoglu, Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran, Natural hazards, 63(2) (2012) 965- 996.
[33] M.H. Tangestani, A comparative study of Dempster–Shafer and fuzzy models for landslide susceptibility mapping using a GIS: An experience from Zagros Mountains, SW Iran, Journal of Asian Earth Sciences, 35(1) (2009) 66-73.
[34] A. Can, G. Dagdelenler, M. Ercanoglu, H. Sonmez, Landslide susceptibility mapping at Ovacık-Karabük (Turkey) using different artificial neural network models: comparison of training algorithms, Bulletin of Engineering Geology and the Environment, 78(1) (2017) 1-14.
[35] I. Yilmaz, A case study from Koyulhisar (Sivas-Turkey) for landslide susceptibility mapping by artificial neural networks, Bulletin of Engineering Geology and the Environment, 68(3) (2009) 297-306.
[36] W. Chen, H.R. Pourghasemi, S.A. Naghibi, A comparative study of landslide susceptibility maps produced using support vector machine with different kernel functions and entropy data mining models in China, Bulletin of Engineering Geology and the Environment, 77(2) (2018) 647-664.
[37] H. Hong, B. Pradhan, M.I. Sameen, W. Chen, C. Xu, Spatial prediction of rotational landslide using geographically weighted regression, logistic regression, and support vector machine models in Xing Guo area (China), Geomatics, Natural Hazards and Risk, 8(2) (2017) 1997-2022.
[38] D.T. Bui, B. Pradhan, O. Lofman, I. Revhaug, O.B. Dick, Landslide susceptibility mapping at Hoa Binh province (Vietnam) using an adaptive neuro-fuzzy inference system and GIS, Computers & Geosciences, 45 (2012) 199-211.
[39] C. Polykretis, C. Chalkias, M. Ferentinou, Adaptive neuro-fuzzy inference system (ANFIS) modeling for landslide susceptibility assessment in a Mediterranean hilly area, Bulletin of Engineering Geology and the Environment, 78(2) (2017) 1-15.
[40] N. Jia, Y. Mitani, M. Xie, I. Djamaluddin, Shallow landslide hazard assessment using a three-dimensional deterministic model in a mountainous area, Computers and Geotechnics, 45 (2012) 1-10.
[41] H. Saroglou, V. Marinos, P. Marinos, G. Tsiambaos, Rockfall hazard and risk assessment: an example from a high promontory at the historical site of Monemvasia, Greece, Natural Hazards and Earth System Sciences, 12(6) (2012) 1823-1836.
[42] D.T. Bui, B. Pradhan, O. Lofman, I. Revhaug, O.B. Dick, 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 (2012) 28-40.
[43] D.G. Price, Engineering geology: principles and practice, Springer, (2009) 268-290.
[44] P. Magliulo, A. Di Lisio, F. Russo, A. Zelano, Geomorphology and landslide susceptibility assessment using GIS and bivariate statistics: a case study in southern Italy, Natural hazards, 47(3) (2008) 411-435.
[45] A.R. Arabameri, A.H. Halabian, Landslide Hazard Zonation Using Statistical Model of AHP (Case Study: Zarand Saveh Basin), Physical Geomorphology, 28 (2015) 65-86.
[46] R. Giannecchini, Relationship between rainfall and shallow landslides in the southern Apuan Alps (Italy), Natural Hazards and Earth System Science, 6(3) (2006) 357-364.
[47] C. Henriques, J.L. Zêzere, F. Marques, F. Marques, The role of the lithological setting on the landslide pattern and distribution, Engineering Geology, 189 (2015) 17-31.
[48] H.R. Pourghasemi, M. Mohammady, B. Pradhan, Landslide susceptibility mapping using index of entropy and conditional probability models in GIS: Safarood Basin, Iran, Catena, 47(3) (2012) 411-435.
[49] A.M. Youssef, H.R. Pourghasemi, Z.S. Pourtaghi, M.M. Al-Katheeri, Landslide susceptibility mapping using random forest, boosted regression tree, classification and regression tree, and general linear models and comparison of their performance at Wadi Tayyah Basin, Asir Region, Saudi Arabia, Landslides, 13(5) (2016) 839-856.
[50] A. Moteashari, J. Qomi, A. Eftekhari, B. Puzesh, M. Shahmari, Landslide hazard zoning of the Tehran-Chalus road and the highway under construction, Applied Geology, 8 (2012) 147-158.
[51] T. Kavzoglu, E. Sahin, I. Colkesen, Landslide susceptibility mapping using GIS-based multi-criteria decision analysis, support vector machines, and logistic regression, Landslides, 11(3) (2013) 425-439.
[52] D. Tien Bui, O. Lofman, I. Revhaug, O. Dick, Landslide susceptibility analysis in the Hoa Binh province of Vietnam using statistical index and logistic regression, Nat Hazards, 59(3) (2011) 1413-1444.
[53] H. Gomez, T. Kavzoglu, Assessment of shallow landslide susceptibility using artificial neural networks in Jabonosa River Basin, Venezuela, Engineering Geology, 78(1-2) (2005) 11-27.
[54] M. Zare, H.R. Pourghasemi, M. Vafakhah, B. Pradhan, 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, 6(8) (2013) 2873-2888.
[55] J. Paola, R. Schowengerdt, A review and analysis of backpropagation neural networks for classification of remotely-sensed multi-spectral imagery, International Journal of remote sensing, 16(16) (1995) 3033-3058.
[56] B. Pradhan, S. Lee, Landslide susceptibility assessment and factor effect analysis: backpropagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modelling, Environmental Modelling & Software, 25(6) (2010) 747-759.
[57] S. Lee, Application of likelihood ratio and logistic regression models to landslide susceptibility mapping using GIS, Environmental Management, 34(2) (2004) 223-232.
[58] X. Deng, L. Li, Y. Tan, Validation of spatial prediction models for landslide susceptibility mapping by considering structural similarity, ISPRS International Journal of Geo-Information, 6(4) (2017) 103-119.
[59] M.J. García-Rodríguez, J. Malpica, B. Benito, M. Díaz, Susceptibility assessment of earthquake-triggered landslides in El Salvador using logistic regression, Geomorphology, 95(3-4) (2008) 172-191.
[60] S. Lee, T. Sambath, Landslide susceptibility mapping in the Damrei Romel area, Cambodia using frequency ratio and logistic regression models, Environmental Geology, 50(6) (2006) 847-855.
[61] L. Ayalew, H. Yamagishi, The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda– Yahiko Mountains, Central Japan, Geomorphology, 65(1-2) (2005) 15-31.
[62] F. Cervi, M. Berti, L. Borgatti, F. Ronchetti, F. Manenti, A. Corsini, Comparing predictive capability of statistical and deterministic methods for landslide susceptibility mapping: a case study in the northern Apennines (Reggio Emilia Province, Italy), Landslides, 7(4) (2010) 433-444.
[63] M.H.A. Hasanat, D. Ramachandram, R. Mandava, Bayesian belief network learning algorithms for modeling contextual relationships in natural imagery: a comparative study, Artificial Intelligence Review, 34(4) (2010) 291-308.
[64] M.L. Süzen, V. Doyuran, A comparison of the GIS based landslide susceptibility assessment methods: multivariate versus bivariate, Environmental geology, 45(5) (2004) 665-679. | ||
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