Volume 11 Issue 2
Aug.  2020
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Ratiranjan Jena, Biswajeet Pradhan, Ghassan Beydoun, Nizamuddin, Ardiansyah, Hizir Sofyan, Muzailin Affan. Integrated model for earthquake risk assessment using neural network andanalytic hierarchy process: Aceh province, Indonesia[J]. Geoscience Frontiers, 2020, (2): 613-634. doi: 10.1016/j.gsf.2019.07.006
Citation: Ratiranjan Jena, Biswajeet Pradhan, Ghassan Beydoun, Nizamuddin, Ardiansyah, Hizir Sofyan, Muzailin Affan. Integrated model for earthquake risk assessment using neural network and analytic hierarchy process: Aceh province, Indonesia[J]. Geoscience Frontiers, 2020, (2): 613-634. doi: 10.1016/j.gsf.2019.07.006

Integrated model for earthquake risk assessment using neural network and analytic hierarchy process: Aceh province, Indonesia

doi: 10.1016/j.gsf.2019.07.006

This research is funded by Centre for Advanced Modelling and Geospatial Information Systems, University of Technology Sydney: 323930, 321740.2232335 and 321740.2232357.

  • Received Date: 2019-04-03
  • Rev Recd Date: 2019-05-21
  • Publish Date: 2020-08-26
  • Catastrophic natural hazards, such as earthquake, pose serious threats to properties and human lives in urban areas. Therefore, earthquake risk assessment (ERA) is indispensable in disaster management. ERA is an integration of the extent of probability and vulnerability of assets. This study develops an integrated model by using the artificial neural network–analytic hierarchy process (ANN–AHP) model for constructing the ERA map. The aim of the study is to quantify urban population risk that may be caused by impending earthquakes. The model is applied to the city of Banda Aceh in Indonesia, a seismically active zone of Aceh province frequently affected by devastating earthquakes. ANN is used for probability mapping, whereas AHP is used to assess urban vulnerability after the hazard map is created with the aid of earthquake intensity variation thematic layering. The risk map is subsequently created by combining the probability, hazard, and vulnerability maps. Then, the risk levels of various zones are obtained. The validation process reveals that the proposed model can map the earthquake probability based on historical events with an accuracy of 84%. Furthermore, results show that the central and southeastern regions of the city have moderate to very high risk classifications, whereas the other parts of the city fall under low to very low earthquake risk classifications. The findings of this research are useful for government agencies and decision makers, particularly in estimating risk dimensions in urban areas and for the future studies to project the preparedness strategies for Banda Aceh.
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    Abraham, A., 2005. Handbook of Measuring System Design. John Wiley and Sons Ltd, Chichester, UK. https://doi.org/10.1002/0471497398.
    Adger, W.N., Brooks, N., Bentham, G., Agnew, M., Eriksen, S., 2004. New Indicators of Vulnerability and Adaptive Capacity; Tyndall Centre for Climate Change Research. Final Project Report. University of East Anglia, Norwich, UK, p. 122pp.
    Aghataher, R., Delavar, M.R., Nami, M.H., Samnay, N., 2008. A fuzzy-AHP decision support system for evaluation of cities vulnerability against earthquakes. World Appl. Sci. J. 3, 66–72.
    Aghdam, I.N., Varzandeh, M.H.M., Pradhan, B., 2016. Landslide susceptibility mapping using an ensemble statistical index (Wi) and adaptive neuro-fuzzy inference system (ANFIS) model at Alborz Mountains (Iran). Environ. Earth Sci. 75 (7), 553. https:// doi.org/10.1007/s12665-015-5233-6.
    Alarifi, A.S., Alarifi, N.S., Al-Humidan, S., 2012. Earthquakes magnitude predication using artificial neural network in northern Red Sea area. J. King Saud Univ. Sci. 24, 301–313.
    Alizadeh, M., Ngah, I., Hashim, M., Pradhan, B., Pour, A., 2018a. A hybrid analytic network process and artificial neural network (ANP-ANN) model for urban earthquake vulnerability assessment. Remote Sens. 10, 975.
    Alizadeh, M., Hashim, M., Alizadeh, E., Shahabi, H., Karami, M., Beiranvand Pour, A., Pradhan, B., Zabihi, H., 2018b. Multi-criteria decision making (MCDM) model for seismic vulnerability assessment (SVA) of urban residential buildings. ISPRS Int. J. Geo-Inf. 7, 444.
    Armas¸, I., 2012. Multi-criteria vulnerability analysis to earthquake hazard of Bucharest, Romania. Nat. Hazards 63, 1129–1156.
    Atkinson, P.M., Tatnall, A.R.L., 1997. Introduction neural networks in remote sensing. Int. J. Remote Sens. 18, 699–709.
    Bahadori, H., Hasheminezhad, A., Karimi, A., 2017. Development of an integrated model for seismic vulnerability assessment of residential buildings: application to Mahabad City. Iran. J. Build Eng. 12, 118–131.
    Bathrellos, G.D., Skilodimou, H.D., Chousianitis, K., Youssef, A.M., Pradhan, B., 2017. Suitability estimation for urban development using multi-hazard assessment map. Sci. Total Environ. 575, 119–134.
    Beccari, B.A., 2016. Comparative analysis of disaster risk, vulnerability and resilience composite indicators. PLOS Currents 8. https://doi.org/10.1371/currents.dis.453df 025e34b682e9737f95070f9b970.
    Bellier, O., S ebrier, S., Pramumijoyo, T., Beaudouin, H., Harjono, I., Bahar, O., 1997. Fomi. Paleoseimicity and seismic hazard along the ornat Sumatran Fault (Indonesia). J. Geodyn. 24, 169–183.
    Bilham, R., Ambrasey, N., 2005. Apparent Himalayan slip deficit from the summation of seismic moments for Himalayan earthquakes, 1500–2000. Curr. Sci. 88, 1658–1663.
    Birkmann, J., 2007. Risk and vulnerability indicators at different scales: applicability, usefulness and policy implications. Environ. Hazards 7, 20–31.
    Birkmann, J., Welle, T., 2015. Assessing the risk of loss and damage: exposure, vulnerability and risk to climate-related hazards for different country classifications. Int. J. Glob. Warming 8, 191–212.
    Brebbia, C., Beskos, D., Kausel, E., 1996. The Kobe Earthquake: Geodynamical Aspects. Computational Mechanics Publications, Southampton, p. 160.
    Chaulagain, H., Rodrigues, H., Silva, V., Spacone, E., Varum, H., 2015. Seismic risk assessment and hazard mapping in Nepal. Nat. Hazards 78, 583–602.
    Culshaw, M.G., Duncan, S.V., Sutarto, N.R., 1979. Engineering geological mapping of the Banda Aceh alluvial basin, northern Sumatra, Indonesia. Bulletin of the International Association of Engineering Geology-Bulletin de l’Association Internationale de G eologie Bull. Int. Assoc. Eng. Geol. Bull. Assoc. Int. G eol. 19, 40–47.
    Cutter, S.L., 1996. Vulnerability to environmental hazards. Prog. Hum. Geogr. 20, 529–539.
    Davidson, R., Shah, H.C., 1997. A multidisciplinary urban earthquake disaster risk index. Earthq. Spectra 13, 211–223.
    Davidson, D., Freudenburg, W., 1996. Gender and environmental risk concerns. Environ. Behav. 28, 302–339.
    Davidson, R.A., 1997. An urban earthquake disaster risk index. The John A. Blume Earthquake Engineering Center Report No. 121. Blume Center, Stanford, CA, USA, p. 269.
    D’Ayala, D.F., Carriero, A., Sabbadini, F., Fanciullacci, D., Ozelik, P., Akdogan, M., Kaya, Y., 2008. Seismic Vulnerability and Risk Assessment of Cultural Heritage Buildings in Istanbul, Turkey. 14th, vol. 3. WCEE, Beijing.
    Dimri, S., Lakhera, R.C., Sati, S., 2007. Fuzzy-based method for landslide hazard assessment in active seismic zone of Himalaya. Landslides 4, 101.
    Fanos, A.M., Pradhan, B., 2019. A Spatial Ensemble Model for Rockfall Source Identification From High Resolution LiDAR Data and GIS. IEEE Access 7, 74570–74585. https://doi.org/10.1109/ACCESS.2019.2919977.
    Gitamandalaksana, 2009. Final report: identification of seismic source’s zone and tsunami hazard probability as considerations in development policy of Banda Aceh city. Nanggroe Aceh Darussalam Province (Package-1), Banda Aceh 33pp. Gong, P., 1996. Integrated analysis of spatial data for multiple sources: using evidential reasoning and artificial neural network techniques for geological mapping. Photogramm. Eng. Remote Sens. 62, 513–523.
    Granger, K., Jones, T., Leiba, M., Scott, G., 1999. Community Risk in Cairns: A Provisional Multi- Hazard Risk Assessment; AGSO Cities Project Report No. 1. Australian Geological Survey Organisation, Canberra, Australia, p. 231.
    Gulkan, P., Sozen, M.A., 1999. Procedure for determining seismic vulnerability of building structures. Struct. J. 96, 336–342.
    Hagan, M.T., Demuth, H.B., Beale, M., 1996. Neural Network Design. PWS Publication, Boston, MA, USA, p. 1012.
    Hosseini, A., GHasemi, Z., Ahadnejad, M., Alimoradi, T., 2014. Evaluation of qualitative and quantitative indicators of social housing in the Tabriz metropolitan. Int. J. Bus. Behav. Sci. 4, 19–30.
    Indonesia State Ministry for National Planning Development Agency/BAPPENAS, 2005. Preliminary Damage and Loss Assessment-The December 26, Natural Disaster. Government Printer, Jakarta, p. 128.
    Irwansyah, E., 2010. Building damage assessment using remote sensing, aerial photograph and GIS data: case study in Banda Aceh after Sumatera earthquake 204. In: Proceeding of the 11th Seminar on Intelligent Technology and Its Application- SITIA, 11, p. 57.
    Johar, F., Majid, M.R., Jaffar, A.R., Yahya, A.S., 2013. Seismic microzonation for Banda Aceh city planning. Plan. Malays. J 11, 1–26.
    Kafle, S.K., 2006. Rapid disaster risk assessment of coastal communities: a case study of mutiara village, Banda Aceh, Indonesia. In: Proceedings of the International Conference on Environment and Disaster Management Held in Jakarta, Indonesia on December, pp. 5–8.
    Karimzadeh, S., Kadas, K., Askan, A., Erberik, M.A., Yakut, A., 2017. A study on fragility analyses of masonry buildings in Erzincan (Turkey) utilizing simulated and real ground motion records. Procedia. Eng. 199, 188–193.
    Karunanithi, N., Grenney, W.J., Whitley, D., Bovee, K., 1994. Neural networks for river flow prediction. J. Comput. Civ. Eng. 8 (2), 201–220.
    Khan, S., 2012. Vulnerability assessments and their planning implications: a case study of the Hutt Valley, New Zealand. Nat. Hazards 64, 1587–1607.
    Khan, S.A., Pilakoutas, K., Hajirasouliha, I., Garcia, R., Guadagnini, M., 2018. Seismic risk assessment for developing countries: Pakistan as a case study. Earthq. Eng. Eng. Vib. 17, 787–804.
    Lin, P.S., Lee, C.T., 2008. Ground-motion attenuation relationships for subduction zone earthquakes in northeastern Taiwan. Bull. Seismol. Soc. Am. 98, 220–240.
    Martins, V.N., Silva, D.S., Cabral, P., 2012. Social vulnerability assessment to seismic risk using multicriteria analysis: the case study of Vila Franca do Campo (S~ao Miguel Island, Azores, Portugal). Nat. Hazards 62, 385–404.
    McIlraith, A.L., Card, H.C., 1997. Birdsong recognition using backpropagation and multivariate statistics. IEEE Trans. Signal Process. 45, 2740–2748.
    Mili, R.R., Hosseini, K.A., Izadkhah, Y.O., 2018. Developing a holistic model for earthquake risk assessment and disaster management interventions in urban fabrics. Int. J. Disaster Risk. Reduct. 27, 355–365.
    Mohammady, M., Pourghasemi, H.R., Pradhan, B., 2012. Landslide susceptibility mapping at Golestan Province, Iran: A comparison between frequency ratio, Dempster–Shafer, and weights-of-evidence models. J. Asian Earth Sci. 61 (15), 221–236. https://doi.org/10.1016/j.jseaes.2012.10.005.
    Morales-Esteban, A., Martínez- Alvarez, F., Reyes, J., 2013. Earthquake prediction in seismogenic areas of the Iberian Peninsula based on computational intelligence. Tectonophysics 593. https://doi.org/10.1016/j.tecto.2013.02.036.
    Nedic, V., Despotovic, D., Cvetanovic, S., Despotovic, M., Babic, S., 2014. Comparison of classical statistical methods and artificial neural network in traffic noise prediction. Environ. Impact Assess. Rev. 49, 24–30.
    Oliveira, C.S., 2003. Seismic vulnerability of historical constructions: a contribution. Bull. Earthq. Eng. 1, 37–82.
    Panahi, M., Rezaie, F., Meshkani, A.S., 2014. Seismic vulnerability assessment of school buildings in Tehran city based on AHP and GIS. Nat. Hazards Earth Syst. Sci. 14, 969–979.
    Panakkat, A., Adeli, H., 2007. Neural network models for earthquake magnitude prediction using multiple seismicity indicators. Int. J. Neural Syst. 17, 13–33.
    Panakkat, A., Adeli, H., 2009. Recurrent neural network for approximate earthquake time and location prediction using multiple seismicity indicators. Comput. Aided Civ. Infrastruct. Eng. 4, 280–292.
    Paola, J.D., Schowengerdt, R.A., 1995. A review and analysis of backpropagation neural networks for classification of remotely-sensed multi-spectral imagery. Int. J. Remote Sens. 16, 3033–3058.
    Pay, C., 2001. A new methodology for the seismic vulnerability assessment of existing buildings in Turkey. M.Sc. thesis. Middle East Technical University, Ankara. Pradhan, B., Hasan, H.A., Jebur, M.N., Tehrany, M.S., 2014. Land subsidence susceptibility mapping at Kinta Valley (Malaysia) using the evidential belief function model in GIS. Nat. Hazards 73 (2), 1019–1042. https://doi.org/10.1007/s11069- 014- 1128-1.
    Pradhan, B., Jena, R., 2016. Spatial relationship between earthquakes, hot-springs and faults in Odisha, India. In: IOP Conference Series: Earth and Environmental Science, vol. 37. IOP Publishing, 012070.
    Pradhan, B., Lee, S., 2009. Landslide risk analysis using artificial neural network model focussing on different training sites. Int. J. Phys. Sci. 4, 1–15.
    Pradhan, B., Lee, S., 2010. Regional landslide susceptibility analysis using backpropagation neural network model at Cameron Highland, Malaysia. Landslides 7 (1), 13–30. https://doi.org/10.1007/s10346-009-0183-2.
    Pradhan, B., Moneir, A.A.A., Jena, R., 2018. Sand dune risk assessment in Sabha region, Libya using Landsat 8, MODIS, and Google Earth Engine images. Geomatics, Nat. Hazards Risk 9, 1280–1305.
    Ram, T.D., Wang, G., 2013. Probabilistic seismic hazard analysis in Nepal. Earthq. Eng. Eng. Vib. 12, 577–586.
    Rashed, T., Weeks, J., 2003. Assessing vulnerability to earthquake hazards through spatial multicriteria analysis of urban areas. Int. J. Geogr. Inf. Sci. 17, 547–576. Rygel, L., O’Sullivan, D., Yarnal, B.A., 2006. Method for constructing a social vulnerability index: an application to hurricane storm surges in a developed country. Mitig. Adapt. Strategies Glob. Change 11, 741–764.
    Saaty, T.L., 2013. Analytic hierarchy process. In: Encyclopedia of Operations Research and Management Science. Springer, Boston, MA, pp. 52–64.
    Setiawan, B., 2017. Site specific ground response analysis for quantifying site amplification at a regolith site. Ind. J. Geoscience 4, 159–167.
    Setiawan, B., Jaksa, M., Griffith, M., Love, D., 2018. Seismic site classification based on constrained modelling of measured HVSR curve in regolith sites. Soil Dyn. Earthq. Eng. 110, 244–261.
    Shimizu, S., Sugisaki, K., Ohmori, H., 2008. Recursive sample-entropy method and its application for complexity observation of earth current. In: International Conference on Control, Automation and Systems (ICCAS). Seoul, South Korea, pp. 1250–1253. Siemon, B., Ploethner, D., Pielawa, J., 2005. Hydrogeological Reconnaissance Survei in the Province Nanggroe Aceh Darussalam Northern Sumatra, Indonesia Survei Area: Banda Aceh/Aceh Besar 2005, Report C 1, BGR. Federal Institute for Geosciences and Natural Resources.
    Soe, M., Ryutaro, T., Ishiyama, D., Takashima, I., Charusiri, K.W.I.P., 2009. Remote sensing and GIS based approach for earthquake probability map: a case study of the northern Sagaing fault area, Myanmar. J. Geol. Soc. Thail. 29–46.
    Sørensen, M., Atakan, K., 2008. Continued earthquake hazard in Northern Sumatra: Potential effects of a future earthquake. EOS. Trans. Am. Geophys. Union 89, 133–134.
    Tehrany, M.S., Pradhan, B., Jebur, M.N., 2014. Flood susceptibility mapping using a novel ensemble weights-of-evidence and support vector machine models in GIS. J. Hydrol. 512, 332–343. https://doi.org/10.1016/j.jhydrol.2014.03.008.
    Tien Bui, D., Ho, T.C., Pradhan, B., Pham, B.-T., Nhu, V.-H., 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, 1101. https://doi.org/10.1007/s12665-016-5919-4.
    Tierney, K., 2006. Social inequality: humans and disasters. In: Daniels, R.J., Keitl, D.F., Kunreuther, H. (Eds.), On Risk and Disaster: Lessons from Hurricane Katrina. University of Pennsylvania Press, Philadelphia, PA, USA. https://doi.org/10.978 3/9780812205473.109.
    Turmov, G.P., Korochentsev, V.I., Gorodetskaya, E.V., Mironenko, A.M., Kislitsin, D.V., Starodubtsev, O.A., 2000. Forecast of underwater earthquakes with a great degree of probability. In: Proceedings of the 2000 International Symposium on Underwater Technology. Tokyo, Japan, pp. 110–115.
    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., Polsky, C., Pulsipher, A., Schiller, A., 2003. A framework for vulnerability analysis in sustainability science. Proc. Natl. Acad. Sci. U.S.A. 100, 8074–8079.
    Wang, K., Chen, Q.F., Sun, S., Wang, A., 2006. Predicting the 1975 Haicheng earthquake. Bull. Seismol. Soc. Am. 96, 757–795.
    Wisner, B., Blaikie, P., Cannon, T., Davis, I., 2003. At Risk: Natural Hazards, People’s Vulnerability and Disasters, second ed. Routledge, Abingdon, UK, pp. 11–13.
    Xu, C., Dai, F.C., Xu, X.W., 2010. Wenchuan earthquake-induced landslides: an overview. Geol. Rev. 56, 860–874 (in Chinese with English abstract).
    Yakut, A., Aydogan, V., Ozcebe, G., Yucemen, M.S., 2003. Preliminary seismic vulnerability assessment of existing reinforced concrete buildings in Turkey. Seismic Assessment and Rehabilitation of Existing Buildings. Springer, Dordrecht, pp. 43–58. Youngs, R.R., Silva, W.J., Humprey, J.R., 1997. Seismol. Res. Lett. 68, 1.
    Youssef, A.M., Al-Kathery, M., Pradhan, B., 2015. Landslide susceptibility mapping at Al- Hasher area, Jizan (Saudi Arabia) using GIS-based frequency ratio and index of entropy models. Geosci. J. 19 (1), 113–134. https://doi.org/10.1007/s12303-014- 0032-8.
    Yücemen, M.S., €Ozcebe, G., Pay, A.C., 2004. Prediction of potential damage due to severe earthquakes. Struct. Saf. 26, 349–366.
    Yuzal, H., Kim, K., Pant, P., Yamashita, E., 2017. Tsunami evacuation buildings evacuation planning in Banda Aceh, Indonesia. J. Emerg. Manag. 15, 49–61.
    Zare, M., Pourghasemi, H.R., Vafakhah, M., Pradhan, B., 2013. Landslide susceptibility mapping at VazWatershed (Iran) using an artificial neural networkmodel: a comparison between multilayer perceptron (MLP) and radial basic function (RBF) algorithms. Arab. J. Geosci. 6 (8), 2873–2888. https://doi.org/10.1007/s12517-012- 0610-x.
    Zebardast, E., 2013. Constructing a social vulnerability index to earthquake hazards using a hybrid factor analysis and analytic network process (F’ANP) model. Nat. Hazards 65, 1331–1359.
    Zhang, J.S., Jia, Z.K., 2010. The study on assessment index of urban social vulnerability to the earthquake disaster. Technological Guide 36, 12–14.
    Zhang, Y., van den Berg, A.E., Dijk, T.V., Weitkamp, G., 2017. Quality over quantity: contribution of urban green space to neighborhood satisfaction. Int. J. Environ. Res. Public Health 14, 535.
    Zhao, Y., Takano, K., 1999. An artificial neural network approach for broadband seismic phase picking. Bull. Seismol. Soc. Am. 89, 670–680.
    Zhihuan, Z., Junjing, Y., 1990. Prediction of earthquake damages and reliability analysis using fuzzy sets. In: First International Symposium on Uncertainty Modeling and Analysis. College Park, MD, USA, pp. 173–176.
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