A Hybrid Analytic Network Process and Artificial Neural Network (ANP-ANN) model for urban Earthquake vulnerability assessment

Remote Sensing

Volumn 10, Issue 6, 2018, Pages

  Alizadeh, Mohsen ^a   Ngah, Ibrahim ^a   Hashim, Mazlan ^a   Pradhan, Biswajeet ^b,c   Pour, Amin Beiranvand ^d  

^a UNIVERSITI TEKNOLOGI MALAYSIA   (Malaysia)

^b UNIVERSITY OF TECHNOLOGY SYDNEY   (Australia)

^c Sejong University   (South Korea)

^d Korea Polar Research Institute (KOPRI)   (South Korea)

Author keywords

Analytic Network Process (ANP); Earthquake vulnerability map; GIS; Remote Sensing; Urban vulnerability

Indexed keywords

BEHAVIORAL RESEARCH; DECISION THEORY; DISASTER PREVENTION; DISASTERS; EARTHQUAKES; ENVIRONMENTAL MANAGEMENT; GEOGRAPHIC INFORMATION SYSTEMS; NEURAL NETWORKS; REMOTE SENSING; RISK ANALYSIS; RISK ASSESSMENT; RISK PERCEPTION; SUSTAINABLE DEVELOPMENT;

ANALYTIC NETWORK PROCESS; ARTIFICIAL NEURAL NETWORK MODELS; GOVERNMENT AUTHORITIES; URBAN VULNERABILITY; VULNERABILITY ASSESSMENTS; VULNERABILITY INDEX; VULNERABILITY INDICATORS; VULNERABILITY MAPS;

NETWORK SECURITY;

EID: 85048977423     PISSN: None     EISSN: 20724292     Source Type: Journal    
DOI: 10.3390/rs10060975     Document Type: Article

References (134)

1. Chang, L.F.;Huang, S.L.Assessing urban flooding vulnerabilitywith an emergy approach. Landsc.Urban Plan. 2015, 143, 11-24
2. Cutter, S.L. Vulnerability to environmental hazards. Prog. Hum. Geogr. 1996, 20, 529-539
3. De Andrade, M.M.N.; Claudio, F.S. Vulnerability assessment including tangible and intangible components in the index composition: An Amazon case study of flooding and flash flooding. Sci. Total Environ. 2018, 630, 903-912
4. De Lange, H.J.; Sala, S.; Vighi, M.; Faber, J.H. Ecological vulnerability in risk assessment-A review and perspectives. Sci. Total Environ. 2010, 408, 3871-3879
5. GLP. Science Plan and Implementation Strategy; IGBP Report No.53/IHDP Report No. 19; GLP: Stockholm, Sweden, 2005
6. Hagenlocher, M.; Renaud, F.G.; Haas, S.; Sebesvari, Z. Vulnerability and risk of deltaic social-ecological systems exposed to multiple hazards. Sci. Total Environ. 2018, 631-632, 71-80
7. Hinkel, J. Indicators of vulnerability and adaptive capacity: Towards a clarification of the science-policy interface. Glob. Environ. Chang. 2011, 21, 198-208
8. Urbanization and Global Environmental Change (UGEC). Science Plan; IHDP Report No. 15; UGEC: Bonn, Germany, 2005
9. Turner, B.L.; Kasperson, R.E.;Matson, P.A.;McCarthy, J.J.; Corell, R.W.; Christensen, L.; Eckley, N.; Kasperson, J.X.; Luers, A.;Martello, M.L.; et al. A framework for vulnerability analysis in sustainability science. Proc. Natl. Acad. Sci. USA 2003, 100, 8074-8079
10. Wisner, B.; Blaikie, P.; Cannon, T.; Davis, I. At Risk: Natural Hazards, People's Vulnerability, and Disasters; Routledge: London, UK, 2004
11. Rashed, T.; Weeks, J. Assessing vulnerability to earthquake hazards through spatial multicriteria analysis of urban areas. Int. J. Geogr. Inf. Sci. 2003, 17, 547-576
12. Morales-Esteban, A.; Martínez-álvarez, F.; Reyes, J. Earthquake prediction in seismogenic areas of the Iberian Peninsula based on computational intelligence. Tectonophysics 2013, 593, 121-134
13. Oliveira, C.S. Seismic vulnerability of historical constructions: A contribution. Bull. Earthq. Eng. 2003, 1, 37-82
14. D'Ayala, D.F.; Carriero, A.; Sabbadini, F.; Fanciullacci, D.; Ozelik, P.; Akdogan, M.; Kaya, Y. Seismic Vulnerability and Risk Assessment of Cultural Heritage Buildings in Istanbul, Turkey. In Proceedings of the 14th World Conference on Earthquake Engineering, Beijing, China, 12-17 October 2008; Volume 3
15. Bakirtas, T.; Akpolat, A.G. The relationship between energy consumption, urbanization, and economic growth in new emerging-market countries. Energy 2018, 147, 110-121
16. Duzgun, H.S.B.; Yucemen, M.S.; Kalaycioglu, H.S.; Celik, K.; Kemec, S.; Ertugay, K.; Deniz, A. An integrated earthquake vulnerability assessment framework for urban areas. Nat. Hazards 2011, 59, 917-947
17. He, C.; Chen, T.; Mao, X.; Zhou, Y. Economic transition, urbanization and population redistribution in China. Habitat Int. 2016, 51, 39-47
18. Hendricksa, M.D.; Meyerb, M.A.; Gharaibehc, N.G.; Zandtd, S.V.; Mastersond, J.; Cooper, J.T.; Horneye, J.A.; Berked, P. The development of a participatory assessment technique for infrastructure: Neighborhood-level monitoring towards sustainable infrastructure systems. Sustain. Cities Soc. 2018, 38, 265-274
19. Marrone, S.; Nardone, R.; Tedesco, A.; D'Amore, P.; Vittorini, V.; Setola, R.; De Cillis, F.;Mazzocca, N. Vulnerability modeling and analysis for critical infrastructure protection applications. Int. J. Crit. Infrastruct. Protect. 2013, 6, 217-227
20. Tucker, B.E.; Trumbull, J.G.;Wyss, S.J. Some remarks concerningworldwide urban earthquake hazard and earthquake hazardmitigation. In Issues in Urban Earthquake Risk; Springer: Dordrecht, TheNetherlands, 1994; pp. 1-10
21. Alarifi, A.S.; Alarifi, N.S.; Al-Humidan, S. Earthquakes magnitude predication using artificial neural network in northern Red Sea area. J. King Saud Univ. Sci. 2012, 24, 301-313
22. Adeli, H.; Panakkat, A. A probabilistic neural network for earthquakemagnitude prediction. Neural Netw. 2009, 22, 1018-1024
23. Erzin, Y.; Cetin, T. The use of neural networks for the prediction of the critical factor of safety of an artificial slope subjected to earthquake forces. Sci. Iran. 2012, 19, 188-194
24. Mirrashid, M. Earthquake magnitude prediction by adaptive neuro-fuzzy inference system (ANFIS) based on fuzzy C-means algorithm. Nat. Hazards 2014, 74, 1577-1593
25. Moustra, M.; Avraamides, M.; Christodoulou, C. Artificial neural networks for earthquake prediction using time series magnitude data or Seismic Electric Signals. Expert Syst. Appl. 2011, 38, 15032-15039
26. Prajapati, S.K.; Dadhich, H.K.; Chopra, S. Isoseismal map of the 2015 Nepal earthquake and its relationships with ground-motion parameters, distance and magnitude. J. Asian Earth Sci. 2017, 133, 24-37
27. Tavakoli, B. Sensitivity of seismic hazard evaluations to uncertainties determined fromseismic source characterization. J. Seismol. 2002, 6, 525-545
28. Yousefi, M.; Taghikhany, T. Incorporation of directivity effect in probabilistic seismic hazard analysis and disaggregation of Tabriz city. Nat. Hazards 2014, 73, 277-301
29. Zamani, A.; Sorbi, M.R.; Safavi, A.A. Application of neural network and ANFIS model for earthquake occurrence in Iran. Earth Sci. Inf. 2013, 6, 71-85
30. Menoni, S. Chains of damages and failures in a metropolitan environment: Some observations on the Kobe earthquake in 1995. J. Hazard. Mater. 2001, 86, 101-119
31. Giacinto, G.; Paolucci, R.; Roli, F. Application of neural networks and statistical pattern recognition algorithms to earthquake risk evaluation. Pattern Recognit. Lett. 1997, 18, 1353-1362
32. Irwansyah, E.; Hartati, S. Zonation of Earthquake Building Damage Hazard Area Using Kohonen Neural Network (NN) and Kriging Algorithm. Adv. Nat. Appl. Sci. 2014, 8, 120-124
33. Karimzadeh, S.; Cakir, Z.; Osmanoğlu, B.; Schmalzle, G.; Miyajima, M.; Amiraslanzadeh, R.; Djamour, Y. Interseismic strain accumulation across the North Tabriz Fault (NW Iran) deduced from InSAR time series. J. Geodyn. 2013, 66, 53-58
34. Karimzadeh, S.; Miyajima, M.; Hassanzadeh, R.; Amiraslanzadeh, R.; Kamel, B. A GIS-based seismic hazard, building vulnerability and human loss assessment for the earthquake scenario in Tabriz. Soil Dyn. Earthq. Eng. 2014, 66, 263-280
35. Kappos, A.J.; Panagopoulos, G.K.; Sextos, A.G.; Papanikolaou, V.K.; Stylianidis, K.C. Development of comprehensive earthquake loss scenarios for a Greek and a Turkish city-structural aspects. Earthq. Struct. 2010, 1, 197-214
36. McGowan, S.M.; Jaiswal, K.S.; Wald, D.J. Using structural damage statistics to derive macroseismic intensity within the Kathmandu valley for the 2015M7.8 Gorkha, Nepal earthquake. Tectonophysics 2017, 714-715, 158-172
37. Tavakoli, B.; Ghafory-Ashtiany, M. Seismic hazard assessment of Iran. Ann. Geophys. 1999, 42, 123-135
38. Vicente, R.; Parodi, S.; Lagomarsino, S.; Varum, H.; Silva, J.M. Seismic vulnerability and risk assessment: Case study of the historic city centre of Coimbra, Portugal. Bull. Earthq. Eng. 2011, 9, 1067-1096
39. Aradag, S.; Genc, Y.; Turk, C. Comparative gasketed plate heat exchanger performance prediction with computations, experiments, correlations and artificial neural network estimations. Eng. Appl. Comput. FluidMech. 2017, 11, 467-482
40. Taormina, R.; Chau, K.W.; Sivakumar, B. Neural network river forecasting through baseflow separation and binary-coded swarm optimization. J. Hydrol. 2015, 529, 1788-1797
41. Hajihassani, M.; Armaghani, D.J.; Marto, A.; Mohamad, E.T. Ground vibration prediction in quarry blasting through an artificial neural network optimized by imperialist competitive algorithm. Bull. Eng. Geol. Environ. 2015, 74, 873-886
42. Chau, K.W. Reliability and performance-based design by artificial neural network. Adv. Eng. Softw. 2007, 38, 145-149
43. Gordan, B.; Armaghani, D.J.; Hajihassani, M.; Monjezi, M. Prediction of seismic slope stability through combination of particle swarm optimization and neural network. Eng. Comput. 2016, 32, 85-97
44. Hinton, G.E. How neural networks learn from experience. Sci. Am. 1992, 267, 145-151
45. Jensen, B. Expert systems-neural networks. In Instrument Engineers' Handbook, 3rd ed.; Chilton: Radnor, PA, USA, 1994
46. Bishop, C.M. Neural Networks for Pattern Recognition; Oxford University Press: Oxford, UK, 1995
47. Liang, Z.; Shan, S.; Liu, X.; Wen, Y. Fuzzy prediction of AWJ turbulence characteristics by using typical multi-phase flow models. Eng. Appl. Comput. Fluid Mech. 2017, 11, 225-257
48. Islam, M.S.; Mohandes, M.; Rehman, S. Vertical extrapolation of wind speed using artificial neural network hybrid system. Neural Comput. Appl. 2017, 28, 2351-2361
49. Sharma, M.; Purohit, G.N.; Mukherjee, S. Information Retrieves from Brain MRI Images for Tumor Detection Using Hybrid Technique K-means and Artificial Neural Network (KMANN). In Networking Communication and Data Knowledge Engineering; Springer: Singapore, 2018; pp. 145-157
50. Haykin, S. Multilayer perceptrons. Neural Netw. 1999, 2, 156-255
51. Lee, S.; Ryu, J.H.; Lee, M.J.; Won, J.S. Use of an artificial neural network for analysis of the susceptibility to landslides at Boun, Korea. Environ. Geol. 2003, 44, 820-833
52. Lee, S.; Ryu, J.H.; Won, J.S.; Park, H.J. Determination and application of the weights for landslide susceptibility mapping using an artificial neural network. Eng. Geol. 2004, 71, 289-302
53. Field, E. Urban Property Rights and Labor Supply in Peru. Q. J. Econ. 2007, 122, 1561-1602
54. Mili, R.R.; Hosseini, K.A.; Izadkhah, Y.O. Developing a holistic model for earthquake risk assessment and disaster management interventions in urban fabrics. Int. J. Disaster Risk Reduct. 2018, 27, 355-365
55. Bahadori, H.; Hasheminezhad, A.; Karimi, A. Development of an integrated model for seismic vulnerability assessment of residential buildings: Application toMahabad City, Iran. J. Build. Eng. 2017, 12, 118-131
56. Zhang, Y.; van den Berg, A.E.; Dijk, T.V.; Weitkamp, G. Quality over Quantity: Contribution of Urban Green Space to Neighborhood Satisfaction. Int. J. Environ. Res. Public Health 2017, 14, 535
57. Karimzadeh, S.; Kadas, K.;Askan, A.; Erberik, M.A.; Yakut, A.Astudy on fragility analyses ofmasonry buildings in Erzincan (Turkey) utilizing simulated and real groundmotion records. Procedia Eng. 2017, 199, 188-193
58. Armas, I.; Toma-Danila, D.; Ionescu, R.; Gavirs, A. Vulnerability to Earthquake Hazard: Bucharest Case Study, Romania. Int. J. Disaster Risk Sci. 2017, 8, 182-192
59. Zebardast, E. Constructing a social vulnerability index to earthquake hazards using a hybrid factor analysis and analytic network process (F'ANP) model. Nat. Hazards 2013, 65, 1331-1359
60. Pradhan, B.; Lee, S. Landslide risk analysis using artificial neural network model focussing on different training sites. Int. J. Phys. Sci. 2009, 4, 1-15
61. Panakkat, A.; Adeli, H. Recurrent neural network for approximate earthquake time and location prediction using multiple seismicity indicators. Comput. Aided Civ. Infrastruct. Eng. 2009, 24, 280-292
62. Ghayamghamian, M.R.; Rajool, A. Long-period Ground Motion Simulation for NTF Fault Near-source energy released. In Proceedings of the Fifteenth World Conference on Earthquake Engineering, Lisbon, Portugal, 23-25 March 2012
63. Berberian, M. Contribution to the Seismotectonics of Iran (Part II-III): In Commemoration of the 50th Anniversary of the Pahlavi Dynasty; No. 39; Ministry of Industry and Mines, Geological Survey of Iran, Tectonic and Seismotectonic Section: Tehran, Iran, 1976
64. Jackson, J. Partitioning of strike-slip and convergent motion between Eurasia and Arabia in eastern Turkey and the Caucasus. J. Geophys. Res. Solid Earth 1992, 97, 12471-12479
65. Wilson, A. Earthquakes in Persia. Bull. Sch. Orient. Stud. Lond. Inst 1930, 6, 103-131
66. Wu, Q.; Ye, S.;Wu, X.; Chen, P. Risk assessment of earth fractures by constructing an intrinsic vulnerability map, a specific vulnerabilitymap, and a hazardmap, using Yuci City, Shanxi, China as an example. Environ. Geol. 2004, 46, 104-112
67. Kurttila, M.; Pesonen, M.; Kangas, J.; Kajanus, M. Utilizing the analytic hierarchy process (AHP) in SWOT analysis-A hybrid method and its application to a forest-certification case. For. Policy Econ. 2000, 1, 41-52
68. Kometa, S.T.; Olomolaiye, P.; Harris, F.C. Validation of the model for evaluating client-generated risk by project consultants. Constr. Manag. Econ. 1996, 14, 131-145
69. Shash, A.A.; Abdul-Hadi, H.N. The effect of contractor size on mark-up size decision in Saudi Arabia. Constr.Manag. Econ. 1993, 11, 421-429
70. Adeoye, D.O. Challenges of Urban Housing Quality: Insights and Experiences of Akure, Nigeria. Procedia Soc. Behav. Sci. 2016, 216, 260-268
71. ESRI. Arc GIS Network Analyst Routing, Closest Facility, and Service Area Analysis; ESRI: Redlands, CA, USA, 2005
72. Sandi, H. Vulnerability and risk analysis for individual structures and systems. In Proceedings of the 8th European Conference on Earthquake Engineering, Lisbon, Portugal, 7-12 September 1986; pp. 11-60
73. Bahrainy, H. Natural disaster management in Iran during the 1990s-need for a new structure. J. Urban Plan. Dev. 2003, 129, 140-160
74. Vacareanu, R.; Cornea, T.; Lungu, D. Evaluarea comportãrii structurale si a ulnerabilitãtii seismice folosind metodologiile HAZUS si ATC-40 modificat. In A doua Conferintã Nationalã de Inginerie Seismicã; Technical University of Civil Engineering of Bucharest Press: Bucharest, Romania, 2001; Volume 2, pp. 2-16
75. Cutter, S.L.; Mitchell, J.T.; Scott, M.S. Revealing the vulnerability of people and places: A case study of Georgetown County, South Carolina. Ann. Assoc. Am. Geogr. 2000, 90, 713-737
76. Cutter, S.L.; Boruff, B.J.; Shirley, W.L. Social vulnerability to environmental hazards. Soc. Sci. Q. 2003, 84, 242-261
77. Cutter, S.L.; Finch, C. Temporal and spatial changes in social vulnerability to natural hazards. Proc. Natl. Acad. Sci. USA 2008, 105, 2301-2306
78. Ebert, A.; Kerle, N.; Stein, A. Urban social vulnerability assessment with physical proxies and spatial metrics derived from air-and spaceborne imagery and GIS data. Nat. Hazards 2009, 48, 275-294
79. Alinia, H.S.; Delavar, M. Tehran's seismic vulnerability classification using granular computing approach. Appl. Geomat. 2011, 3, 229-240
80. Yildiz, S.; Karaman, H. Post-earthquake ignition vulnerability assessment of Küçükçekmece District. Nat. Hazards Earth Syst. Sci. 2013, 13, 3357-3368
81. Panahi, M.; Rezaie, F.; Meshkani, S.A. Seismic vulnerability assessment of Atmospheric Open Access Atmospheric Measurement school buildings in Tehran city based on Techniques Open Access Discussions AHP and GIS. Nat. Hazards Earth Syst. Sci. 2013, 14, 4511-4538
82. Zaharia, B.; Radulian, M.; Popa, M.; Grecu, B.; Bala, A.; Tataru, D. Estimation of the local response using Nakamura method for Bucharest area. Roman. Rep. Phys. 2008, 60, 131-144
83. Tate, E. Social vulnerability indices: A comparative assessment using uncertainty and sensitivity analysis. Nat. Hazards 2012, 63, 325-347
84. Bakhtiari, A. Country Report: The Islamic Republic of Iran on Disaster Risk Management; Iranian National Disaster Management Organization (NDMO) and Visiting Researcher at ADRC: Kobe, Japan, 2014
85. Rezaie, F.; Panahi, M. GIS modeling of seismic vulnerability of residential fabrics considering geotechnical, structural, social and physical distance indicators in Tehran using multi-criteria decision-making techniques. Nat. Hazards Earth Syst. Sci. 2015, 15, 461-474
86. Holand, I.S.; Lujala, P.; Rød, J.K. Social vulnerability assessment for Norway: A quantitative approach. Norsk Geografisk Tidsskrift-Norwegian J. Geogr. 2011, 65, 1-17
87. Armas, I.; Gavris, A. Social vulnerability assessment using spatialmulti-criteria analysis (SEVImodel) and the Social Vulnerability Index (SoVImodel)-A case study for Bucharest, Romania. Nat. Hazards Earth Syst. Sci. 2013, 13, 1481-1499
88. Binita, K.; Shepherd, J.M.; Gaither, C.J. Climate change vulnerability assessment in Georgia. Appl. Geogr. 2015, 62, 62-74
89. Brooks, N.; Adger, W.N.; Kelly, P.M. The determinants of vulnerability and adaptive capacity at the national level and the implications for adaptation. Glob. Environ. Chang. 2005, 15, 151-163
90. Martins, V.N.; e Silva, D.S.; Cabral, P. Social vulnerability assessment to seismic risk using multicriteria analysis: The case study of Vila Franca do Campo (Sao Miguel Island, Azores, Portugal). Nat. Hazards 2012, 62, 385-404
91. Peng, Y. Regional earthquake vulnerability assessment using a combination of MCDM methods. Ann. Oper. Res. 2015, 234, 95-110
92. Asadzadeh, A.; Kötter, T.; Zebardast, E. An augmented approach for measurement of disaster resilience using connective factor analysis and analytic network process (F'ANP)model. Int. J. Disaster Risk Reduct. 2015, 14, 504-518
93. Beck, E.; André-Poyaud, I.; Davoine, P.-A.; Chardonnel, S.; Lutoff, C. Risk perception and social vulnerability to earthquakes in Grenoble (French Alps). J. Risk Res. 2012, 15, 1245-1260
94. Eakin, H.; Bojorquez-Tapia, L.A. Insights into the composition of household vulnerability from multicriteria decision analysis. Glob. Environ. Chang. 2008, 18, 112-127
95. Lungu, D.; Aldea, A.; Arion, C.; Cornea, T.; Vacareanu, R. European distinctive features inventory database and typology. In Proceedings of the International Conference Earthquake Loss Estimation and Risk Reduction 2004, Bucuresti, Romania, 24-26 October 2004; Volume 2, pp. 251-272
96. Tanaka, K. The impact of disaster education on public preparation andmitigation for earthquakes: A cross-country comparison between Fukui, Japan and the San Francisco Bay Area, California, USA. Appl. Geogr. 2005, 25, 201-225
97. Sarris, A.; Loupasakis, C.; Soupios, P.; Trigkas, V.; Vallianatos, F. Earthquake vulnerability and seismic risk assessment of urban areas in high seismic regions: Application to Chania City, Crete Island, Greece. Nat. Hazards 2010, 54, 395-412
98. Debnath, R. An assessment of spatio-temporal pattern of urban earthquake vulnerability using GIS: A study on Dhaka City. Ann. GIS 2013, 19, 63-78
99. Armaş, I. Earthquake risk perception in Bucharest, Romania. Risk Anal. 2006, 26, 1223-1234
100. Armaş, I. Social vulnerability and seismic risk perception. Case study: The historic center of the Bucharest Municipality/Romania. Nat. Hazards 2008, 47, 397-410
101. Armaş, I.; Avram, E. Perception of flood risk in Danube Delta, Romania. Nat. Hazards 2009, 50, 269-287
102. Armaş, I. Multi-criteria vulnerability analysis to earthquake hazard of Bucharest, Romania. Nat. Hazards 2012, 63, 1129-1156
103. Armaş, I.; Ionescu, R.; Garivs, A.; Toma-Danila, D. Identifying seismic vulnerability hotspots in Bucharest. Appl. Geogr. 2016, 77, 49-63
104. Ma, J.; Qin, S. Automatic depicting algorithmof earthquake collapsed buildings with airborne high resolution image. In Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Munich, Germany, 22-27 March 2012; pp. 939-942
105. Hizbaron, D.R.; Baiquni, M.; Sartohadi, J.; Rijanta, R. Urban Vulnerability in Bantul District, Indonesia-Towards Safer and Sustainable Development. Sustainability 2012, 4, 2022-2037
106. Toro, J.; Duarte, O.; Requena, I.; Zamorano, M. Determining vulnerability importance in environmental impact assessment: The case of Colombia. Environ. Impact Assess. Rev. 2012, 32, 107-117
107. Davidson, R.A.; Shah, H.C. An Urban Earthquake Disaster Risk Index; Blume, J.H., Ed.; Standford University, Earthquake Engineering Center: Standford, CA, USA, 1997
108. Davidson, D.J.; Freudenburg, W.R.Gender and environmental risk concern a reviewand analysis of available research. Environ. Behav. 1996, 28, 302-339
109. Krishnamurthy, P.K.; Krishnamurthy, L. Social Vulnerability Assessment through GIS Techniques: A Case Study of Flood Risk Mapping in Mexico. In Geospatial Techniques for Managing Environmental Resources; Springer: Dordrecht, The Netherlands, 2011; pp. 276-291
110. Shepard, C.C.; Agostini, V.N.; Gilmer, B.; Allen, T.; Stone, J.; Brooks, W.; Beck, M.W. Assessing future risk: Quantifying the effects of sea level rise on storm surge risk for the southern shores of Long Island, New York. Nat. Hazards 2012, 60, 727-745
111. Granger, K.; Jones, T.G.; Leiba, M.; Scott, G. Community Risk in Cairns: A Multi-Hazard Risk Assessment; Australian Geological Survey Organisation: Canberra, Australian, 1999; pp. 678-689
112. King, D.; MacGregor, C. Using social indicators to measure community vulnerability to natural hazards. Aust. J. Emerg. Manag. 2000, 15, 52-67
113. Saaty, T.L. Analytical Network Process; RWS Publications: Pittsburgh, PA, USA, 1996
114. Marasco, A. Third-party logistics: A literature review. Int. J. Prod. Econ. 2008, 113, 127-147
115. Chung, S.H.; Lee, A.H.I.; Pearn, W.L. Analytic network process (ANP) approach for product mix planning in semiconductor fabricator. Int. J. Prod. Econ. 2005, 96, 15-36
116. Bojórquez, E.; Bojórquez, J.; Ruiz, S.E.; Reyes-Salazar, A. Prediction of inelastic response spectra using artificial neural networks. Math. Probl. Eng. 2012, 34, 236-248
117. Cheng, J.; Li, Q.S. Reliability analysis of structures using artificial neural network based genetic algorithms. Comput. Methods Appl. Mech. Eng. 2008, 197, 3742-3750
118. Günaydin, K.; Günaydin, A. Peak ground acceleration prediction by artificial neural networks for northwestern Turkey. Math. Probl. Eng. 2008, 2008, 919420
119. Ergu, D.; Kou, G.; Shi, Y.; Shi, Y. Analytic network process in risk assessment and decision analysis. Comput. Oper. Res. 2014, 42, 58-74
120. Meade, L.M.; Sarkis, J. Analyzing organizational project alternatives for agile manufacturing processes: An analytical network approach. Int. J. Prod. Res. 1999, 37, 241-261
121. Lee, H.; Kim, M.S.; Park, Y. An analytic network process approach to operationalization of five forces model. Appl. Math. Model. 2012, 36, 1783-1795
122. Sahoo, S.; Dhar, A.; Kar, A. Environmental vulnerability assessment using Grey Analytic Hierarchy Process based model. Environ. Impact Assess. Rev. 2016, 56, 145-154
123. Antanasijević, D.Z.; Pocajt, V.V.; Povrenović, D.S.; Ristić, M.; Perić-Grujić, A.A. PM10 emission forecasting using artificial neural networks and genetic algorithm input variable optimization. Sci. Total Environ. 2013, 443, 511-519
124. Nedic, V.; Despotovic, D.; Cvetanovic, S.; Despotovic, M.; Babic, S. Comparison of classical statistical methods and artificial neural network in traffic noise prediction. Environ. Impact Assess. Rev. 2014, 49, 24-30
125. Hagan, M.T.; Demuth, H.B.; Beale, M. Neural Network Design; PWS Pub.: Boston, MA, USA, 1996
126. Paola, J.D.; Schowengerdt, R.A. A review and analysis of backpropagation neural networks for classification of remotely-sensed multi-spectral imagery. Int. J. Remote Sens. 1995, 16, 3033-3058
127. Atkinson, P.M.; Tatnall, A.R.L. Introduction neural networks in remote sensing. Int. J. Remote Sens. 1997, 18, 699-709
128. Gong, P. Integrated analysis of spatial data for multiple sources: Using evidential reasoning and artificial neural network techniques for geological mapping. Photogramm. Eng. Remote Sens. 1996, 62, 513-523
129. Abraham, A. Artificial neural networks. In Handbook ofMeasuring System Design;Wiley: Chichester, UK, 2005
130. Pradhan, B.; Lee, S. Regional landslide susceptibility analysis using back-propagation neural network model at Cameron Highland, Malaysia. Landslides 2010, 7, 13-30
131. Cardona, O.D.; van Aalst, M.K.; Birkmann, J.; Fordham, M.; McGregor, G.; Mechler, R. Determinants of Risk: Exposure and Vulnerability; Cambridge University Press: Cambridge, UK, 2012; pp. 54-76
132. Pelling, M.;Wisner, B. Disaster Risk Reduction: Cases from Urban Africa; Routledge: London, UK, 2012
133. Villegas-González, P.A.; Ramos-Cañón, A.M.; González-Méndez, M.; González-Salazar, R.E.; De Plaza-Solórzano, J.S. Territorial vulnerability assessment frame in Colombia: Disaster riskmanagement. Int. J. Disaster Risk Reduct. 2017, 21, 384-395
134. Steinführer, A. Social Vulnerability and the 2002 Flood; Country Report Germany (Mulde River); Helmholz Unweltforschungszentrum (UFZ): Berlin, Germany, 2007