Skip to main content

Advertisement

SpringerLink
Log in

GIS-Based Multi-criteria Decision-Making Techniques and Analytical Hierarchical Process for Flash Flood Risk Assessment Due to a Possible Dam Break in Urban Arid Environment: Case Study of Biskra City, Southern Algeria

  • Research Article
  • Published:
Journal of the Indian Society of Remote Sensing Aims and scope Submit manuscript

Abstract

Urban flooding is the most frequent and damaging of all natural hazards, including those caused by dam breaks. One flood mitigation and control method is using flood risk maps to provide helpful information. This study aims to map the three parameters of urban flooding: exposure, vulnerability, and flood risk following a hypothetical failure of the Gazelles fountain dam (Manbaa Al-Ghozlan) in the city of Biskra (40 km downstream of the dam) through a multi-criteria decision-making process (MCDM). Six influencing factors were used for the vulnerability assessment: land use, distance from significant watercourses (Oued), distance from drainage accumulation, elevation, population density and distance from main roads. The influence of each factor was examined by attributing weights to each criterion according to its impact on urban flood vulnerability. A flood risk map that can evaluate the extent of damage and its economic implications is made based on hazard and vulnerability maps. Based on pixel values, the flood risk map has been divided into four classes: very high, high, moderate and low. The results indicate that more than 63% of the area is at high risk. The water depth in these areas may exceed 10 m in the case of dam failure. The constructed flood risk map is an informative tool to assess critical damage for decision-makers in arid and semi-arid 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
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Data Availability

The data presented in this study are available on request from the corresponding author.

References

  • Abdrabo, K. I., Kantoush, S. A., Saber, M., Sumi, T., Habiba, O. M., Elleithy, D., & Elboshy, B. (2020). Integrated methodology for urban flood risk mapping at the microscale in ungauged regions: A case study of Hurghada, Egypt. Remote Sensing, 12(21), 3548. https://doi.org/10.3390/rs12213548

    Article  Google Scholar 

  • Armenakis, C., & Nirupama, N. (2014). Flood risk mapping for the city of Toronto. Procedia Economics and Finance, 18, 320–326. https://doi.org/10.1016/s2212-5671(14)00946-0

    Article  Google Scholar 

  • Bales, J., Sarver, K. M., & Giorgino, M. J. (2001). Mountain Island Lake, North Carolina: Analysis of Ambient Conditions and Simulation of Hydrodynamics, Constituent Transport, and Water-quality Characteristics, 1996–97 (Vol. 1, No. 4138). US Department of the Interior, US Geological Survey.

  • Balogun, O. S., & Ganiyu, H. O. (2017). Development of Inundation Map for Hypothetical Asa Dam Break using HEC-RAS and ARC GIS. Arid Zone Journal of Engineering, Technology and Environment, 13(6), 831.

    Google Scholar 

  • Bendib, A. (2021). High-resolution Alos Palsar for the Characterization of Water Storage at the Fountaine Des Gazelles Dam in Biskra, Eastern Algeria. Journal of the Indian Society of Remote Sensing, 49, 1927–1938. https://doi.org/10.1007/s12524-021-01365-y

    Article  Google Scholar 

  • Boultif, M., & Benmessaoud, H. (2017). Using climate-soil-socioeconomic parameters for a drought vulnerability assessment in a semi-arid region: Application at the region of El Hodna, (M’sila, Algeria). Geographica Pannonica, 21(3), 142–150. https://doi.org/10.5937/GeoPan1703142B

    Article  Google Scholar 

  • Boumsenegh, A. (2007). Les inondations dans la ville de BISKRA Causes et Impacts. Magister thesis. Faculty of Engeneering science,University of El Hadj Lakhdar, Batna

  • Butt, M. J., Umar, M., & Qamar, R. (2013). Landslide dam and subsequent dam-break flood estimation using HEC-RAS model in Northern Pakistan. Natural Hazards, 65, 241–254. https://doi.org/10.1007/s11069-012-0361-8

    Article  Google Scholar 

  • Chukwuma, E. C., Okonkwo, C. C., Ojediran, J. O., Anizoba, D. C., Ubah, J. I., & Nwachukwu, C. P. (2021). A GIS based flood vulnerability modelling of Anambra State using an integrated IVFRN-DEMATEL-ANP model. Heliyon. https://doi.org/10.1016/j.heliyon.2021.e08048

    Article  Google Scholar 

  • da Silva, L. B. L., Humberto, J. S., Alencar, M. H., Ferreira, R. J. P., & de Almeida, A. T. (2020). GIS-based multidimensional decision model for enhancing flood risk prioritization in urban areas. International Journal of Disaster Risk Reduction, 48, 101582. https://doi.org/10.1016/j.ijdrr.2020.101582

    Article  Google Scholar 

  • Elfeki, A., Al-Shabani, A., Bahrawi, J., & Alzahrani, S. (2018). Quick urban flood risk assessment in arid environment using HECRAS and dam break theory: case study of Daghbag Dam in Jeddah, Saudi Arabia. In Recent Advances in Environmental Science from the Euro-Mediterranean and Surrounding Regions: Proceedings of Euro-Mediterranean Conference for Environmental Integration (EMCEI-1), Tunisia 2017 (pp. 1917–1919). Springer. https://doi.org/10.1007/978-3-319-70548-4_553

  • Froehlich, D. C. (2008). Embankment dam breach parameters and their uncertainties. Journal of Hydraulic Engineering, 134(12), 1708–1721.

    Article  Google Scholar 

  • Gitz, V., Meybeck, A., Lipper, L., Young, C. D., & Braatz, S. (2016). Climate change and food security: Risks and responses. Food and Agriculture Organization of the United Nations (FAO) Report, 110(2).

  • Hadipour, V., Vafaie, F., & Kerle, N. (2020). An indicator-based approach to assess social vulnerability of coastal areas to sea-level rise and flooding: A case study of Bandar Abbas city, Iran. Ocean & Coastal Management, 188, 105077. https://doi.org/10.1016/j.ocecoaman.2019.105077

    Article  Google Scholar 

  • Hamlat, A., Kadri, C. B., Guidoum, A., & Bekkaye, H. (2021). Flood hazard areas assessment at a regional scale in M’zi wadi basin, Algeria. Journal of African Earth Sciences, 182, 104281. https://doi.org/10.1016/j.jafrearsci.2021.104281

    Article  Google Scholar 

  • Islam, M. M., Ujiie, K., Noguchi, R., & Ahamed, T. (2022). Flash flood-induced vulnerability and need assessment of wetlands using remote sensing, GIS, and econometric models. Remote Sensing Applications: Society and Environment, 25, 100692. https://doi.org/10.1016/j.rsase.2021.100692

    Article  Google Scholar 

  • Khosravi, K., Nohani, E., Maroufinia, E., & Pourghasemi, H. R. (2016). A GIS-based flood susceptibility assessment and its mapping in Iran: A comparison between frequency ratio and weights-of-evidence bivariate statistical models with multi-criteria decision-making technique. Natural Hazards, 83, 947–987. https://doi.org/10.1007/s11069-016-2357-2

    Article  Google Scholar 

  • Kittipongvises, S., Phetrak, A., Rattanapun, P., Brundiers, K., Buizer, J. L., & Melnick, R. (2020). AHP-GIS analysis for flood hazard assessment of the communities nearby the world heritage site on Ayutthaya Island, Thailand. International Journal of Disaster Risk Reduction, 48, 101612. https://doi.org/10.1016/j.ijdrr.2020.101612

    Article  Google Scholar 

  • Kopackova, H., Komarkova, J., & Sedlák, P. (2007). Knowledge pre-processing in decision making. In 7th WSEAS International Conference on APPLIED COMPUTER SCIENCE, Venice, Italy.

  • Levy, J. K., Hartmann, J., Li, K. W., An, Y., & Asgary, A. (2007). Multi-criteria decision support systems for flood hazard mitigation and emergency response in urban watersheds 1. JAWRA Journal of the American Water Resources Association, 43(2), 346–358. https://doi.org/10.1111/j.1752-1688.2007.00027.x

    Article  Google Scholar 

  • Li, Q., Zhou, J., Liu, D., & Jiang, X. (2012). Research on flood risk analysis and evaluation method based on variable fuzzy sets and information diffusion. Safety Science, 50(5), 1275–1283. https://doi.org/10.1016/j.ssci.2012.01.007

    Article  Google Scholar 

  • Luo, X., & Dimitrakopoulos, R. (2003). Data-driven fuzzy analysis in quantitative mineral resource assessment. Computers & Geosciences, 29(1), 3–13. https://doi.org/10.1016/S0098-3004(02)00078-X

    Article  CAS  Google Scholar 

  • Mahmoud, S. H., & Gan, T. Y. (2018). Urbanization and climate change implications in flood risk management: Developing an efficient decision support system for flood susceptibility mapping. Science of the Total Environment, 636, 152–167. https://doi.org/10.1016/j.scitotenv.2018.04.282

    Article  CAS  Google Scholar 

  • Njoku, C., Efiong, J., Uzoezie, A., Okeniyi, F., & Alagbe, A. (2018). A GIS multi-criteria evaluation for flood risk-vulnerability mapping of Ikom local government area, cross river state. Journal of Geography, Environment and Earth Science International, 15(2), 1–17. https://doi.org/10.9734/jgeesi/2018/40527

    Article  Google Scholar 

  • Okonufua, E., Olajire, O. O., & Ojeh, V. N. (2019). Flood vulnerability assessment of Afikpo South local government area, Ebonyi State, Nigeria. International Journal of Environment and Climate Change, 9(6), 331–342. https://doi.org/10.9734/ijecc/2019/v9i630118

    Article  Google Scholar 

  • Quirogaa, V. M., Kurea, S., Udoa, K., & Manoa, A. (2016). Application of 2D numerical simulation for the analysis of the February 2014 Bolivian Amazonia flood: Application of the new HEC-RAS version 5. Ribagua, 3(1), 25–33. https://doi.org/10.1016/j.riba.2015.12.001

    Article  Google Scholar 

  • Rafiei-Sardooi, E., Azareh, A., Choubin, B., Mosavi, A. H., & Clague, J. J. (2021). Evaluating urban flood risk using hybrid method of TOPSIS and machine learning. International Journal of Disaster Risk Reduction, 66, 102614. https://doi.org/10.1016/j.ijdrr.2021.102614

    Article  Google Scholar 

  • Saaty, T. L. (1988). What is the analytic hierarchy process? Introduction In our everyday life, we must constantly make choices concerning what tasks to do or not to do, when to do them, and whether to do them at all. University of Pittsburgh.

    Google Scholar 

  • Saber, M., Hamaguchi, T., Kojiri, T., Tanaka, K., & Sumi, T. (2015). A physically based distributed hydrological model of wadi system to simulate flash floods in arid regions. Arabian Journal of Geosciences, 8, 143–160. https://doi.org/10.1007/s12517-013-1190-0

    Article  Google Scholar 

  • Singh, G., & Pandey, A. (2021). Flash flood vulnerability assessment and zonation through an integrated approach in the Upper Ganga Basin of the Northwest Himalayan region in Uttarakhand. International Journal of Disaster Risk Reduction, 66, 102573. https://doi.org/10.1016/j.ijdrr.2021.102573

    Article  Google Scholar 

  • Sumi, T., Kantoush, S. A., & Saber, M. (2022). Wadi flash floods: Challenges and advanced approaches for disaster risk reduction. Berlin: Springer.

    Book  Google Scholar 

  • Tanoue, M., Hirabayashi, Y., & Ikeuchi, H. (2016). Global-scale river flood vulnerability in the last 50 years. Scientific Reports, 6(1), 36021. https://doi.org/10.1038/srep36021

    Article  CAS  Google Scholar 

  • Versini, P. A., Gaume, E., & Andrieu, H. (2010). Assessment of the susceptibility of roads to flooding based on geographical information–test in a flash flood prone area (the Gard region, France). Natural Hazards and Earth System Sciences, 10(4), 793–803.

    Article  Google Scholar 

  • Zhao, G., Pang, B., Xu, Z., Peng, D., & Xu, L. (2019). Assessment of urban flood susceptibility using semi-supervised machine learning model. Science of the Total Environment, 659, 940–949. https://doi.org/10.1016/j.scitotenv.2018.12.217

    Article  CAS  Google Scholar 

Download references

Funding

The research presented in this paper was financially supported by the Directorate General for Scientific Research and Technological Development (DGRSDT, its French acronym).

Author information

Authors and Affiliations

  1. Ecology of Arid Ecosystems and Climate Risks Division, Scientific and Technical Research Center on Arid Regions CRSTRA, Biskra, Algeria

    Meriem Boultif & Ali Hachemi

  2. LARGHYDE Laboratory, Department of Civil Engineering and Hydraulics, Faculty of Sciences and Technology, Mohamed Khider University, Biskra, Algeria

    Billal Kheloufi & Leila Mimeche

  3. Research Laboratory in Subterranean and Surface Hydraulics -LARHYSS, Université Mohamed Khider, Biskra, Algeria

    Ali Hachemi

Authors
  1. Meriem Boultif
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Billal Kheloufi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ali Hachemi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Leila Mimeche
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Meriem Boultif.

Ethics declarations

Conflict of interest

The authors declare no conflict of interest.

Ethical approval

Ethical approval is not applicable for this article.

Additional information

Publisher's Note

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

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Boultif, M., Kheloufi, B., Hachemi, A. et al. GIS-Based Multi-criteria Decision-Making Techniques and Analytical Hierarchical Process for Flash Flood Risk Assessment Due to a Possible Dam Break in Urban Arid Environment: Case Study of Biskra City, Southern Algeria. J Indian Soc Remote Sens (2024). https://doi.org/10.1007/s12524-024-01860-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12524-024-01860-y

Keywords

Search

Navigation