Abstract
Iran is one of the flood-prone areas in the world with inappropriate climatic patterns. In this study, flood risk maps in three scenarios by combining Analytic Hierarchy Process (AHP), Analytical Network Process (ANP) and Fuzzy Analytic Hierarchy Process (FAHP) models with Ordered Weighted Average (OWA), Weighted Linear Combination (WLC) models., Local Weighted Linear Combination (LWLC) and two new models, Weighted Multi-Criteria Analysis (WMCA) and Geo Technique for Order of Preference by Similarity to Ideal Solution (Geo TOPSIS) were prepared from Heraz watershed in northern Iran. The analysis of the results of the AHP model in the first scenario, the ANP model in the second scenario and the FAHP model in the third scenario show that the criteria of precipitation, slope, land use, elevation, drainage density and distance to river are the most important criteria for the occurrence of floods in Haraz basin. Evaluation of flood risk models show that on average, about 70%, 20%, 8%, and 2% of Haraz basin are in medium, low, high, and no flood risk situations, respectively. Geographically, the southeastern and central parts are in high and low flood risk, respectively, and other parts of the basin are in medium risk. In this basin, many forest lands, pastures, agriculture and population centers are in medium and high risk of flooding. Generally, based on the obtained results, WMCA and Geo TOPSIS models along with WLC, LWLC and OWA models are effective methods for flood risk studies and based on the obtained results, Haraz basin needs necessary planning for flood risk management.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data Availability
Data associated with recent research will be available from the corresponding author upon request.
References
Afsari R, NadizadehShorabeh S, Kouhnavard M, Homaee M, JokarArsanjani J (2022) A spatial decision support approach for flood vulnerability analysis in urban areas: a case study of Tehran. ISPRS Int J Geo-Inf 11(7):380. https://doi.org/10.3390/ijgi11070380
Akay H (2021) Flood hazards susceptibility mapping using statistical, fuzzy logic, and MCDM methods. Soft Comput 25:9325–9346. https://doi.org/10.1007/s00500-021-05903-1
Arabameri A, Pourghasemi HR, Cerda A (2018) Erodibility prioritization of sub-watersheds using morphometric parameters analysis and its mapping: a comparison among TOPSIS VIKOR, SAW, and CF multi-criteria decision making models. SciTotal Environ 613–614:1385–1400. https://doi.org/10.1016/j.scitotenv.2017.09.210
Amini S, Bidaki RZ, Mirabbasi R, Shafaei M (2022) Flood risk analysis based on nested copula structure in Armand Basin, Iran. Acta Geophys 70:1385–1399. https://doi.org/10.1007/s11600-022-00766-y
Arabameri A, Saha S, Mukherjee K, Blaschke T, Chen W, Thi Ngo PT, Band ShS (2020) Modeling spatial flood using novel ensemble artificial intelligence approaches in Northern Iran. Remote Sens 12(20):3423. https://doi.org/10.3390/rs12203423
Behzadian M, Otaghsara SKh, Yazdani M, Ignatius J (2012) A state-of the-art survey of TOPSIS applications. Expert Syst Appl 39(17):13051–13069. https://doi.org/10.1016/j.eswa.2012.05.056
Carter B, Rinner C (2014) Locally weighted linear combination in a vector geographic information system. J Geograph Syst 16:343–361. https://doi.org/10.1007/s10109-013-0194-3
Chauhan P, Chauniyal DD, Singh N, Tiwari RK (2016) Quantitative geo-morphometric and land cover-based micro-watershed prioritization in the Tons river basin of the lesser Himalaya. Environ Earth Sci 75:498. https://doi.org/10.1007/s12665-016-5342-x
Chen Y (2022) Flood hazard zone mapping incorporating geographic information system (GIS) and multi-criteria analysis (MCA) techniques. J Hydrol 612(part C):128268. https://doi.org/10.1016/j.jhydrol.2022.128268
Costache R, Arabameri A, Costache I, Crăciun A, Pham BT (2022) New machine learning ensemble for flood susceptibility estimation, new machine learning ensemble for flood susceptibility estimation. Water Resour Manage. https://doi.org/10.1007/s11269-022-03276-0
Dahri N, Yousfi R, Bouamrane A, Abida H, Pham QB, Derdous O (2022) Comparison of analytic network process and artificial neural network models for flash flood susceptibility assessment. J Afr Earth Sc 193:104576. https://doi.org/10.1016/j.jafrearsci.2022.104576
Dandapat K, Panda GK (2017) Flood vulnerability analysis and risk assessment using analytical hierarchy process. Model Earth Syst Environ 3:1627–1646. https://doi.org/10.1007/s40808-017-0388-7
Das S, Angadi DP (2022) Land use land cover change detection and monitoring of urban growth using remote sensing and GIS techniques: a micro-level study. GeoJournal 87:2101–2123. https://doi.org/10.1007/s10708-020-10359-1
Doorga JRS, Magerl L, Bunwaree P, Zhao J, Watkins S, Staub CG, Rughooputh SDDV, Cunden TSM, Lollchund R, Boojhawon R (2022) GIS-based multi-criteria modelling of flood risk susceptibility in Port Louis, Mauritius: towards resilient flood management. Int J Disaster Risk Reduct 67:102683. https://doi.org/10.1016/j.ijdrr.2021.102683
Duan Y, Xiong J, Cheng W, Wang N, Li Y, He Y, Liu J, He W, Yang G (2022) Flood vulnerability assessment using the triangular fuzzy number-based analytic hierarchy process and support vector machine model for the Belt and Road region. Nat Hazards 110:269–294. https://doi.org/10.1007/s11069-021-04946-9
Ebrahimian Ghajari Y, Alesheikh AA, Modiri M, Hosnavi R, Abbasi M (2017) Spatial modelling of urban physical vulnerability to explosion hazards using GIS and fuzzy MCDA. Sustainability 9(7):1274. https://doi.org/10.3390/su9071274
Fernandez P, Mourato S, Madalena M (2016) Social vulnerability assessment of flood risk using GIS-based multicriteria decision analysis. A case study of Vila Nova de Gaia (Portugal). Geomat Nat Hazards Risk 7(4):1367–1389. https://doi.org/10.1080/19475705.2015.1052021
Ferretti V, Pomarico S (2013) Ecological land suitability analysis through spatial indicators: An application of the Analytic Network Process technique and Ordered Weighted Average approach. Ecol Ind 34:507–519. https://doi.org/10.1016/j.ecolind.2013.06.005
Google earth (2020) Google earth images. https://earth.google.com (Accessed 15 Jun 2020)
Ghosh P, Lepcha K (2019) Weighted linear combination method versus grid based overlay operation method – A study for potential soil erosion susceptibility analysis of Malda district (West Bengal) in India. Egypt J Remote Sens Space Sci 22(1):95–115. https://doi.org/10.1016/j.ejrs.2018.07.002
Hidayah E, Halik G, Indarto I, Khaulan DW (2022) Flood hazard mapping of the Welang river, Pasuruan, East Java, Indonesia. J Appl Water Eng Res. https://doi.org/10.1080/23249676.2022.2114025
Hirabayashi Y, Mahendran R, Koirala S, Konoshima L, Yamazaki D, Watanabe S, Kim H, Kanae S (2013) Global flood risk under climate change. Nat Clim Chang 3:816–821. https://doi.org/10.1038/nclimate1911
Hwang CL, Yoon K (1981) Multiple Attribute Decision Making, 1st Edation, Springer Berlin, Heidelberg, 269 p
Iran Meteorological Organization (2019) climatic conditions and meteorological data, www.irimo.ir. (Accessed 26 Apr 2020)
Jang JH, Vohnicky P, Kuo YL (2021) Improvement of flood risk analysis via downscaling of hazard and vulnerability maps. Water Resour Manage 35:2215–2230. https://doi.org/10.1007/s11269-021-02836-0
Jensen JR (2015) Introductory digital image processing: a remote sensing perspective, 4th edn. Pearson Publisher, London, p 656
Khosravi Kh, Panahi M, Golkarian A, Keesstra SD, Saco PM, Bui DT, Lee S (2020) Convolutional neural network approach for spatial prediction of flood hazard at national scale of Iran. J Hydrol 591:125552. https://doi.org/10.1016/j.jhydrol.2020.125552
Li N, Guo Sh, Xiong F, Wang J, Xie Y (2022) Comparative study of flood coincidence risk estimation methods in the mainstream and its tributaries. Water Resour Manage 36:683–698. https://doi.org/10.1007/s11269-021-03050-8
Li H, Ma Z, Zhu Y, Liu Y, Yang X (2020) Planning and prioritizing forest landscape restoration within megacities using the ordered weighted averaging operator. Ecol Ind 116:1–12. https://doi.org/10.1016/j.ecolind.2020.106499
Mahmoody Vanolya N, Jelokhani-Niaraki M (2021) 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. https://doi.org/10.1007/s10708-019-10075-5
Malczewski J, Liu X (2014) Local ordered weighted averaging in GIS-based multicriteria analysis. Ann GIS 20(2):117–129. https://doi.org/10.1080/19475683.2014.904439
Malczewski J (2011) Local weighted linear combination. Trans GIS 15(4):439–455. https://doi.org/10.1111/j.1467-9671.2011.01275.x
Malczewski J (2006) Ordered weighted averaging with fuzzy quantifiers: GIS-based multicriteria evaluation for land-use suitability analysis. Int J Appl Earth Obs Geoinf 8(4):270–277. https://doi.org/10.1016/j.jag.2006.01.003
Mubeen A, Ruangpan L, Vojinovic Z, Torrez AS, Plavšić J (2021) Planning and Suitability assessment of large-scale nature-based solutions for flood-risk reduction. Water Resour Manage 35:3063–3081. https://doi.org/10.1007/s11269-021-02848-w
Mudashiru RB, Sabtu N, Abdullah A, Saleh A, Abustan I (2022) Optimality of flood influencing factors for flood hazard mapping: an evaluation of two multi-criteria decision-making methods. J Hydrol 612:128055. https://doi.org/10.1016/j.jhydrol.2022.128055
Msabi MM, Makonyo M (2021) Flood susceptibility mapping using GIS and multi-criteria decision analysis: a case of Dodoma region, central Tanzania. Remote Sens Appl: Soc Environ 21:1–28. https://doi.org/10.1016/j.rsase.2020.100445
Najafabadi RM, Ramesht MH, Ghazi I, Khajedin SJ, Seif A, Nohegar A, Mahdavi A (2016) Identification of natural hazards and classification of urban areas by TOPSIS model (case study: Bandar Abbas city, Iran). Geomat Nat Haz Risk 7(1):85–100. https://doi.org/10.1080/19475705.2013.871353
Natural Resources and Watershed Management of Mazandaran Province (2020) Ancillary data. https://sari.frw.ir (Accessed 10 Jun 2020)
Nogherotto R, Fantini A, Raffaele F, Sante FD, Dottori F, Coppola E, Giorgi F (2022) A combined hydrological and hydraulic modelling approach for the flood hazard mapping of the Po river basin. Flood Risk Manag 15(1):e12755. https://doi.org/10.1111/jfr3.12755
Ogato GSh, Bantider A, Abebe K, Geneletti D (2020) Geographic information system (GIS)-Based multicriteria analysis of flooding hazard and risk in Ambo Town and its watershed, West shoa zone, oromia regional State, Ethiopia. J Hydrol: Reg Stud 27:100659. https://doi.org/10.1016/j.ejrh.2019.100659
Pal S, Ziaul S (2017) Detection of land use and land cover change and land surface temperature in English Bazar urban centre. Egypt J Remote Sens Space Sci 20(1):125–145. https://doi.org/10.1016/j.ejrs.2016.11.003
Pathan AI, Girish Agnihotri P, Said S, Patel D (2022) AHP and TOPSIS based flood risk assessment- a case study of the Navsari City, Gujarat, India. Environ Monit Assess 194:509. https://doi.org/10.1007/s10661-022-10111-x
Pirnia A, Darabi H, Choubin B, Omidvar E, Onyutha Ch, Torabi Haghighi A (2019) Contribution of climatic variability and human activities to stream flow changes in the Haraz River basin, northern Iran. J Hydro-Environ Res 25:12–24. https://doi.org/10.1016/j.jher.2019.05.001
QGIS Python Plugins Repository (2020) Weighted Multi-Criteria Analysis–WMCA and Vector MCDA plugins. https://plugins.qgis.org/plugins. (Accessed 27 Jul 2020)
Rajabi M, Nahavandchi H, Hoseini M (2020) Evaluation of CYGNSS observations for flood detection and mapping during sistan and baluchestan torrential rain in 2020. Water 12(7):2047. https://doi.org/10.3390/w12072047
Rinner C, Malczewski J (2002) Web-enabled spatial decision analysis using Ordered Weighted Averaging (OWA). J Geograph Syst 4:385–403. https://doi.org/10.1007/s101090300095
Sadeghi-Pouya A, Nouri J, Mansouri N, Kia-Lashaki A (2016) An indexing approach to assess flood vulnerability in the western coastal cities of Mazandaran, Iran. Int J Disaster Risk Reduct 22:304–316. https://doi.org/10.1016/j.ijdrr.2017.02.013
Saha S, Sarkar D, Mondal P (2022) Efficiency exploration of frequency ratio, entropy and weights of evidence-information value models in flood vulnerabilityassessment: a study of raiganj subdivision, Eastern India. Stoch Environ Res Risk Assess 36:1721–1742. https://doi.org/10.1007/s00477-021-02115-9
Shahabi H, Shirzadi A, Ronoud S, Asadi Sh, Pham BT, Mansouripour F, Geertsema M, Clague JJ, Bui DT (2021) Flash flood susceptibility mapping using a novel deep learning model based on deep belief network, back propagation and genetic algorithm. Geosci Front 12(3):1–23. https://doi.org/10.1016/j.gsf.2020.10.007
Sharifi A (2020) Flood mapping using relevance vector machine and SAR data: a case study from Aqqala, Iran. J Indian Soc Remote Sens 48:1289–1296. https://doi.org/10.1007/s12524-020-01155-y
Shrestha BB, Perera EDP, Kudo Sh, Miyamoto M, Yamazaki Y, Kuribayashi D, Sawano H, Sayama T, Magome J, Hasegawa A, Ushiyama T, Iwami Y, Tokunaga Y (2019) Assessing flood disaster impacts in agriculture under climate change in the river basins of Southeast Asia. Nat Hazards 97:157–192. https://doi.org/10.1007/s11069-019-03632-1
Silva LBL, Alencar MH, Almeida AT (2022) A novel spatiotemporal multi-attribute method for assessing flood risks in urban spaces under climate change and demographic scenarios. Sustain Cities Soc 76:103501. https://doi.org/10.1016/j.scs.2021.103501
Uddin MJ, Hasan MM, Eisenreich SJ, Quevauviller P (2019) Strengthening pluvial flood risk management in the Southeast Region of Bangladesh: lessons learnt from the EU policy and practice. Environ Process 6:859–881. https://doi.org/10.1007/s40710-019-00393-8
U.S. Geological Survey (USGS) (2020) Landsat images and STRM DEM. https://earthexplorer.usgs.gov (Accessed 20 Jul 2020)
Vasconcellos SM, Kobiyama M, Dagostin FS, Corseuil CW, Castiglio VC (2021) Flood Hazard mapping in alluvial fans with computational modeling. Water Resour Manage 35:1463–1478. https://doi.org/10.1007/s11269-021-02794-7
Wang X, Chen G, Dai, Zhao J, Liu X, Gao Y, Zhang J, Chen Y, Li X, Qin W, Wang P (2022) Improved process management of glacial lake outburst flood hazards by integrating modular monitoring. Assessment, and Simulation, Water Resour Manage 36:2343–2358. https://doi.org/10.1007/s11269-022-03146-9
Wang SW, Munkhnasan L, Lee WK (2021) Land use and land cover change detection and prediction in Bhutan's high altitude city of Thimphu, using cellular automata and Markov chain 2:1–11. https://doi.org/10.1016/j.envc.2020.100017
Wei G, Ding W, Liang G, He B, Wu J, Zhang R, Zhou H (2022) A new framework based on data-based mechanistic model and forgetting mechanism for flood forecast. Water Resour Manage 36:3591–3607. https://doi.org/10.1007/s11269-022-03215-z
Xiao Y, Yi S, Tang ZA (2018) Spatially explicit multi-criteria analysis method on solving spatial heterogeneity problems for flood hazard assessment. Water Resour Manage 32:3317–3335. https://doi.org/10.1007/s11269-018-1993-6
Yager RR (1988) On ordered weighted averaging aggregation operators in multi criteria decision making. IEEE Trans Syst Man Cybern 18(1):183–190. https://doi.org/10.1109/21.87068
Yariyan P, Avand M, Abbaspour RA, Torabi Haghighi A, Costache R, Ghorbanzadeh O, Janizadeh S, Blaschke T (2020) Flood susceptibility mapping using an improved analytic network process with statistical models. Geomat Nat Haz Risk 11(1):2282–2314. https://doi.org/10.1080/19475705.2020.1836036
Zope PE, Eldho TI, Jothiprakash V (2016) Impacts of land use–land cover change and urbanization on flooding: a case study of Oshiwara River Basin in Mumbai, India. Catena 145:142–154. https://doi.org/10.1016/j.catena.2016.06.009
Acknowledgements
This is our pleasure to thanks Remote Sensing & GIS Centre of Sari Agric. & Natural Resources University for technical and financial support.
Author information
Authors and Affiliations
Contributions
Author 1: Karim Solaimani. Choose the title of article as a main environmental hazard in northern part of Iran. Data analysis using remote sensing data with digital image processing for flood zonation. Manuscript writing up in English language and edit it. Author 2: Fatemeh Shokrian. Providing of data from different sources e.g.; hydrometric and rainfall. Data analysis using statistical methods and process of them for flood zonation. Author 3: Shadman Darvishi. Data analysis using AHP, ANP, OWA models and process of them for flood zonation.
Corresponding author
Ethics declarations
Ethical Approval
Not applicable.
Consent to Participate
Not applicable.
Consent to Publish
Not applicable.
Competing Interests
The authors declare no competing interests.
Additional information
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.
About this article
Cite this article
Solaimani, K., Shokrian, F. & Darvishi, S. An Assessment of the Integrated Multi-Criteria and New Models Efficiency in Watershed Flood Mapping. Water Resour Manage 37, 403–425 (2023). https://doi.org/10.1007/s11269-022-03380-1
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11269-022-03380-1