Skip to main content

Advertisement

SpringerLink
Log in

Flood hazard mapping and assessment using fuzzy analytic hierarchy process and GIS techniques in Takelsa, Northeast Tunisia

  • Original Paper
  • Published:
Arabian Journal of Geosciences Aims and scope Submit manuscript

Abstract

Floods are among the most widespread and devastating natural hazard that might be caused human and economic losses. Flood damage mitigation and management may be achieved through implementing proper management and planning strategies. The Takelsa region (Northeast Tunisia) is very susceptible to floods. Therefore, the present study focused on the flood risk mapping and the risk index assessment based on the GIS-FAHP-multi-criteria decision-making process. Eight factors, namely slope, rainfall, distance from streams, drainage density, TWI, land use/land cover, and soil type were used to produce a flood susceptibility prototype and compute the flood hazard potential index (FHpotI) using rating and weight coefficients of each factor. The results achieved show that the slope is the most important flood influencing factor with an occurrence rate equal to 21% and about 52% (144.36 km2) of the total area of the zone is characterized by a flood hazard potential high to very high. Finally, in order to determine of the reliability degree and the prediction accuracy of these results, we applied the ROC curve validation method, which gives an accuracy equal to 87%. This research provided results and recommendations that can be applied by the municipality, the planners, policymakers, and authorities to make decisions in order to ensure sustainable hydrometeorological risk management.

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
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

References

  • Ahmadlou M, Karimi M, Alizadeh S, Shirzadi A, Parvinnejhad D, Shahabi H, Panahi M (2018) Flood susceptibility assessment using integration of adaptive network-based fuzzy inference system (ANFIS) and biogeography-based optimization (BBO) and BAT algorithms (BA). Geocarto Int 34(11):1252–1272

    Article  Google Scholar 

  • Akay H (2021) Flood hazards susceptibility mapping using statistical, fuzzy logic, and MCDM methods. Soft Comput 25:9325–9346

    Article  Google Scholar 

  • Aladejana O, Salami AT, Adetoro OO (2019) Potential flood hazard zone mapping based on geomorphologic considerations and fuzzy analytical hierarchy model in a data scarce West African basin. Geocarto Inter. https://doi.org/10.1080/10106049.2019.1687595

    Article  Google Scholar 

  • Azareh A, Sardooi ER, Bahram Ch, Barkhori S, Shahdadi A, Adamowski J, Shamshirband Sh (2019) Incorporating multicriteria decision-making and fuzzy-value functions for flood susceptibility assessment. Geocarto Inter. https://doi.org/10.1080/10106049.2019.1695958

    Article  Google Scholar 

  • Bui T, Dieu KK, Li S, Shahabi H, Panahi M, Singh VP, Chapi K, Shirzadi A, Panahi S, Chen W (2018) New hybrids of anfis with several optimization algorithms for flood susceptibility modeling. Water 10:1210

    Article  Google Scholar 

  • Bui DT, Khosravi K, Shahabi H, Daggupati P, Adamowski JF, Melesse A, Pham BT, Pourghasemi HR, Mahmoudi M, Bahrami S, Pradhan B, Shirzadi A, Chapi K, Lee S (2019) Flood spatial modeling in Northern Iran using remote sensing and GIS: a comparison between evidential belief functions and its ensemble with a multivariate logistic regression model. Remote Sensing 11:1589

    Article  Google Scholar 

  • Buckley JJ (1985) Fuzzy hierarchical analysis. Fuzzy Sets Syst 17:233–247

    Article  Google Scholar 

  • Cao B, Zhao J, Lv Z, Gu Y, Yang P, Halgamuge SK (2020) Multiobjective evolution of fuzzy rough neural network via distributed parallelism for stock prediction. IEEE Trans Fuzzy Syst 28:939–952

    Article  Google Scholar 

  • Chenini IM, Msaddek MH (2020) Groundwater recharge susceptibility mapping using logistic regression model and bivariate statistical analysis. Q J Eng Geol Hydrogeol 53(2):167–175

    Article  Google Scholar 

  • Çelik HE, Cigizoglu HK, Agiralioglu N, Coskun HG, Esin AI, Aydin A (2012) The analysis of 2004 flood on Kozdere Stream in Istanbul. Nat Hazards 63(2):461–477

    Article  Google Scholar 

  • Chang DY (1996) Applications of the extent analysis method on fuzzy AHP. Eur J Oper Res 95(3):649–655

    Article  Google Scholar 

  • Chapi K, Singh VP, Shirzadi A, Shahabi H, Bui DT, Pham BT, Khosravi K (2017) A novel hybrid artificial intelligence approach for flood susceptibility assessment. Environ Model Softw 95:229–245

    Article  Google Scholar 

  • Chaudhry AK, Kumar K, Alam MA (2019) Mapping of groundwater potential zones using the fuzzy analytic hierarchy process and geospatial technique. Geocarto Int. https://doi.org/10.1080/10106049.2019.1695959

    Article  Google Scholar 

  • Chen HW, Chang NB (2010) Using fuzzy operators to address the complexity in decision making of water resources redistribution in two neighboring river basins. Adva in Water Res 33:652–666

    Article  Google Scholar 

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

  • Costache R, Pham QB, Avand M, Linh NTT, Vojtek M, Motekoa J, Lee S, Khoi DN, Nhi PTT, Dung TD (2020) Novel hybrid models between bivariate statistics, artificial neural networks and boosting algorithms for flood susceptibility assessment. J Environ Manage 265:110485

    Article  Google Scholar 

  • Chung CH-JOF, Fabbri AG (2003) Validation of spatial prediction models for landslide hazard mapping. Nat Hazards 30:451–472

    Article  Google Scholar 

  • Das B, Pal SC (2019) Combination of GIS and fuzzy-AHP for delineating groundwater recharge potential zones in the critical Goghat-II block of West Bengal, India. Hydro Res 2:21–30

    Google Scholar 

  • Das S (2020) Flood susceptibility mapping of the Western Ghat coastal belt using multi-source geospatial data and analytical hierarchy process (AHP). RSASE. https://doi.org/10.1016/j.rsase.2020.100379

    Article  Google Scholar 

  • Dahri N, Abida H (2017) Monte Carlo simulation-aided analytical hierarchy process (AHP) for flood susceptibility mapping in Gabes Basin (southeastern Tunisia). Environ Earth Sci 76:302

    Article  Google Scholar 

  • Darabi H, Choubin B, Rahmati O, Haghighi AT, Pradhan B, Kløve B (2019) Urban flood risk mapping using the GARP and QUEST models: a comparative study of machine learning techniques. J Hydrol 569:142–154

    Article  Google Scholar 

  • Dilley M, Chen RS, Deichmann U, Lerner-L AL, Arnold M (2005) Natural disaster hotspots: a global risk analysis. World Bank, Washington, DC

    Book  Google Scholar 

  • Dottori F, Salamon P, Bianchi A, Alfieri L, Hirpa FA, Feyen L (2016) Development and evaluation of a framework for global flood hazard mapping. Adv Water Res 94:87–102

    Article  Google Scholar 

  • Du X, Ying H, Lin F (2012) On modeling of fuzzy hybrid systems. J Intell Fuzzy Syst 23(4):129–141. https://doi.org/10.3233/IFS-2012-0501

    Article  Google Scholar 

  • Fehri N (2014) L’aggravation du risque d’inondation en Tunis : éléments de réflexion. Physio Géographie 8:149–175

    Google Scholar 

  • Fernández D, Lutz M (2010) Urban flood hazard zoning in Tucum_an Province, Argentina, using GIS and multicriteria decision analysis. Eng Geol 111(1–4):90–98

    Article  Google Scholar 

  • Field CB (2014) Climate change 2014—impacts, adaptation and vulnerability: regional aspects. Cambridge University Press, Cambridge

    Book  Google Scholar 

  • Foudi S, Osés-Eraso N, Tamayo I (2015) Integrated spatial flood risk assessment: the case of Zaragoza. Land Use Policy 42:278–292

    Article  Google Scholar 

  • Getahun YS, Gebre SL (2015) Flood hazard assessment and mapping of flood inundation area of the Awash River Basin in Ethiopia using GIS and HECGeoRAS/HEC-RAS model. J Civ Environ Eng 5:179

    Google Scholar 

  • General Direction of Water Resources (GDWR) (1985 – 2015) Annuaires pluviométrique de la Tunisie (1985–2020). DGRE, Tunisia

  • Gheshlaghi A, Hassan BF, Blaschke T (2020) GIS-based forest fire risk mapping using the analytical network process and fuzzy logic. J Environ Planning Manage 63:481–499

    Article  Google Scholar 

  • Gheshlaghi HA, Feizizadeh B (2021) GIS-based ensemble modelling of fuzzy system and bivariate statistics as a tool to improve the accuracy of landslide susceptibility mapping. Nat Hazards 107:1981–2014

    Article  Google Scholar 

  • Gigović L, Pamučar D, Bajić Z, Drobnjak S (2017) Application of GIS-interval rough AHP methodology for flood hazard mapping in urban areas. Water 9:360

    Article  Google Scholar 

  • Greenbaum D (1989) Hydrogeological applications of remote sensing in areas of crystalline basement. Proc Groundw. Explor. Dev. Cryst. Basement Aquifers, Zimbabwe

  • Gül GO (2013) Estimating flood exposure potentials in Turkish catchments through index-based flood 599 mapping. J Natural Hazards 69(1):403–423

    Article  Google Scholar 

  • Hong H, Panahi M, Shirzadi A, Ma T, Liu J, Zhu AX, Chen W, Kougias Kazakis N (2018) Flood susceptibility assessment in Hengfeng area coupling adaptive neuro-fuzzy inference system with genetic algorithm and differential evolution. Sci Total Environ 621:1124–1141

    Article  Google Scholar 

  • Islam M, Sado K (2000) Flood hazard assessment in Bangladesh using NOAA AVHRR data with geographical information system. Hydrol Process 14(3):605–620

    Article  Google Scholar 

  • Johnston J, MOFPD (1999) The cost effectiveness of fire station siting and the impact on emergency response. An applied research project submitted to the National Fire Academy as part of the Executive Fire Officer Program. Orinda California, p25

  • Kahraman C, Cebeci U, Ruan D (2004) Multi-attribute comparison ofcatering service companies using fuzzy AHP: the case of Turkey. Int J Prod Econ 87:171–184

    Article  Google Scholar 

  • Kanani-Sadat Y, Arabsheibani R, Karimipour F, Nasseri M (2019) A new approach to flood susceptibility assessment in data-scarce and ungauged regions based on GIS-based hybrid multi criteria decision making method. J Hydrol 572:17–31

    Article  Google Scholar 

  • Khosravi K, Pham BT, Chapi K, Shirzadi A, Shahabi H, Revhaug I, Prakash I, Bui DT (2018) A comparative assessment of decision trees algorithms for flash flood susceptibility modeling at Haraz watershed, northern Iran. Sci Total Environ 627:744–755

    Article  Google Scholar 

  • Khosravi K, Shahabi H, Pham BT, Adamowski J, Shirzadi A, Pradhan B, Dou J, Ly H-B, Grof G, Ho HL et al (2019) A comparative assessment of flood susceptibility modeling using multi-criteria decision-making analysis and machine learning methods. J Hydrol 573:311–323

    Article  Google Scholar 

  • Kirkby M.J (1975) Hydrograph modelling strategies. In: Peel R, Chisholm M Haggett, P (eds) Processes in Physical and Human Geography. Academic Press London, 69–90

  • Komolafe AA, Herath S, Avtar R (2018) Methodology to assess potential flood damages in urban areas under the influence of climate change. Nat Hazards Rev 19(2):05018001

    Article  Google Scholar 

  • Kumar S, Kumar Srivastava P, Snehmani (2016) GIS-based MCDA–AHP modelling for avalanche susceptibility mapping of Nubra valley region, Indian Himalaya. Geocarto Inter. https://doi.org/10.1080/10106049.2016.1206626

  • Laarhoven PJMV, Pedrycz W (1983) A fuzzy extension of saaty’s priority theory. Fuzzy Sets Syst 11(1–3):199–227

    Google Scholar 

  • Lee S, Kim J-C, Jung H-S, Lee MJ, Lee S (2017) Spatial prediction of flood susceptibility using random-forest and boosted-tree models in Seoul metropolitan city, Korea. Geomat Nat Hazards Risk 8:1185–1203

    Article  Google Scholar 

  • Li K, Shaohong W, Dai E, Zhongchun X (2012) Flood loss analysis and quantitative risk assessment in China. Nat Hazards 63:737–760

    Article  Google Scholar 

  • Leung LC, Cao D (2000) Theory and methodology on consistency and ranking of alternatives in fuzzy AHP. Eur J of Opera Res 124(2000):102–113

    Article  Google Scholar 

  • Mahmoud SH, Gan TY (2018a) Multi-criteria approach to develop flood susceptibility maps in arid regions of Middle East. J Clean Prod 196:216–229

    Article  Google Scholar 

  • Mahmoud SH, Gan TY (2018b) Urbanization and climate change implications in flood risk management: developing an efficient decision support system for flood susceptibility mapping. Sci Total Environ 636:152–167

    Article  Google Scholar 

  • Mojaddadi H, Pradhan B, Nampak H, Ahmad N, bin Ghazali AH, (2017) Ensemble machine-learning-based geospatial approach for flood risk assessment using multisensor remote-sensing data and GIS. Geomat Nat Haz Risk 8(2):1080–1102

    Article  Google Scholar 

  • Moore ID, Grayson RB, Ladson AR (1991) Digital terrain modelling: a review of hydrological, geomorphological, and biological applications. Hydrol Process 5:3–30

    Article  Google Scholar 

  • Moradi H, Avand MT, Janizadeh S (2019) Landslide susceptibility survey using modeling methods. In: Spatial modeling in GIS and R for earth and environmental sciences Elsevier p.259–275

  • Mosavi A, Ozturk P, Chau KW (2018) Flood prediction using machine learning models: literature review. Water 10(11):1536

    Article  Google Scholar 

  • Msaddek MH, Moumni Y, Haji T, Chenini I, Dlala M (2021a) A fuzzy mathematical model for evaluation of rock-fracture and structural complexity: application for Southern Atlas in Tunisia. Acta Geod Geoph. https://doi.org/10.1007/s40328-021-00347-3

    Article  Google Scholar 

  • Msaddek MH, Ben Alaya M, Moumni Y, Ayari A, Chenini I (2021b) Enhanced machine learning model to estimate groundwater spring potential based on Digital Elevation Model parameters. Geocarto Int. https://doi.org/10.1080/10106049.2021.2007292

  • Msaddek MH, Souissi D, Moumni Y, Chenini I, Bouaziz N, Dlala M (2019) Groundwater potentiality assessment in an arid zone using a statistical approach and multi-criteria evaluation, southwestern Tunisia. Geol Q 63(1):3–15

  • Nampak H, Pradhan B, Manap MA (2014) Application of GIS based data driven evidential belief function model to predict groundwater potential zonation. J of Hydro 513(2014):283–300

    Article  Google Scholar 

  • Nandalal HK, Ratnayake UR (2011) Flood risk analysis using fuzzy models. J Flood Risk Management 4(2011):128–139

    Article  Google Scholar 

  • National Institute of Statistics (NIS) (2021) Tunisia statistic 

  • National Meteorological Institute (NMI) (2018) Meteorological data. Tunisia

  • National Meteorological Institute (NMI) (2021) Meteorological datas (1985–2020). Tunisia

  • Pour SH, Wahab AKA, Shahid S, Asaduzzaman, Dewan A (2020) Low impact development techniques to mitigate the impacts of climate-change-induced urban floods: current trends, issues and challenges, sustainable cities and society. https://doi.org/10.1016/j.scs.2020.102373

  • Pourghasemi HR, Razavi-Termeh SV, Kariminejad N, Hong H, Chen W (2020) An assessment of metaheuristic approaches for flood assessment. J Hydrol 582:124536

    Article  Google Scholar 

  • Rahmati O, Pourghasemi HR, Zeinivand H (2016) Flood susceptibility mapping using frequency ratio and weights-of-evidence models in the Golastan Province. Iran Geocarto Int 31:42–70

    Article  Google Scholar 

  • Rahmati O, Zeinivand H, Besharat M (2016b) Flood hazard zoning in Yasooj region, Iran, using GIS and multi-criteria decision analysis. Geomat Nat Hazards Risk 7(3):1000–1017

    Article  Google Scholar 

  • Rehman S, Sahana M, Hong H, Sajjad H, Ahmed BB (2019) A systematic review on approaches and methods used for flood vulnerability assessment: framework for future research. Nat Hazards 96(2):975–998

    Article  Google Scholar 

  • Rossi G, Cancelliere A, Giuliano G (2005) Case study: multicriteria assessment of drought mitigation measures. J Water Resour Plan Manag 131:449–457

    Article  Google Scholar 

  • Roy S, Bose A, Chowdhury IR (2021) Flood risk assessment using geospatial data and multi-criteria decision approach: a study from historically active flood-prone region of Himalayan foothill. India Arab J Geosci 14:999

    Article  Google Scholar 

  • Sadiq R, Husain T (2005) A fuzzy-based methodology for an aggregative environmental risk assessment: a case study of drilling waste. Environ Model Software 20(1):33–46

    Article  Google Scholar 

  • Sahana M, Patel PP (2019) A comparison of frequency ratio and fuzzy logic models for flood susceptibility assessment of the lower Kosi River Basin in India. Enviro Earth Sci 78(2019):289

    Article  Google Scholar 

  • Salman AB, Howari FM, El-Sankary MM, Wal AM, Saleh MM (2010) Environmental impact and natural hazards on Kharga Oasis monumental sites, Western Desert of Egypt. J Afr Earth Sci 58:341–353

    Article  Google Scholar 

  • Shaban A, Khawlie M, Abdallah C (2006) Use of remote sensing and GIS to determine recharge potential zones: the case of Occidental Lebanon. Hydrogeol J 14(4):433–443

    Article  Google Scholar 

  • Shahabi H, Shirzadi A, Ghaderi K, Omidvar E, Al-Ansari N, Clague JJ, Geertsema M, Khosravi K, Amini A, Bahrami S (2020) Flood detection and susceptibility mapping using sentinel-1 remote sensing data and a machine learning approach: hybrid intelligence of bagging ensemble based on K-nearest neighbor classifier. Remote Sens 12:266

    Article  Google Scholar 

  • Samanta S, Pal DK, Palsamanta B (2018) Flood susceptibility analysis through remote sensing, GIS and frequency ratio model. Appl Water Sci 8:66

    Article  Google Scholar 

  • Santos PP, Reis E, Pereira S, Santos M (2019) A flood susceptibility model at the national scale based on multicriteria analysis. Sci Total Environ 667:325–337

    Article  Google Scholar 

  • Sener E, Şener S, Davraz A (2018) Groundwater potential mapping by combining fuzzy-analytic hierarchy process and GIS in Beys ¸ehir Lake Basin Turkey. Arab J Geosci. 11(8):187–208

    Article  Google Scholar 

  • Stefanidis S, Stathis D (2013) Assessment of flood hazard based on natural and anthropogenic factors using analytic hierarchy process (AHP). Nat Hazards 68:569–585

    Article  Google Scholar 

  • Swets JA (1988) Measuring the accuracy of diagnostic systems. Sci (New York, N.Y.) 240 (4857):1285–1293. https://doi.org/10.1126/science.3287615.

  • Souissi D, Msaddek MH, Zouhri L, Chenini I, El May M, Mahmoud D (2018) Mapping groundwater recharge potential zones in arid region using GIS and Landsat approaches, southeast Tunisia. Hydrol Sci J. https://doi.org/10.1080/02626667.2017.1414383

    Article  Google Scholar 

  • Souissi D, Lahcen Z, Salma H, Mohamed HM, Adel Z, Mahmoud D (2019) GIS-based MCDM–AHP modeling for flood susceptibility mapping of arid areas, southeastern Tunisia. Geocarto Inter pp 1–27. https://doi.org/10.1080/10106049.2019.1566405

  • Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R,(2014) Dropout: a simple way to prevent neural networks from overfitting. JMLR, 15(1), 1929-1958

  • Tang Z, Zhang H, Yi S, Xiao Y (2018) Assessment of flood susceptible areas using spatially explicit, probabilistic multi-criteria decision analysis. J Hydrol 558:144–158

    Article  Google Scholar 

  • Tang X, Li J, Liu M, Liu W, Hong H (2020) Flood susceptibility assessment based on a novel random Naïve Bayes method: a comparison between different factor discretization methods. CATENA 190:104536. https://doi.org/10.1016/j.catena.2020.104536

    Article  Google Scholar 

  • Tehrany MS, Pradhan B, Jebur MN (2014) Flood susceptibility mapping using a novel ensemble weights of evidence and support vector machine models in GIS. J Hydrol 512:332–343

    Article  Google Scholar 

  • Tehrany MS, Kumar L (2018) The application of a Dempster-Shaferbased evidential belief function in flood susceptibility mapping and comparison with frequency ratio and logistic regression methods. Environ Earth Sci 77:490

    Article  Google Scholar 

  • Tella A, Balogun AL (2020) Ensemble fuzzy MCDM for spatial assessment of flood susceptibility in Ibadan, Nigeria. Nat Hazards (104):2277–2306. https://doi.org/10.1007/s11069-020-04272-6

  • Tuzmen S and Sipahi (2011) March. A multi-criteria factor evaluation model for gas station site selection. In 2nd International Conference on Business and Economic Research (2nd ICBER 2011) Proceeding (No. 2011–164). Conference Master Resources

  • Vafakhah M, Mohammad Hasani Loor S, Pourghasemi H, Katebikord A (2020) Comparing performance of random forest and adaptive neuro-fuzzy inference system data mining models for flood susceptibility mapping. Arab J Geosci 13:417

    Article  Google Scholar 

  • Wang Y, Fang Z, Hong H, Costache R, Tang X (2021) Flood susceptibility mapping by integrating frequency ratio and inde of entropy with multilayer perceptron and classification and regression tree. J Environ Manage 289:112449

    Article  Google Scholar 

  • Wang Y, Hong H, Chen W, Li S, Panahi M, Khosravi K, Shirzadi A, Shahabi H, Panahi S, Costache R (2019) Flood susceptibility mapping in Dingnan County (China) using adaptive neuro-fuzzy inference system with biogeography based optimization and imperialistic competitive algorithm. J Environ Manage 247:712–729

    Article  Google Scholar 

  • Ward RC, Robinson M (2000) Principles of hydrology, 4th edn. McGraw-Hill, Maidenhead

    Google Scholar 

  • Xu J, Wang Z, Shen F, Ouyang Ch, Tu Y (2016) Natural disasters and social conflict: a systematic literature review. Int J Disaster Risk Reduct. https://doi.org/10.1016/j.ijdrr.2016.04.001

    Article  Google Scholar 

  • Yang M, Khan FI, Sadiq R (2011) Prioritization of environmental issues in off shore oil and gas operations: a hybrid approach using fuzzy inference system and fuzzy analytic hierarchy process. Process Saf Environ Prot 89(1):22–34

    Article  Google Scholar 

  • Yesilnacar E, Topal T (2005) Landslide susceptibility mapping: a comparison of logistic regression and neural networks methods in a medium scale study, Hendek region (Turkey). Eng Geol 79:251–266

    Article  Google Scholar 

  • Youssef AM, Pradhan B, Sefry SA (2016) Flash flood susceptibility assessment in Jeddah city (Kingdom of Saudi Arabia) using bivariate and multivariate statistical models. Environ Earth Sci J 75:12

    Article  Google Scholar 

  • Zadeh LA (1996) (1996) Fuzzy logic computing with words. IEEE Transactions Fuzzy Syst 4(2):103–111

    Article  Google Scholar 

  • Zghibi A, Mirchi A, Msaddek MH, Merzougui A, Zouhri L, Taupin JD, Chekirbane A, Chenini I (2020) Tarhouni J (2020) Using analytical hierarchy process and multi-influencing factors to map groundwater recharge zones in a semi-arid Mediterranean coastal aquifer. Water, MDPI 12(9):2525

    Article  Google Scholar 

  • Zhao G, Pang B, Xu Z, Yue J, Tu T (2018) Mapping flood susceptibility in mountainous areas on a national scale in China. Sci Total Environ 615:1133–1142

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to give thanks to Mariem Saadi for her language assistance and proofreading.

Funding

This work was supported by Doctoral School, Faculty of Sciences, Tunis El Manar University.

Author information

Authors and Affiliations

  1. LR 01ES06 Laboratory of Mineral Resources and Environment (LRME), Department of Geology, Faculty of Sciences of Tunis, University of Tunis El Manar, 2092, Tunis, Tunisia

    Dhekra Souissi, Abdelaziz Sebei, Adel Zghibi & Mohamed Ghanmi

  2. Georesources Laboratory, Water Researches and Technologies Center, University of Carthage, Technoparck of Borj Cedria, Soliman, Tunisia

    Ali Souie

  3. Ministry of Agriculture, Water Resources and Fisheries, Alain Savary, 1002, Tunis, Tunisia

    Rabeb Mahfoudhi

  4. AGHYLE, SFR Condorcet FR CNRS 3417, Polytechnic Institute UniLaSalle Beauvais, 19 Pierre Waguet Street, 60026, Beauvais, France

    Lahcen Zouhri

  5. Municipality of Takelsa, 8031, Nabeul, Tunisia

    Walid Amiri

Authors
  1. Dhekra Souissi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ali Souie
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Abdelaziz Sebei
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Rabeb Mahfoudhi
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Adel Zghibi
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Lahcen Zouhri
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Walid Amiri
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Mohamed Ghanmi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dhekra Souissi.

Ethics declarations

Conflict of interest

The authors declare no competing interests.

Additional information

Responsible Editor: Fethi Lachaal

This article is part of the Topical Collection on Water Quality, Global Changes and Groundwater Responses.

Rights and permissions

Springer Nature or its licensor 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

Souissi, D., Souie, A., Sebei, A. et al. Flood hazard mapping and assessment using fuzzy analytic hierarchy process and GIS techniques in Takelsa, Northeast Tunisia. Arab J Geosci 15, 1405 (2022). https://doi.org/10.1007/s12517-022-10541-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12517-022-10541-4

Keywords

Search

Navigation