Skip to main content

Advertisement

SpringerLink
Log in

A multi-criteria decision-making approach to vulnerability assessment of rural flooding in Khyber Pakhtunkhwa Province, Pakistan

  • Research Article
  • Published:
Environmental Science and Pollution Research Aims and scope Submit manuscript

Abstract

Assessment of rural regions’ vulnerability to flooding is gaining prominence on a global scale. However, researchers are greatly undermined in their efforts to make a comprehensive assessment owing to the multidimensional and non-linear link between different indicators and flood risk. Thus, a multi-criteria decision-making (MCDM) approach is proposed to assess the multifaceted vulnerability of rural flooding in Khyber Pakhtunkhwa Province, Pakistan. This research presents a hybrid model for flood vulnerability assessment by combining TOPSIS and the entropy weight method. Households’ vulnerability to flooding in rural areas is assessed through four components (social, economic, physical, and institutional) and twenty indicators. All indicator weights are derived using the entropy weight method. The TOPSIS method is then used to rank the selected research areas based on their flood vulnerability levels. The ranking results reveal that flood vulnerability is highest in the Nowshehra District, followed by the Charsadda, Peshawar, and D.I. Khan Districts. The weighting results show that physical vulnerability is the most important component, while location of household’s house from the river source (< 1 km) is the key indicator for assessing flood vulnerability. A sensitivity analysis is provided to study the impact of indicator’s weights on the comprehensive ranking results. The sensitivity results revealed that out of twenty indicators, fourteen indicators had the lowest sensitivity, three indicators were reported with low sensitivity while the other three were considered highly sensitive for flood vulnerability assessment. Our research has the potential to offer policymakers specific guidelines for lowering flood risk in flood-prone areas.

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

Similar content being viewed by others

Data availability

The datasets used or analyzed during the current study are available from the corresponding author upon reasonable request.

References

  • Affeltranger B (2001) Public participation in the design of local strategies for flood mitigation and control. Unesco

  • Ainuddin S, Routray JK (2012) Community resilience framework for an earthquake prone area in Baluchistan. Int J Disaster Risk Reduction 2:25–36

    Google Scholar 

  • Ali S, Liu Y, Ishaq M et al (2017a) Climate change and its impact on the yield of major food crops: evidence from Pakistan. Foods 6(6):39

    Google Scholar 

  • Ali S, Ying L, Shah T et al (2017b) Understanding the adaptation process of rural smallholder farmers toward climate change: evidence from northern Malakand area of Pakistan. J Appl Environ Biol Sci 7 (7):2–15

    Google Scholar 

  • Ali S, Ying L, Nazir A et al (2021) Rural farmers perception and coping strategies towards climate change and their determinants: evidence from Khyber Pakhtunkhwa Province, Pakistan. J Clean Prod 291:125,250

    Google Scholar 

  • Armaş I (2012) Multi-criteria vulnerability analysis to earthquake hazard of Bucharest, Romania. Nat Hazards 63(2):1129–1156

    Google Scholar 

  • Assari A, Mahesh T, Assari E (2012) Role of public participation in sustainability of historical city: usage of TOPSIS method. Indian J Sci Technol 5(3):2289–2294

    Google Scholar 

  • Balica S, Douben N, Wright NG (2009) Flood vulnerability indices at varying spatial scales. Water Sci Technol 60(10):2571–2580

    CAS  Google Scholar 

  • Balica S, Popescu I, Beevers L et al (2013) Parametric and physically based modelling techniques for flood risk and vulnerability assessment: a comparison. Environ Model Softw 41:84–92

    Google Scholar 

  • Balica SF, Wright NG, Van der Meulen F (2012) A flood vulnerability index for coastal cities and its use in assessing climate change impacts. Nat Hazards 64(1):73–105

    Google Scholar 

  • Birkmann J, Welle T (2016) The WorldRiskIndex 2016: reveals the necessity for regional cooperation in vulnerability reduction. J Extreme Events 3(02):1650,005

    Google Scholar 

  • Birkmann J, Cardona OD, Carreño ML et al (2013) Framing vulnerability, risk and societal responses: the move framework. Nat Hazards 67(2):193–211

    Google Scholar 

  • Bogner A, Littig B, Menz W (2009) Interviewing experts. Springer, New York

    Google Scholar 

  • Bollin C, Hidajat R, Birkmann J (2006) Community-based risk index: pilot implementation in Indonesia. Meas Vulnerability Nat Hazards Towards Disaster Resilient Soc 271:89

    Google Scholar 

  • de Brito MM, Evers M (2016) Multi-criteria decision-making for flood risk management: a survey of the current state of the art. Nat Hazards Earth Syst Sci 16(4):1019–1033

    Google Scholar 

  • Brouwer R, Akter S, Brander L et al (2007) Socioeconomic vulnerability and adaptation to environmental risk: a case study of climate change and flooding in Bangladesh. Risk Anal Int J 27(2):313–326

    Google Scholar 

  • Chen J, Zhang Y, Chen Z et al (2015) Improving assessment of groundwater sustainability with analytic hierarchy process and information entropy method: a case study of the Hohhot Plain, China. Environ Earth Sci 73(5):2353–2363

    Google Scholar 

  • Chen SM, Cheng SH, Lan TC (2016) Multicriteria decision making based on the TOPSIS method and similarity measures between intuitionistic fuzzy values. Inform Sci 367:279–295

    Google Scholar 

  • Chen W, Cutter SL, Emrich CT et al (2013) Measuring social vulnerability to natural hazards in the Yangtze River Delta region, China. Int J Disaster Risk Sci 4(4):169–181

    Google Scholar 

  • Chung ES, Abdulai PJ, Park H et al (2016) Multi-criteria assessment of spatial robust water resource vulnerability using the TOPSIS method coupled with objective and subjective weights in the Han River basin. Sustainability 9(1):29

    Google Scholar 

  • Cutter SL (2003) The vulnerability of science and the science of vulnerability. Ann Assoc Am Geogr 93(1):1–12

    Google Scholar 

  • Deb M, Debbarma B, Majumder A et al (2016) Performance–emission optimization of a diesel-hydrogen dual fuel operation: A NSGA II coupled TOPSIS MADM approach. Energy 117:281–290

    CAS  Google Scholar 

  • Fekete A (2009) Validation of a social vulnerability index in context to river-floods in Germany. Nat Hazards Earth Syst Sci 9(2):393–403

    Google Scholar 

  • FFC (2012) Federal flood commission report on floods, 2010, (n.d.). https://ffc.gov.pk/wp-content/uploads/2020/06/Annual-Flood-Report-2012.pdf. Accessed 11 July 2022

  • Fuchs S, Birkmann J, Glade T (2012) Vulnerability assessment in natural hazard and risk analysis: current approaches and future challenges. Nat Hazards 64(3):1969–1975

    Google Scholar 

  • Gain AK, Mojtahed V, Biscaro C et al (2015) An integrated approach of flood risk assessment in the eastern part of Dhaka City. Nat Hazards 79(3):1499–1530

    Google Scholar 

  • Govindan K, Khodaverdi R, Jafarian A (2013) A fuzzy multi criteria approach for measuring sustainability performance of a supplier based on triple bottom line approach. J Clean Prod 47:345–354

    Google Scholar 

  • Huang D, Zhang R, Huo Z et al (2012) An assessment of multidimensional flood vulnerability at the provincial scale in China based on the DEA method. Nat Hazards 64(2):1575–1586

    Google Scholar 

  • Hwang CL, Yoon K (1981) Multiple attributes decision making: methods and applications. Springer-Verlag, New York

    Google Scholar 

  • Islam MS, Swapan MSH, Haque SM (2013) Disaster risk index: how far should it take account of local attributes? Int J Disaster Risk Reduction 3:76–87

    Google Scholar 

  • Jamshed A, Rana IA, Birkmann J et al (2017) Changes in vulnerability and response capacities of rural communities after extreme events: case of major floods of 2010 and 2014 in Pakistan. J Extreme Events 4(03):1750,013

    Google Scholar 

  • Jamshed A, Birkmann J, Rana IA et al (2020) The relevance of city size to the vulnerability of surrounding rural areas: an empirical study of flooding in Pakistan. Int J Disaster Risk Reduction 48:101,601

    Google Scholar 

  • Karmakar S, Simonovic SP, Peck A et al (2010) An information system for risk-vulnerability assessment to flood. J Geogr Inf Syst 2(03):129

    Google Scholar 

  • Khan FA, Salman A (2012) A simple human vulnerability index to climate change hazards for Pakistan. Int J Disaster Risk Sci 3(3):163–176

    CAS  Google Scholar 

  • Koks EE, Jongman B, Husby TG et al (2015) Combining hazard, exposure and social vulnerability to provide lessons for flood risk management. Environ Sci Policy 47:42–52

    Google Scholar 

  • Maiti S, Jha SK, Garai S et al (2017) An assessment of social vulnerability to climate change among the districts of Arunachal Pradesh, India. Ecol Indic 77:105–113

    Google Scholar 

  • National Disaster Management Authority (NDMA) (2012) National Disaster Management Authority. Annual Report 2012. Available at: http://cms.ndma.gov.pk/storage/app/public/publications/October2020/2rupXpoXMsag4K42cZ4i.pdf

  • Nazeer M, Bork HR (2021) A local scale flood vulnerability assessment in the flood-prone area of Khyber Pakhtunkhwa, Pakistan. Nat Hazards 105(1):755–781

    Google Scholar 

  • OLOGUNORISA* TE (2004) An assessment of flood vulnerability zones in the Niger Delta, Nigeria. Int J Environ Stud 61(1):31–38

  • Ouma YO, Tateishi R (2014) Urban flood vulnerability and risk mapping using integrated multi-parametric AHP and GIS: methodological overview and case study assessment. Water 6(6):1515–1545

    Google Scholar 

  • Pandey R, Jha S (2012) Climate vulnerability index-measure of climate change vulnerability to communities: a case of rural Lower Himalaya, India. Mitig Adapt Strat Glob Chang 17(5):487–506

    Google Scholar 

  • Pandey R, Jha SK, Alatalo JM et al (2017) Sustainable livelihood framework-based indicators for assessing climate change vulnerability and adaptation for Himalayan communities. Ecol Indic 79:338–346

    Google Scholar 

  • PDMA-KP (2014) Monsoon contingency plan Khyber Pakhtunkhwa. http://www.pdma.gov.pk/sites/default/files/Monsoon_Contingency_Plan_KP_2014.pdf

  • Peng C, Du H, Liao TW (2017) A research on the cutting database system based on machining features and TOPSIS. Robot Comput Integr Manuf 43:96–104

    Google Scholar 

  • Pohekar SD, Ramachandran M (2004) Application of multi-criteria decision making to sustainable energy planning—a review. Renew Sustain Energy Rev 8(4):365–381

    Google Scholar 

  • Qasim S, Qasim M, Shrestha RP et al (2017) An assessment of flood vulnerability in Khyber Pukhtunkhwa Province of Pakistan. AIMS Environ Sci 4(2):206–216

    Google Scholar 

  • Rafiq L, Blaschke T (2012) Disaster risk and vulnerability in Pakistan at a district level. Geomatics, Natural Hazards Risk 3(4):324–341

    Google Scholar 

  • Rana IA, Routray JK (2016) Actual vis-à-vis perceived risk of flood prone urban communities in Pakistan. Int J Disaster Risk Reduction 19:366–378

    Google Scholar 

  • Rana IA, Routray JK (2018a) Integrated methodology for flood risk assessment and application in urban communities of Pakistan. Nat Hazards 91(1):239–266

    Google Scholar 

  • Rana IA, Routray JK (2018b) Multidimensional model for vulnerability assessment of urban flooding: an empirical study in Pakistan. Int J Disaster Risk Sci 9(3):359–375

    Google Scholar 

  • Rana IA, Bhatti SS, Jamshed A et al (2021) An approach to understanding the intrinsic complexity of resilience against floods: evidences from three urban communities of Pakistan. Int J Disaster Risk Reduction 63:102,442

    Google Scholar 

  • Rosić M, Pešić D, Kukić D et al (2017) Method for selection of optimal road safety composite index with examples from DEA and TOPSIS method. Accid Anal Prev 98:277–286

    Google Scholar 

  • Saqib SE, Ahmad MM, Panezai S et al (2016) An empirical assessment of farmers’ risk attitudes in flood-prone areas of Pakistan. Int J Disaster Risk Reduction 18:107–114. https://doi.org/10.1016/j.ijdrr.2016.06.007

    Article  Google Scholar 

  • Shah AA, Ye J, Abid M et al (2017) Determinants of flood risk mitigation strategies at household level: a case of Khyber Pakhtunkhwa (KP) Province, Pakistan. Nat Hazards 88(1):415–430

    Google Scholar 

  • Shah AA, Ye J, Abid M et al (2018a) Flood hazards: household vulnerability and resilience in disaster-prone districts of Khyber Pakhtunkhwa Province, Pakistan. Nat Hazards 93(1):147–165

    Google Scholar 

  • Shah AA, Ye J, Pan L et al (2018b) School’s flood emergency preparedness in Khyber Pakhtunkhwa Province, Pakistan. Int J Disaster Risk Sci 9(2):181–194

    Google Scholar 

  • Shah AA, Shaw R, Ye J et al (2019) Current capacities, preparedness and needs of local institutions in dealing with disaster risk reduction in Khyber Pakhtunkhwa, Pakistan. Int J Disaster Risk Reduction 34:165–172

    Google Scholar 

  • Shah AA, Gong Z, Ali M et al (2020a) Measuring education sector resilience in the face of flood disasters in pakistan: an index-based approach. Environ Sci Pollut Res 27(35):44,106–44,122

    Google Scholar 

  • Shah AA, Ye J, Shaw R et al (2020b) Factors affecting flood-induced household vulnerability and health risks in Pakistan: the case of Khyber Pakhtunkhwa (KP) Province. Int J Disaster Risk Reduction 42:101,341

    Google Scholar 

  • Shah AA, Ajiang C, Gong Z et al (2022a) Reconnoitering school children vulnerability and its determinants: evidence from flood disaster-hit rural communities of Pakistan. Int J Disaster Risk Reduction 70:102,735

    Google Scholar 

  • Shah AA, Ajiang C, Khan NA et al (2022b) Flood risk perception and its attributes among rural households under developing country conditions: the case of Pakistan. Water 14(6):992

    Google Scholar 

  • Shannon CE (2001) A mathematical theory of communication. ACM SIGMOBILE Mob Comput Commun Rev 5(1):3–55

    Google Scholar 

  • Smith K (2013) Environmental hazards: assessing risk and reducing disaster. Routledge, Evanston

    Google Scholar 

  • Sorg L, Medina N, Feldmeyer D et al (2018) Capturing the multifaceted phenomena of socioeconomic vulnerability. Nat Hazards 92(1):257–282

    Google Scholar 

  • Sun R, Gong Z, Gao G et al (2020) Comparative analysis of multi-criteria decision-making methods for flood disaster risk in the Yangtze River Delta. Int J Disaster Risk Reduction 51:101,768

    Google Scholar 

  • Thouret JC, Ettinger S, Guitton M et al (2014) Assessing physical vulnerability in large cities exposed to flash floods and debris flows: the case of Arequipa (Peru). Nat Hazards 73(3):1771–1815

    Google Scholar 

  • Walker BB, Taylor-Noonan C, Tabbernor A et al (2014) A multi-criteria evaluation model of earthquake vulnerability in Victoria, British Columbia. Nat hazards 74(2):1209–1222

    Google Scholar 

  • Yan HB, Huynh VN, Nakamori Y (2011) A probabilistic model for linguistic multi-expert decision making involving semantic overlapping. Expert Syst Appl 38(7):8901–8912

    Google Scholar 

  • Yang W, Xu K, Lian J et al (2018) Integrated flood vulnerability assessment approach based on TOPSIS and Shannon entropy methods. Ecol Indic 89:269–280

    Google Scholar 

  • Yaqub M, Beytullah E, Doğan E (2015) Flood causes, consequences and protection measures in Pakistan. Disaster Sci Eng 1(1):8–16

    Google Scholar 

  • Yoon DK (2012) Assessment of social vulnerability to natural disasters: a comparative study. Nat Hazards 63(2):823–843

    Google Scholar 

  • Younus MAF (2017) An assessment of vulnerability and adaptation to cyclones through impact assessment guidelines: a bottom-up case study from Bangladesh coast. Nat Hazards 89(3):1437–1459

    Google Scholar 

  • Zagonari F, Rossi C (2013) A heterogeneous multi-criteria multi-expert decision-support system for scoring combinations of flood mitigation and recovery options. Environ Model Softw 49:152–165

    Google Scholar 

  • Zhou Y, Liu Y, Wu W et al (2015) Integrated risk assessment of multi-hazards in China. Nat Hazards 78(1):257–280

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Management Science and Engineering, Nanjing University of Information Science & Technology, Nanjing, 210044, China

    Abid Khan & Zaiwu Gong

  2. School of Public Administration, Hohai University, Nanjing, China

    Ashfaq Ahmad Shah

  3. Department of Mathematics, Abdul Wali Khan University, Mardan, Pakistan

    Mirajul Haq

Authors
  1. Abid Khan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zaiwu Gong
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ashfaq Ahmad Shah
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mirajul Haq
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Abid Khan: conceptualization, methodology, software, validation, formal analysis, writing—original draft, data curation. Zaiwu Gong: investigation, visualization, supervision. Ashfaq Ahmad Shah: validation, formal analysis, writing—review and editing. Mirajul Haq: visualization, writing—review and editing.

Corresponding author

Correspondence to Abid Khan.

Ethics declarations

Ethics approval

I certify that this manuscript is original and has not been published and will not be submitted elsewhere for publication while being considered by Environmental Science and Pollution Research. No data have been fabricated or manipulated to support your conclusions.

Consent to participate

Not applicable.

Consent for publication

Not applicable.

Human and animal rights and informed consent

This article does not contain any studies with human participants or animals performed by any of the authors.

Competing interests

The authors declare no competing interests.

Additional information

Responsible Editor: Philippe Garrigues

Publisher’s note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Khan, A., Gong, Z., Shah, A.A. et al. A multi-criteria decision-making approach to vulnerability assessment of rural flooding in Khyber Pakhtunkhwa Province, Pakistan. Environ Sci Pollut Res 30, 56786–56801 (2023). https://doi.org/10.1007/s11356-023-25609-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11356-023-25609-1

Keywords

Search

Navigation