Skip to main content
SpringerLink
Log in

Efficiency of multiple hybrid techniques for the earthquake physical susceptibility mapping: the case of Abbottabad District, Pakistan

  • Original Article
  • Published:
Environmental Earth Sciences Aims and scope Submit manuscript

Abstract

An earthquake is a natural event that causes serious intimidation to infrastructure and humans’ life in northern Pakistan. Environmental, physical, and social dimensions effectively add to seismic vulnerability. The current study deals with seismic susceptibility by integrating various decisive supporting methods to generate more accurate outcomes in the Abbottabad District, Pakistan. Hybrid models: fuzzy-logistic regression (fuzzy-LR) and multi-criteria evaluation–logistic regression (MCE–LR) trained at 70 by multiple criteria decision analysis–multi-criteria evaluation (MCDA–MCE) and fuzzy-multiple criteria analysis (fuzzy-MCDA) are used to build hybrid training datasets at 30. High accuracy in the MCDA–MCE model is observed based on the model output. Seismic susceptibility maps are generated by implementing the resulting datasets and hybrid learning models (fuzzy-LR and MCE–LR). Finally, the area under the curve (AUC) and frequency ratio (FR) validate the outcomes of seismic susceptibility maps. In comparison, both MCDA–MCE hybrid model (AUC = 0.812) and MCE–LR hybrid model (AUC = 0.875) indicated more precision than fuzzy-MCDA model (AUC = 0.806) and fuzzy-LR hybrid model (AUC = 0.842), respectively. The current study concludes that training datasets are the responsible factor for defining the seismic susceptibility mapping and modelling accuracy. Moreover, this study helps to specify the high susceptible locations in the urbanized environment and facilitate policymakers to implement measures in the study area for better planning in future to avoid the effects of the earthquake.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  • Abdollahi S, Pourghasemi HR, Ghanbarian GA, Safaeian R (2019) Prioritization of effective factors in the occurrence of land subsidence and its susceptibility mapping using an SVM model and their different kernel functions. Bull Eng Geol Env 78(6):4017–4034

    Article  Google Scholar 

  • Achour Y, Pourghasemi HR (2019) How do machine learning techniques help in increasing accuracy of landslide susceptibility maps? Geosci Front

  • Alizadeh M, Hashim M, Alizadeh E, Shahabi H, Karami MR, Beiranvand Pour A, Zabihi H (2018) Multi-criteria decision making (MCDM) model for seismic vulnerability assessment (SVA) of urban residential buildings. ISPRS Int J Geo Inf 7(11):444

    Article  Google Scholar 

  • Amini HK, Hosseini M, Jafari MK, Hosseinioun S (2009) Recognition of vulnerable urban fabrics in earthquake zones: a case study of the Tehran metropolitan area

  • Armaş I, Toma-Danila D, Ionescu R, Gavriş A (2017) Vulnerability to earthquake hazard: Bucharest case study, Romania. Int J Disaster Risk Sci 8(2):182–195

    Article  Google Scholar 

  • Asadi Y, Samany NN, Ezimand K (2019) Seismic vulnerability assessment of urban buildings and traffic networks using fuzzy ordered weighted average. J Mt Sci 16(3):677–688

    Article  Google Scholar 

  • Asim KM, Idris A, Iqbal T, Martínez-Álvarez F (2018) Earthquake prediction model using support vector regressor and hybrid neural networks. PLoS ONE 13(7):e0199004

    Article  Google Scholar 

  • Aslam B, Zafar A, Qureshi UA, Khalil U (2021) Seismic investigation of the northern part of Pakistan using the statistical and neural network algorithms. Environ Earth Sci 80(2):1–18

    Article  Google Scholar 

  • Avand M, Janizadeh S, Naghibi SA, Pourghasemi HR, Khosrobeigi Bozchaloei S, Blaschke T (2019) A comparative assessment of Random Forest and k-Nearest Neighbor classifiers for gully erosion susceptibility mapping. Water 11(10):2076

    Article  Google Scholar 

  • 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(1–2):15–31

    Article  Google Scholar 

  • Bagheri V, Uromeihy A, Aghda SMF (2018) Evaluation of ANFIS and LR models for seismic rockfalls’ susceptibility mapping: a case study of Firooz Abad-Kojour, Iran, Earthquake (2004). Environ Earth Sci 77(24):800

    Article  Google Scholar 

  • Bilham R (2009) The seismic future of cities. Bull Earthq Eng 7(4):839

    Article  Google Scholar 

  • Carreño ML, Cardona OD, Barbat AH (2012) New methodology for urban seismic risk assessment from a holistic perspective. Bull Earthq Eng 10(2):547–565

    Article  Google Scholar 

  • Chen W, Shahabi H, Shirzadi A, Li T, Guo C, Hong H, Ma M (2018) A novel ensemble approach of bivariate statistical-based logistic model tree classifier for landslide susceptibility assessment. Geocarto Int 33(12):1398–1420

    Article  Google Scholar 

  • Chen W, Chen Y, Tsangaratos P, Ilia I, Wang X (2020a) Combining Evolutionary Algorithms and Machine Learning Models in Landslide Susceptibility Assessments. Remote Sens 12(23):3854

    Article  Google Scholar 

  • Chen W, Zhao X, Tsangaratos P, Shahabi H, Ilia I, Xue W, Ahmad BB (2020b) Evaluating the usage of tree-based ensemble methods in groundwater spring potential mapping. J Hydrol 583:124602

    Article  Google Scholar 

  • Dou J, Yunus AP, Tien Bui D, Sahana M, Chen C-W, Zhu Z, Thai Pham B (2019) Evaluating GIS-based multiple statistical models and data mining for earthquake and rainfall-induced landslide susceptibility using the LiDAR DEM. Remote Sens 11(6):638

    Article  Google Scholar 

  • Duleba S, Moslem S (2019) Examining Pareto optimality in analytic hierarchy process on real Data: an application in public transport service development. Expert Syst Appl 116:21–30

    Article  Google Scholar 

  • Duzgun H, Yucemen M, Kalaycioglu H, Celik K, Kemec S, Ertugay K, Deniz A (2011) An integrated earthquake vulnerability assessment framework for urban areas. Nat Hazards 59(2):917–947

    Article  Google Scholar 

  • Frigerio I, De Amicis M (2016) Mapping social vulnerability to natural hazards in Italy: a suitable tool for risk mitigation strategies. Environ Sci Policy 63:187–196

    Article  Google Scholar 

  • Ghorbanzadeh O, Feizizadeh B, Blaschke T (2018a) An interval matrix method used to optimize the decision matrix in AHP technique for land subsidence susceptibility mapping. Environ Earth Sci 77(16):584

    Article  Google Scholar 

  • Ghorbanzadeh O, Feizizadeh B, Blaschke T (2018b) Multi-criteria risk evaluation by integrating an analytical network process approach into GIS-based sensitivity and uncertainty analyses. Geomat Nat Haz Risk 9(1):127–151

    Article  Google Scholar 

  • Ghorbanzadeh O, Rostamzadeh H, Blaschke T, Gholaminia K, Aryal J (2018c) A new GIS-based data mining technique using an adaptive neuro-fuzzy inference system (ANFIS) and k-fold cross-validation approach for land subsidence susceptibility mapping. Nat Hazards 94(2):497–517

    Article  Google Scholar 

  • Ghorbanzadeh O, Blaschke T, Gholamnia K, Aryal J (2019a) Forest fire susceptibility and risk mapping using social/infrastructural vulnerability and environmental variables. Fire 2(3):50

    Article  Google Scholar 

  • Ghorbanzadeh O, Blaschke T, Gholamnia K, Meena SR, Tiede D, Aryal J (2019b) Evaluation of different machine learning methods and deep-learning convolutional neural networks for landslide detection. Remote Sens 11(2):196

    Article  Google Scholar 

  • Ghorbanzadeh O, Moslem S, Blaschke T, Duleba S (2019c) Sustainable urban transport planning considering different stakeholder groups by an interval-AHP decision support model. Sustainability 11(1):9

    Article  Google Scholar 

  • Ghorbanzadeh O, Valizadeh Kamran K, Blaschke T, Aryal J, Naboureh A, Einali J, Bian J (2019d) Spatial prediction of wildfire susceptibility using field survey gps data and machine learning approaches. Fire 2(3):43

    Article  Google Scholar 

  • Ghorbanzadeh O, Blaschke T (2018) Wildfire susceptibility evaluation by integrating an analytical network process approach into GIS-based analyses. In: Paper presented at the Proceedings of ISERD International Conference

  • Guan X, Qian L, Li M, Chen H, Zhou L (2017) Earthquake relief emergency logistics capacity evaluation model integrating cloud generalized information aggregation operators. J Intell Fuzzy Syst 32(3):2281–2294

    Article  Google Scholar 

  • Gudiyangada Nachappa T, Tavakkoli Piralilou S, Ghorbanzadeh O, Shahabi H, Blaschke T (2019) Landslide susceptibility mapping for austria using geons and optimization with the dempster-shafer theory. Appl Sci 9(24):5393

    Article  Google Scholar 

  • Jaafari A, Zenner EK, Panahi M, Shahabi H (2019) Hybrid artificial intelligence models based on a neuro-fuzzy system and metaheuristic optimization algorithms for spatial prediction of wildfire probability. Agric for Meteorol 266:198–207

    Article  Google Scholar 

  • Jena R, Pradhan B, Beydoun G, Alamri AM, Sofyan H (2020) Earthquake hazard and risk assessment using machine learning approaches at Palu, Indonesia. Sci Total Environ 749:141582

    Article  Google Scholar 

  • Karimzadeh S, Miyajima M, Hassanzadeh R, Amiraslanzadeh R, Kamel B (2014) A GIS-based seismic hazard, building vulnerability and human loss assessment for the earthquake scenario in Tabriz. Soil Dyn Earthq Eng 66:263–280

    Article  Google Scholar 

  • Khan S, Khan MA (2016) Mapping sediment thickness of Islamabad city using empirical relationships: implications for seismic hazard assessment. J Earth Syst Sci 125(3):623–644

    Article  Google Scholar 

  • Khan SU, Qureshi MI, Rana IA, Maqsoom A (2019a) An empirical relationship between seismic risk perception and physical vulnerability: a case study of Malakand Pakistan. Int J Disaster Risk Reduct 41:101317

    Article  Google Scholar 

  • Khan SU, Qureshi MI, Rana IA, Maqsoom A (2019b) Seismic vulnerability assessment of building stock of Malakand (Pakistan) using FEMA P-154 method. SN Appl Sci 1(12):1625

    Article  Google Scholar 

  • Kumamoto T, Masataka T (2011) Multivariate statistical analysis for seismotectonic zonation by the use of earthquake, active fault and crustal structure.In: Paper presented at the Japan Geosciences Union Meeting

  • Kumlu KBY, Tüdeş Ş (2019) Determination of earthquake-risky areas in Yalova City Center (Marmara region, Turkey) using GIS-based multicriteria decision-making techniques (analytical hierarchy process and technique for order preference by similarity to ideal solution). Nat Hazards 96(3):999–1018

    Article  Google Scholar 

  • La Z (1965) Fuzzy sets. Inf Control 8:338–353

    Article  Google Scholar 

  • Lee S, Panahi M, Pourghasemi HR, Shahabi H, Alizadeh M, Shirzadi A, Rezaie F (2019) Sevucas: a novel gis-based machine learning software for seismic vulnerability assessment. Appl Sci 9(17):3495

    Article  Google Scholar 

  • Liu X, Han S (2008) Orness and parameterized RIM quantifier aggregation with OWA operators: a summary. Int J Approx Reason 48(1):77–97

    Article  Google Scholar 

  • Malczewski J (1999) GIS and multicriteria decision analysis. Wiley, New York

    Google Scholar 

  • Mamdani EH (1974) Application of fuzzy algorithms for control of simple dynamic plant. In: Paper presented at the Proceedings of the institution of electrical engineers

  • Maqsood ST, Schwarz J (2010) Building vulnerability and damage during the 2008 Baluchistan earthquake in Pakistan and past experiences. Seismol Res Lett 81(3):514–525

    Article  Google Scholar 

  • Martins VN, e Silva DS, Cabral P (2012) Social vulnerability assessment to seismic risk using multicriteria analysis: the case study of Vila Franca do Campo (São Miguel Island, Azores, Portugal). Nat Hazards 62(2):385–404

    Article  Google Scholar 

  • McClure J, Johnston D, Henrich L, Milfont TL, Becker J (2015) When a hazard occurs where it is not expected: risk judgments about different regions after the Christchurch earthquakes. Nat Hazards 75(1):635–652

    Article  Google Scholar 

  • Merciu C, Ianos I, Merciu G, Jones R, Pomeroy G (2018) Mapping accessibility for earthquake hazard response in the historic urban centre of Bucharest. Nat Hazard 18(7):2011–2026

    Article  Google Scholar 

  • Michael N (2005) Artificial intelligence a guide to intelligent systems: Addison Wesley

  • Moslem S, Ghorbanzadeh O, Blaschke T, Duleba S (2019) Analysing stakeholder consensus for a sustainable transport development decision by the fuzzy AHP and interval AHP. Sustainability 11(12):3271

    Article  Google Scholar 

  • Naseer A, Khan AN, Hussain Z, Ali Q (2010) Observed seismic behavior of buildings in northern Pakistan during the 2005 Kashmir earthquake. Earthq Spectra 26(2):425–449

    Article  Google Scholar 

  • Nik ME, Khademolhosseini N, Abbaspour-Fard MH, Mahdinia A, Alami-Saied K (2009) Optimum utilisation of low-capacity combine harvesters in high-yielding wheat farms using multi-criteria decision making. Biosys Eng 103(3):382–388

    Article  Google Scholar 

  • Pachauri A, Pant M (1992) Landslide hazard mapping based on geological attributes. Eng Geol 32(1–2):81–100

    Article  Google Scholar 

  • Pirnazar M, Karimi AZ, Feizizadeh B, Ghorbanzadeh O, Hamedani MH (2021) Criteria Decision Making Approach; Study Area: East-Azerbaijan Province (Kaleybar Chay Basin)

  • Potter SH, Becker JS, Johnston DM, Rossiter KP (2015) An overview of the impacts of the 2010–2011 Canterbury earthquakes. Int J Disaster Risk Reduct 14:6–14

    Article  Google Scholar 

  • Pourghasemi HR, Rahmati O (2018) Prediction of the landslide susceptibility: Which algorithm, which precision? CATENA 162:177–192

    Article  Google Scholar 

  • Pourghasemi HR, Beheshtirad M, Pradhan B (2016) A comparative assessment of prediction capabilities of modified analytical hierarchy process (M-AHP) and Mamdani fuzzy logic models using Netcad-GIS for forest fire susceptibility mapping. Geomat Nat Haz Risk 7(2):861–885

    Article  Google Scholar 

  • Qadri ST, Malik OA (2021) Establishing site response-based micro-zonation by applying machine learning techniques on ambient noise data: a case study from Northern Potwar Region, Pakistan. Environ Earth Sci 80(2):1–15

    Article  Google Scholar 

  • Qadri ST, Nawaz B, Sajjad S, Sheikh RA (2015a) Ambient noise H/V spectral ratio in site effects estimation in Fateh jang area, Pakistan. Earthq Sci 28(1):87–95

    Article  Google Scholar 

  • Qadri ST, Sajjad S, Sheikh R, Rehman K, Rafi Z, Nawaz B, Haider W (2015b) Ambient noise measurements in Rawalpindi-Islamabad, twin cities of Pakistan: a step towards site response analysis to mitigate impact of natural hazard. Nat Hazards 78(2):1111–1123

    Article  Google Scholar 

  • Qadri ST, Islam MA, Shalaby M, Khattak KR, Sajjad S (2017) Characterizing site response in the Attock Basin, Pakistan, using microtremor measurement analysis. Arab J Geosci 10(12):1–11

    Google Scholar 

  • Rafi M, Lodi S, Varum H, Alam N, Ahmed M, Silveira D (2012) Assessment of seismic performance of adobe structures in Pakistan and Portugal. In: Paper presented at the 15th world conference on earthquake engineering

  • Rahman N, Ansary MA, Islam I (2015) GIS based mapping of vulnerability to earthquake and fire hazard in Dhaka city, Bangladesh. Int J Disaster Risk Reduct 13:291–300

    Article  Google Scholar 

  • Rahmati O, Ghorbanzadeh O, Teimurian T, Mohammadi F, Tiefenbacher JP, Falah F, Bui DT (2019) Spatial modeling of snow avalanche using machine learning models and geo-environmental factors: comparison of effectiveness in two mountain regions. Remote Sens 11(24):2995

    Article  Google Scholar 

  • Razandi Y, Pourghasemi HR, Neisani NS, Rahmati O (2015) Application of analytical hierarchy process, frequency ratio, and certainty factor models for groundwater potential mapping using GIS. Earth Sci Inf 8(4):867–883

    Article  Google Scholar 

  • Rimal B, Baral H, Stork NE, Paudyal K, Rijal S (2015) Growing city and rapid land use transition: Assessing multiple hazards and risks in the Pokhara Valley, Nepal. Land 4(4):957–978

    Article  Google Scholar 

  • Saaty TL (1999) Fundamentals of the analytic network process. In: Paper presented at the Proceedings of the 5th international symposium on the analytic hierarchy process

  • Saaty TL (2008) Relative measurement and its generalization in decision making why pairwise comparisons are central in mathematics for the measurement of intangible factors the analytic hierarchy/network process. RACSAM-Revista de la Real Academia de Ciencias Exactas, Fisicas y Naturales. Serie A. Matematicas 102(2): 251–318

  • Shafapour Tehrany M, Shabani F, Neamah Jebur M, Hong H, Chen W, Xie X (2017) GIS-based spatial prediction of flood prone areas using standalone frequency ratio, logistic regression, weight of evidence and their ensemble techniques. Geomat Nat Haz Risk 8(2):1538–1561

    Article  Google Scholar 

  • Shah AA, Qadri T, Khwaja S (2018) Living with earthquake hazards in South and South East Asia

  • Shahabi H, Hashim M (2015) Landslide susceptibility mapping using GIS-based statistical models and Remote sensing data in tropical environment. Sci Rep 5(1):1–15

    Article  Google Scholar 

  • Shahabi H, Khezri S, Ahmad BB, Hashim M (2014) Landslide susceptibility mapping at central Zab basin, Iran: a comparison between analytical hierarchy process, frequency ratio and logistic regression models. CATENA 115:55–70

    Article  Google Scholar 

  • Sinha N, Priyanka N, Joshi P (2016) Using spatial multi-criteria analysis and ranking tool (SMART) in earthquake risk assessment: a case study of Delhi region, India. Geomat Nat Haz Risk 7(2):680–701

    Article  Google Scholar 

  • Sivakumar N, Karthik S, Thangaraj S, Saravanan S, Shidhardhan C (2013) Seismic vulnerability of open ground floor columns in multi storey buildings. Int J Sci Eng Res (IJSER), 1(3)

  • Solé J, Turiel A, Llebot JE (2007) Using empirical mode decomposition to correlate paleoclimatic time-series

  • Tang A, Wen A (2009) An intelligent simulation system for earthquake disaster assessment. Comput Geosci 35(5):871–879

    Article  Google Scholar 

  • Tavakkoli Piralilou S, Shahabi H, Jarihani B, Ghorbanzadeh O, Blaschke T, Gholamnia K, Aryal J (2019) Landslide detection using multi-scale image segmentation and different machine learning models in the higher himalayas. Remote Sens 11(21):2575

    Article  Google Scholar 

  • Termeh SVR, Kornejady A, Pourghasemi HR, Keesstra S (2018) Flood susceptibility mapping using novel ensembles of adaptive neuro fuzzy inference system and metaheuristic algorithms. Sci Total Environ 615:438–451

    Article  Google Scholar 

  • Theodoridou P, Varouchakis E, Karatzas G (2017) Spatial analysis of groundwater levels using Fuzzy Logic and geostatistical tools. J Hydrol 555:242–252

    Article  Google Scholar 

  • Tian Y, Xu C, Hong H, Zhou Q, Wang D (2019) Mapping earthquake-triggered landslide susceptibility by use of artificial neural network (ANN) models: an example of the 2013 Minxian (China) Mw 5.9 event. Geomatics Nat Hazards Risk 10(1):1–25

    Article  Google Scholar 

  • Wald D, Jaiswal K, Marano K, Bausch D (2011) Earthquake impact scale. Nat Hazard Rev 12(3):125–139

    Article  Google Scholar 

  • Xing H, Zhonglin Z, Shaoyu W (2015) The prediction model of earthquake casuailty based on robust wavelet v-SVM. Nat Hazards 77(2):717–732

    Article  Google Scholar 

  • Yager RR (1988) On ordered weighted averaging aggregation operators in multicriteria decisionmaking. IEEE Trans Syst Man Cybern 18(1):183–190

    Article  Google Scholar 

  • Yariyan P, Avand M, Soltani F, Ghorbanzadeh O, Blaschke T (2020) Earthquake vulnerability mapping using different hybrid models. Symmetry 12(3):405

    Article  Google Scholar 

  • Yariyan P, Karami M, Ali Abbaspour R (2019) Exploitation of mcda to learn the radial base neural network (RBFNN) aim physical and social vulnerability analysis versus the earthquake (case study: Sanandaj City, Iran). Int Arch Photogramm Remote Sens Spatial Inf Sci

  • 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(3):1331–1359

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to thank COMSATS University Islamabad Wah Campus, Riphah International University and Universiti Teknologi PETRONAS (UTP) for the support provided for this research.

Author information

Authors and Affiliations

  1. Department of Civil Engineering, COMSATS University Islamabad Wah Campus, Wah Cantt, Pakistan

    Ahsen Maqsoom, Muhammad Awais & Usman Hassan

  2. Department of Data Sciences, Riphah International University, Islamabad, Pakistan

    Bilal Aslam

  3. Department of Civil and Environmental Engineering, Universiti Teknologi PETRONAS, 32610, Bandar Seri Iskandar, Perak, Malaysia

    Wesam Salah Alaloul & Muhammad Ali Musarat

  4. Department of Civil Engineering, University of Engineering and Technology, Taxila, Pakistan

    Muhammad Irshad Qureshi

Authors
  1. Ahsen Maqsoom
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bilal Aslam
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Muhammad Awais
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Usman Hassan
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Wesam Salah Alaloul
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Muhammad Ali Musarat
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Muhammad Irshad Qureshi
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All the authors contributed equally to this study.

Corresponding author

Correspondence to Wesam Salah Alaloul.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Maqsoom, A., Aslam, B., Awais, M. et al. Efficiency of multiple hybrid techniques for the earthquake physical susceptibility mapping: the case of Abbottabad District, Pakistan. Environ Earth Sci 80, 678 (2021). https://doi.org/10.1007/s12665-021-09964-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12665-021-09964-1

Keywords

Search

Navigation