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The use of subjective–objective weights in GIS-based multi-criteria decision analysis for flood hazard assessment: a case study in Mazandaran, Iran

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Abstract

The assessment of flooding areas and developing flood hazard maps play a key role in prevention of the many social, economic and environmental damages caused by flood. Most parts of Mazandaran, Iran are at risk of flooding caused by heavy rainfall and rivers. In this paper, the flood hazard map of Mazandaran province is assessed using subjective and subjective–objective weights in an Ordered Weighted Averaging-based GIS analysis. Flood hazard maps are produced based on the two types of weights, along the scale ranging from the pessimistic to optimistic decision strategies. The accuracy of the flood hazard maps was evaluated based on: (1) the percentage of historic flood occurrences that are within the flood hazard maps and (2) the assessment ratio, which is the ratio of known flood areas to whole area in a particular class of flood hazard maps. The results indicate that the percentage of flood areas produced by subjective and subjective–objective weights in “Very high class” are the same in the cases of most pessimistic (14.65%) and optimistic strategy (100%). However, in other strategies (0 < ORness < 1), the subjective–objective weights show better values than subjective weights for percentage of flood hazard areas. Similarly, the assessment ratio results show that flood hazard areas produced by subjective and subjective–objective weights in “Very high class” are the same in the most pessimistic (4.47) and optimistic strategies (1.2).

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References

  • Ahmadisharaf, E., Kalyanapu, A. J., & Chung, E.-S. (2016). Spatial probabilistic multi-criteria decision making for assessment of flood management alternatives. Journal of Hydrology, 533, 365–378.

    Google Scholar 

  • Alemi-Ardakani, M., Milani, A. S., Yannacopoulos, S., & Shokouhi, G. (2016). On the effect of subjective, objective and combinative weighting in multiple criteria decision making: A case study on impact optimization of composites. Expert Systems with Applications, 46, 426–438.

    Google Scholar 

  • Aye, Z. C., Sprague, T., Cortes, V. J., Prenger-Berninghoff, K., Jaboyedoff, M., & Derron, M.-H. (2016). A collaborative (web-GIS) framework based on empirical data collected from three case studies in Europe for risk management of hydro-meteorological hazards. International Journal of Disaster Risk Reduction, 15, 10–23.

    Google Scholar 

  • Bahremand, A., De Smedt, F., Corluy, J., Liu, Y., Poorova, J., Velcicka, L., et al. (2007). WetSpa model application for assessing reforestation impacts on floods in Margecany–Hornad watershed, Slovakia. Water Resources Management, 21(8), 1373–1391.

    Google Scholar 

  • Bannari, A., Ghadeer, A., El-Battay, A., Hameed, N. A., & Rouai, M. (2017). Detection of areas associated with flash floods and erosion caused by rainfall storm using topographic attributes, hydrologic indices, and GIS global changes and natural disaster management: Geo-information technologies (pp. 155–174). Cham: Springer.

    Google Scholar 

  • Berger, P. A. (2006). Generating agricultural landscapes for alternative futures analysis: A multiple attribute decision-making model. Transactions in GIS, 10(1), 103–120.

    Google Scholar 

  • Boroushaki, S., & Malczewski, J. (2008). Implementing an extension of the analytical hierarchy process using ordered weighted averaging operators with fuzzy quantifiers in ArcGIS. Computers & Geosciences, 34(4), 399–410.

    Google Scholar 

  • Chatterjee, C., Foerster, S., & Bronstert, A. (2008). Comparison of hydrodynamic models of different complexities to model floods with emergency storage areas. Hydrological Processes, 22(24), 4695–4709.

    Google Scholar 

  • Chen, H., Ito, Y., Sawamukai, M., & Tokunaga, T. (2015). Flood hazard assessment in the Kujukuri Plain of Chiba Prefecture, Japan, based on GIS and multicriteria decision analysis. Natural Hazards, 78(1), 105–120.

    Google Scholar 

  • Choubin, B., Moradi, E., Golshan, M., Adamowski, J., Sajedi-Hosseini, F., & Mosavi, A. (2019). An ensemble prediction of flood susceptibility using multivariate discriminant analysis, classification and regression trees, and support vector machines. Science of the Total Environment, 651, 2087–2096.

    Google Scholar 

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

    Google Scholar 

  • Davoudi Moghaddam, D., Pourghasemi, H. R., & Rahmati, O. (2019). Assessment of the contribution of geo-environmental factors to flood inundation in a semi-arid region of SW Iran: Comparison of different advanced modeling approaches. In H. Pourghasemi & M. Rossi (Eds.), Natural hazards GIS-based spatial modeling using data mining techniques (pp. 59–78). Berlin: Springer.

    Google Scholar 

  • Deng, H., Yeh, C.-H., & Willis, R. J. (2000). Inter-company comparison using modified TOPSIS with objective weights. Computers & Operations Research, 27(10), 963–973.

    Google Scholar 

  • Esmaili, R., Jokar, S. E., & Roshan, N. P. (2016). Determining the potential of flooding using TOPSIS method, case study 1011 mazandaran province, galandrud catchment. Journal of Physical Geography, 9(1), 77–87.

    Google Scholar 

  • Feizizadeh, B., Jankowski, P., & Blaschke, T. (2014). A GIS based spatially-explicit sensitivity and uncertainty analysis approach for multi-criteria decision analysis. Computers & Geosciences, 64, 81–95. https://doi.org/10.1016/j.cageo.2013.11.009.

    Article  Google Scholar 

  • Fernández, D., & Lutz, M. (2010). Urban flood hazard zoning in Tucumán Province, Argentina, using GIS and multicriteria decision analysis. Engineering Geology, 111(1), 90–98.

    Google Scholar 

  • Gamper, C., Thöni, M., & Weck-Hannemann, H. (2006). A conceptual approach to the use of cost benefit and multi criteria analysis in natural hazard management. Natural Hazards and Earth System Science, 6(2), 293–302.

    Google Scholar 

  • Gashaw, W., & Legesse, D. (2011). Flood hazard and risk assessment using GIS and remote sensing in Fogera Woreda, northwest Ethiopia Nile River Basin (pp. 179–206). Berlin: Springer.

    Google Scholar 

  • Jahan, A., Mustapha, F., Sapuan, S., Ismail, M. Y., & Bahraminasab, M. (2012). A framework for weighting of criteria in ranking stage of material selection process. The International Journal of Advanced Manufacturing Technology, 58(1–4), 411–420.

    Google Scholar 

  • Jayakrishnan, R., Srinivasan, R., Santhi, C., & Arnold, J. (2005). Advances in the application of the SWAT model for water resources management. Hydrological Processes, 19(3), 749–762.

    Google Scholar 

  • Jelokhani-Niaraki, M. (2018). Knowledge sharing in Web-based collaborative multicriteria spatial decision analysis: An ontology-based multi-agent approach. Computers, Environment and Urban Systems, 72, 104–123.

    Google Scholar 

  • Jelokhani-Niaraki, M., & Malczewski, J. (2015a). A group multicriteria spatial decision support system for parking site selection problem: A case study. Land Use Policy, 42, 492–508. https://doi.org/10.1016/j.landusepol.2014.09.003.

    Article  Google Scholar 

  • Jelokhani-Niaraki, M., & Malczewski, J. (2015b). Decision complexity and consensus in web-based spatial decision making: A case study of site selection problem using GIS and multicriteria analysis. Cities, 45, 60–70.

    Google Scholar 

  • Jelokhani-Niaraki, M., Sadeghi-Niaraki, A., & Choi, S. M. (2018). Semantic interoperability of GIS and MCDA tools for environmental assessment and decision making. Environmental Modelling & Software, 100, 104–122.

    Google Scholar 

  • Jiang, H., & Eastman, J. R. (2000). Application of fuzzy measures in multi-criteria evaluation in GIS. International Journal of Geographical Information Science, 14(2), 173–184.

    Google Scholar 

  • Kazakis, N., Kougias, I., & Patsialis, T. (2015). Assessment of flood hazard areas at a regional scale using an index-based approach and analytical hierarchy process: application in Rhodope–Evros region, Greece. Science of the Total Environment, 538, 555–563.

    Google Scholar 

  • Khosravi, K., Pham, B. T., Chapi, K., Shirzadi, A., Shahabi, H., Revhaug, I., et al. (2018). A comparative assessment of decision trees algorithms for flash flood susceptibility modeling at Haraz watershed, northern Iran. Science of the Total Environment, 627, 744–755.

    Google Scholar 

  • Khosravi, K., Pourghasemi, H. R., Chapi, K., & Bahri, M. (2016). Flash flood susceptibility analysis and its mapping using different bivariate models in Iran: A comparison between Shannon’s entropy, statistical index, and weighting factor models. Environmental Monitoring and Assessment, 188(12), 656.

    Google Scholar 

  • Kia, M. B., Pirasteh, S., Pradhan, B., Mahmud, A. R., Sulaiman, W. N. A., & Moradi, A. (2012). An artificial neural network model for flood simulation using GIS: Johor River Basin, Malaysia. Environmental Earth Sciences, 67(1), 251–264.

    Google Scholar 

  • Kiavarz, M., & Jelokhani-Niaraki, M. (2017). Geothermal prospectivity mapping using GIS-based ordered weighted averaging approach: A case study in Japan’s Akita and Iwate provinces. Geothermics, 70, 295–304. https://doi.org/10.1016/j.geothermics.2017.06.015.

    Article  Google Scholar 

  • Kiavarz Moghaddam, M., Samadzadegan, F., Noorollahi, Y., Sharifi, M. A., & Itoi, R. (2014). Spatial analysis and multi-criteria decision making for regional-scale geothermal favorability map. Geothermics, 50, 189–201.

    Google Scholar 

  • Kourgialas, N. N., & Karatzas, G. P. (2011). Flood management and a GIS modelling method to assess flood-hazard areas—A case study. Hydrological Sciences Journal-Journal des Sciences Hydrologiques, 56(2), 212–225.

    Google Scholar 

  • Kourgialas, N. N., & Karatzas, G. P. (2016). A flood risk decision making approach for Mediterranean tree crops using GIS; climate change effects and flood-tolerant species. Environmental Science & Policy, 63, 132–142.

    Google Scholar 

  • Kui, L., Stella, J. C., Lightbody, A., & Wilcox, A. C. (2014). Ecogeomorphic feedbacks and flood loss of riparian tree seedlings in meandering channel experiments. Water Resources Research, 50(12), 9366–9384.

    Google Scholar 

  • Kulkarni, A., Mohanty, J., Eldho, T., Rao, E., & Mohan, B. (2014). A web GIS based integrated flood assessment modeling tool for coastal urban watersheds. Computers & Geosciences, 64, 7–14.

    Google Scholar 

  • Lagmay, A. M. F. A., Racoma, B. A., Aracan, K. A., Alconis-Ayco, J., & Saddi, I. L. (2017). Disseminating near-real-time hazards information and flood maps in the Philippines through Web-GIS. Journal of Environmental Sciences, 59, 13–23.

    Google Scholar 

  • Lai, C., Shao, Q., Chen, X., Wang, Z., Zhou, X., Yang, B., et al. (2016). Flood risk zoning using a rule mining based on ant colony algorithm. Journal of Hydrology, 542, 268–280.

    Google Scholar 

  • Li, P., Wu, J., & Qian, H. (2012). Groundwater quality assessment with two multi-criteria decision making methods. International Journal of Geomatics and Geosciences, 2(3), 868.

    Google Scholar 

  • Liu, Y., Gebremeskel, S., De Smedt, F., Hoffmann, L., & Pfister, L. (2003). A diffusive transport approach for flow routing in GIS-based flood modeling. Journal of Hydrology, 283(1), 91–106.

    Google Scholar 

  • Liu, H.-C., Liu, L., & Wu, J. (2013). Material selection using an interval 2-tuple linguistic VIKOR method considering subjective and objective weights. Materials and Design, 52, 158–167.

    Google Scholar 

  • Luu, C., Von Meding, J., & Kanjanabootra, S. (2018). Assessing flood hazard using flood marks and analytic hierarchy process approach: A case study for the 2013 flood event in Quang Nam, Vietnam. Natural Hazards, 90(3), 1031–1050.

    Google Scholar 

  • Mahmoudi, B., Marvie Mohadjer, M., Danehkar, A., & Feghhi, J. (2014). Estimating of forest cover changes in topographic zones in Mazandaran Province. Journal of Natural Environment, 67(3), 333–341.

    Google Scholar 

  • Malczewski, J. (2004). GIS-based land-use suitability analysis: A critical overview. Progress in Planning, 62(1), 3–65.

    Google Scholar 

  • Malczewski, J. (2006). Ordered weighted averaging with fuzzy quantifiers: GIS-based multicriteria evaluation for land-use suitability analysis. International Journal of Applied Earth Observation and Geoinformation, 8(4), 270–277. https://doi.org/10.1016/j.jag.2006.01.003.

    Article  Google Scholar 

  • Malczewski, J., & Rinner, C. (2015). Multicriteria decision analysis in geographic information science. Berlin: Springer.

    Google Scholar 

  • Ministry of Energy. (2015). The evaluation of flood damages. http://shaghool.ir/Files/164-n.pdf. Accessed 15 Sept 2018.

  • Mojaddadi, H., Pradhan, B., Nampak, H., Ahmad, N., & Ghazali, A. H. B. (2017). Ensemble machine-learning-based geospatial approach for flood risk assessment using multi-sensor remote-sensing data and GIS. Geomatics, Natural Hazards and Risk, 8(2), 1080–1102.

    Google Scholar 

  • Moore, I. D., Grayson, R., & Ladson, A. (1991). Digital terrain modelling: A review of hydrological, geomorphological, and biological applications. Hydrological Processes, 5(1), 3–30.

    Google Scholar 

  • Moradnezhadi, M., Jourgholami, M., & Malekian, A. (2015). Prioritization of appropriate sub-basins in order to forest harvesting using HEC-HMS model (case study: Kheyrud Forest). Journal of forest and wood products (JFWP), 68(2), 405–418.

    Google Scholar 

  • Mukherjee, F., & Singh, D. (2019). Detecting flood prone areas in Harris County: A GIS based analysis. GeoJournal. https://doi.org/10.1007/s10708-019-09984-2.

    Article  Google Scholar 

  • Omidipoor, M., Jelokhani-Niaraki, M., Moeinmehr, A., Sadeghi-Niaraki, A., & Choi, S. M. (2019b). A GIS-based decision support system for facilitating participatory urban renewal process. Land Use Policy, 88, 104150.

    Google Scholar 

  • Omidipoor, M., Jelokhani-Niaraki, M., & Samany, N. N. (2019a). A Web-based geo-marketing decision support system for land selection: A case study of Tehran, Iran. Annals of GIS, 1–15.

  • Prime, T., Brown, J. M., & Plater, A. J. (2016). Flood inundation uncertainty: The case of a 0.5% annual probability flood event. Environmental Science & Policy, 59, 1–9.

    Google Scholar 

  • Rahman, M. A., Rusteberg, B., Gogu, R., Ferreira, J. L., & Sauter, M. (2012). A new spatial multi-criteria decision support tool for site selection for implementation of managed aquifer recharge. Journal of Environmental Management, 99, 61–75.

    Google Scholar 

  • Rahmani, M., Oboori, M., & Molaaghajanzade, S. (2015). Flood risk zoning by using overlapping methods of weighted linear combination (WLC) and ordered weighted average (OWA) scenarios: Case study on Sari, Mazandaran, Iran. In Paper presented at the international conference on modern research in agriculture science and environment, Malayzia.

  • Rahmati, O., Haghizadeh, A., & Stefanidis, S. (2016a). Assessing the accuracy of GIS-based analytical hierarchy process for watershed prioritization; Gorganrood River Basin, Iran. Water Resources Management, 30(3), 1131–1150.

    Google Scholar 

  • Rahmati, O., Pourghasemi, H. R., & Zeinivand, H. (2016b). Flood susceptibility mapping using frequency ratio and weights-of-evidence models in the Golastan Province, Iran. Geocarto International, 31(1), 42–70.

    Google Scholar 

  • Rashed, T., & Weeks, J. (2003). Assessing vulnerability to earthquake hazards through spatial multicriteria analysis of urban areas. International Journal of Geographical Information Science, 17(6), 547–576.

    Google Scholar 

  • Rozalis, S., Morin, E., Yair, Y., & Price, C. (2010). Flash flood prediction using an uncalibrated hydrological model and radar rainfall data in a Mediterranean watershed under changing hydrological conditions. Journal of Hydrology, 394(1), 245–255.

    Google Scholar 

  • Saaty, T. L. (1990). Decision making for leaders: The analytic hierarchy process for decisions in a complex world. Pittsburgh: RWS publications.

    Google Scholar 

  • Sadeghi-Pouya, A., Nouri, J., Mansouri, N., & Kia-Lashaki, A. (2017). An indexing approach to assess flood vulnerability in the western coastal cities of Mazandaran, Iran. International Journal of Disaster Risk Reduction, 22, 304–316.

    Google Scholar 

  • Sahoo, M., Sahoo, S., Dhar, A., & Pradhan, B. (2016). Effectiveness evaluation of objective and subjective weighting methods for aquifer vulnerability assessment in urban context. Journal of Hydrology, 541, 1303–1315.

    Google Scholar 

  • Sarhadi, A., Soltani, S., & Modarres, R. (2012). Probabilistic flood inundation mapping of ungauged rivers: Linking GIS techniques and frequency analysis. Journal of Hydrology, 458, 68–86.

    Google Scholar 

  • Shannon, C., & Weaver, W. (1947). The math theory of communica. Urbana: The University of Illinois Press.

    Google Scholar 

  • Shooshtari, S. J., & Gholamalifard, M. (2015). Scenario-based land cover change modeling and its implications for landscape pattern analysis in the Neka Watershed, Iran. Remote Sensing Applications: Society and Environment, 1, 1–19.

    Google Scholar 

  • Shorabeh, S. N., Firozjaei, M. K., Nematollahi, O., Firozjaei, H. K., & Jelokhani-Niaraki, M. (2019). A risk-based multi-criteria spatial decision analysis for solar power plant site selection in different climates: A case study in Iran. Renewable Energy, 143, 958–973.

    Google Scholar 

  • Siahkamari, S., Haghizadeh, A., Zeinivand, H., Tahmasebipour, N., & Rahmati, O. (2018). Spatial prediction of flood-susceptible areas using frequency ratio and maximum entropy models. Geocarto international, 33(9), 927–941.

    Google Scholar 

  • Skilodimou, H. D., Bathrellos, G. D., Chousianitis, K., Youssef, A. M., & Pradhan, B. (2019). Multi-hazard assessment modeling via multi-criteria analysis and GIS: A case study. Environmental Earth Sciences, 78(2), 47.

    Google Scholar 

  • Tehrany, M. S., Pradhan, B., & Jebur, M. N. (2013). Spatial prediction of flood susceptible areas using rule based decision tree (DT) and a novel ensemble bivariate and multivariate statistical models in GIS. Journal of Hydrology, 504, 69–79.

    Google Scholar 

  • 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. Journal of Hydrology, 512, 332–343.

    Google Scholar 

  • Tehrany, M. S., Pradhan, B., Mansor, S., & Ahmad, N. (2015). Flood susceptibility assessment using GIS-based support vector machine model with different kernel types. CATENA, 125, 91–101.

    Google Scholar 

  • Tucker, C. J. (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8(2), 127–150.

    Google Scholar 

  • Van Westen, C. (1997). Statistical landslide hazards analysis, ILWIS 2.1 for Windows application guide. Enschede: ITC Publication.

    Google Scholar 

  • Walke, N., Obi Reddy, G. P., Maji, A. K., & Thayalan, S. (2012). GIS-based multicriteria overlay analysis in soil-suitability evaluation for cotton (Gossypium spp.): A case study in the black soil region of Central India. Computers & Geosciences, 41, 108–118. https://doi.org/10.1016/j.cageo.2011.08.020.

    Article  Google Scholar 

  • Wang, T.-C., & Lee, H.-D. (2009). Developing a fuzzy TOPSIS approach based on subjective weights and objective weights. Expert Systems with Applications, 36(5), 8980–8985.

    Google Scholar 

  • Yager, R. R. (1988). On ordered weighted averaging aggregation operators in multicriteria decisionmaking. IEEE Transactions on Systems, Man and Cybernetics, 18(1), 183–190. https://doi.org/10.1109/21.87068.

    Article  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. Environmental Modelling and Software, 49, 152–165.

    Google Scholar 

  • Zheng, N., Takara, K., Yamashiki, Y., & Tachikawa, Y. (2009). Assessing vulnerability to regional flood hazard through spatial multi-criteria analysis in the Huaihe River Basin, China. Annual Journal of Hydraulic Engineering, 53, 127–132.

    Google Scholar 

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Acknowledgements

This research was supported by University of Tehran.

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  1. Department of Remote sensing and GIS, Faculty of Geography, University of Tehran, Tehran, Iran

    Narjes Mahmoody Vanolya & Mohammadreza Jelokhani-Niaraki

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  1. Narjes Mahmoody Vanolya
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Mahmoody Vanolya, N., Jelokhani-Niaraki, M. The use of subjective–objective weights in GIS-based multi-criteria decision analysis for flood hazard assessment: a case study in Mazandaran, Iran. GeoJournal 86, 379–398 (2021). https://doi.org/10.1007/s10708-019-10075-5

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