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Research Articles

National-scale flood risk assessment using GIS and remote sensing-based hybridized deep neural network and fuzzy analytic hierarchy process models: a case of Bangladesh

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Pages 12119-12148 | Received 03 Dec 2021, Accepted 03 Apr 2022, Published online: 25 Apr 2022

References

  • Adnan M, Abdullah A, Dewan A, Hall JW. 2020a. The effects of changing land use and flood hazard on poverty in coastal Bangladesh. Land Use Policy. 99:104868.
  • Adnan M, Dewan A, Zannat KE, Abdullah A. 2019a. The use of watershed geomorphic data in flash flood susceptibility zoning: a case study of the Karnaphuli and Sangu river basins of Bangladesh. Nat Hazards. 99(1):425–448.
  • Adnan M, Haque A, Hall JW. 2019b. Have coastal embankments reduced flooding in Bangladesh? Sci Total Environ. 682:405–416.
  • Adnan M, Talchabhadel R, Nakagawa H, Hall JW. 2020b. The potential of tidal river management for flood alleviation in south western Bangladesh. Sci Total Environ. 731:138747.
  • Ahmadlou M, Al‐Fugara AK, Al‐Shabeeb AR, Arora A, Al‐Adamat R, Pham QB, Al‐Ansari N, Linh N, Sajedi H. 2021. Flood susceptibility mapping and assessment using a novel deep learning model combining multilayer perceptron and autoencoder neural networks. J Flood Risk Manage. 14(1):e12683.
  • Akay H. 2021. Flood hazards susceptibility mapping using statistical, fuzzy logic, and MCDM methods. Soft Comput. 25(14):9325–9346.
  • Akay H, Baduna Koçyiğit M. 2020. Flash flood potential prioritization of sub-basins in an ungauged basin in Turkey using traditional multi-criteria decision-making methods. Soft Comput. 24(18):14251–14263.
  • Arabameri A, Rezaei K, Cerdà A, Conoscenti C, Kalantari Z. 2019. A comparison of statistical methods and multi-criteria decision making to map flood hazard susceptibility in Northern Iran. Sci Total Environ. 660:443–458.
  • Bai S, Lü G, Wang J, Zhou P, Ding L. 2011. Gis-based rare events logistic regression for landslide-susceptibility mapping of Lianyungang, China. Environ Earth Sci. 62(1):139–149.
  • Bannari A, Ghadeer A, El-Battay A, Hameed N, Rouai M. 2017. Detection of areas associated with flash floods and erosion caused by rainfall storm using topographic attributes, hydrologic indices, and GIS. In: Global changes and natural disaster management: geo-information technologies. Cham (Denmark): Springer; p. 155–174.
  • Barbour EJ, Adnan M, Borgomeo E, Paprocki K, Khan SA, Salehin M, Hall JW. 2022. The unequal distribution of water risks and adaptation benefits in coastal Bangladesh. Nat Sustain. doi:10.1038/s41893-021-00846-9
  • Barc. 2014., Land resource database. Dhaka (Bangladesh): Barc.
  • Bondarenko M, Kerr D, Sorichetta A, Tatem AJ. 2020. Estimates of 2020 total number of people per grid square, adjusted to match the corresponding unpd 2020 estimates and broken down by gender and age groupings, produced using built-settlement growth model (BSGM) outputs. UK: University of Southampton.
  • Brito M, Evers M, Almoradie A. 2018. Participatory flood vulnerability assessment: a multi-criteria approach. Hydrol Earth Syst Sci. 22(1):373–390.
  • Bui DT, Hoang N-D, Martínez-Álvarez F, Ngo P-TT, Hoa PV, Pham TD, Samui P, Costache R. 2020a. A novel deep learning neural network approach for predicting flash flood susceptibility: a case study at a high frequency tropical storm area. Sci Total Environ. 701:134413.
  • Bui DT, Ngo P-TT, Pham TD, Jaafari A, Minh NQ, Hoa PV, Samui P. 2019. A novel hybrid approach based on a swarm intelligence optimized extreme learning machine for flash flood susceptibility mapping. Catena. 179:184–196.
  • Bui Q-T, Nguyen Q-H, Nguyen XL, Pham VD, Nguyen HD, Pham V-M. 2020b. Verification of novel integrations of swarm intelligence algorithms into deep learning neural network for flood susceptibility mapping. J Hydrol. 581:124379.
  • Büyüközkan G, Feyzıog˜Lu O. 2004. A fuzzy-logic-based decision-making approach for new product development. Int J Prod Econ. 90(1):27–45.
  • Care. 2020. Monsoon floods 2020 - coordinated preliminary impact and needs assessment.
  • Chen J, Huang G, Chen W. 2021. Towards better flood risk management: assessing flood risk and investigating the potential mechanism based on machine learning models. J Environ Manage. 293:112810.
  • Chou TY, Hoang TV, Fang YM, Nguyen QH, Lai TA, Pham VM, Vu VM, Bui QT. 2021. Swarm‐based optimizer for convolutional neural network: an application for flood susceptibility mapping. Trans GIS. 25(2):1009–1026.
  • Chowdhury EH, Hassan QK. 2017. Use of remote sensing data in comprehending an extremely unusual flooding event over southwest Bangladesh. Nat Hazards. 88(3):1805–1823.
  • Costache R, Ali SA, Parvin F, Pham QB, Arabameri A, Nguyen H, Crăciun A, Anh DT. 2021. Detection of areas prone to flood-induced landslides risk using certainty factor and its hybridization with fahp, xgboost and deep learning neural network. Geocarto Int. 1–36. doi:10.1080/10106049.2021.1973115
  • Costache R, Pham QB, Sharifi E, Linh N, Abba SI, Vojtek M, Vojteková J, Nhi P, Khoi DN. 2020a. Flash-flood susceptibility assessment using multi-criteria decision making and machine learning supported by remote sensing and GIS techniques. Remote Sens. 12(1):106.
  • Costache R, Popa MC, Bui DT, Diaconu DC, Ciubotaru N, Minea G, Pham QB. 2020b. Spatial predicting of flood potential areas using novel hybridizations of fuzzy decision-making, bivariate statistics, and machine learning. J Hydrol. 585:124808.
  • 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.
  • David A, Schmalz B. 2020. Flood hazard analysis in small catchments: comparison of hydrological and hydrodynamic approaches by the use of direct rainfall. J Flood Risk Manage. 13(4):e12639.
  • De Moel H, Jongman B, Kreibich H, Merz B, Penning-Rowsell E, Ward PJ. 2015. Flood risk assessments at different spatial scales. Mitig Adapt Strateg Glob Chang. 20(6):865–890.
  • Dewan TH. 2015. Societal impacts and vulnerability to floods in Bangladesh and Nepal. Weather Clim Extremes. 7:36–42.
  • Dewan AM, Kankam-Yeboah K, Nishigaki M. 2006. Using synthetic aperture radar (sar) data for mapping river water flooding in an urban landscape: a case study of greater Dhaka, Bangladesh. J Japan Soc Hydrol Water Resour. 19(1):44–54.
  • Ekmekcioğlu Ö, Koc K, Özger M. 2021. District based flood risk assessment in istanbul using fuzzy analytical hierarchy process. Stoch Environ Res Risk Assess. 35(3):617–637.
  • Hasan SS, Deng X, Li Z, Chen D. 2017. Projections of future land use in bangladesh under the background of baseline, ecological protection and economic development. Sustainability. 9(4):505.
  • Huffman GJ, Stocker EF, Bolvin DT, Nelkin EJ, Tan J. 2019. Gpm imerg final precipitation l3 half hourly 0.1 degree x 0.1 degree v06, Greenbelt, MD, Goddard Earth Sciences Data and Information Services Center (GES DISC). USA: The National Aeronautics and Space Administration (NASA).
  • Islam MM, Sado K. 2000. Flood hazard assessment in Bangladesh using NOAA AVHRR data with geographical information system. Hydrol Process. 14(3):605–620.
  • Islam A, Talukdar S, Mahato S, Kundu S, Eibek KU, Pham QB, Kuriqi A, Linh N. 2021. Flood susceptibility modelling using advanced ensemble machine learning models. Geosci Front. 12(3):101075.
  • Jaxa. 2015., Alos global digital surface model “alos world 3d‐30m (aw3d30)”.
  • Karra K, Kontgis C, Statman-Weil Z, Mazzariello J, Mathis M, Brumby S. 2021. Global land use/land cover with sentinel 2 and deep learninged. IGARSS 2021–2021 IEEE International Geoscience and Remote Sensing Symposium, USA: IEEE.
  • Leon MA, Barua P, Sarker P, Kumar P, Hasan M. 2020. Annual flood report 2019.
  • Liou T-S, Wang M-JJ. 1992. Ranking fuzzy numbers with integral value. Fuzzy Sets Syst. 50(3):247–255.
  • Lu Y, Bookman R, Waldmann N, Marco S. 2020. A 45 kyr laminae record from the dead sea: implications for basin erosion and floods recurrence. Quat Sci Rev. 229:106143.
  • Luu C, Von Meding J, Mojtahedi M. 2019. Analyzing Vietnam's national disaster loss database for flood risk assessment using multiple linear regression-topsis. Int J Disaster Risk Reduct. 40:101153.
  • Ma L, Liu Y, Zhang X, Ye Y, Yin G, Johnson BA. 2019a. Deep learning in remote sensing applications: a meta-analysis and review. ISPRS J Photogramm Remote Sens. 152:166–177.
  • Ma M, Liu C, Zhao G, Xie H, Jia P, Wang D, Wang H, Hong Y. 2019b. Flash flood risk analysis based on machine learning techniques in the Yunnan Province, China. Remote Sens. 11(2):170.
  • Meyer V, Scheuer S, Haase D. 2009. A multicriteria approach for flood risk mapping exemplified at the mulde river, germany. Nat Hazards. 48(1):17–39.
  • Midi H, Sarkar SK, Rana S. 2010. Collinearity diagnostics of binary logistic regression model. J Interdiscip Math. 13(3):253–267.
  • Mojaddadi H, Pradhan B, Nampak H, Ahmad N, Ghazali A. 2017. Ensemble machine-learning-based geospatial approach for flood risk assessment using multi-sensor remote-sensing data and GIS. Geomatics Nat Hazards Risk. 8(2):1080–1102.
  • Nasa. 2020. Intense flooding in bangladesh. USA: The National Aeronautics and Space Administration (NASA).
  • Ngo P-TT, Pham TD, Hoang N-D, Tran DA, Amiri M, Le TT, Hoa PV, Van Bui P, Nhu V-H, Bui DT. 2021. A new hybrid equilibrium optimized sysfor based geospatial data mining for tropical storm-induced flash flood susceptible mapping. J Environ Manage. 280:111858.
  • Nguyen HD, Nguyen Q-H, Du Q, Nguyen T, Nguyen TG, Bui Q-T. 2021. A novel combination of deep neural network and manta ray foraging optimization for flood susceptibility mapping in Quang Ngai Province, Vietnam. Geocarto Int. 1–25.
  • Panahi M, Jaafari A, Shirzadi A, Shahabi H, Rahmati O, Omidvar E, Lee S, Bui DT. 2021. Deep learning neural networks for spatially explicit prediction of flash flood probability. Geosci Front. 12(3):101076.
  • Pappenberger F, Matgen P, Beven KJ, Henry J-B, Pfister L, Fraipont P. 2006. Influence of uncertain boundary conditions and model structure on flood inundation predictions. Adv Water Resour. 29(10):1430–1449.
  • Paul GC, Saha S, Hembram TK. 2019. Application of the GIS-based probabilistic models for mapping the flood susceptibility in Bansloi sub-basin of Ganga-Bhagirathi River and their comparison. Remote Sens Earth Syst Sci. 2(2–3):120–146.
  • Persits FM, Wandrey CJ, Milici RC, Manwar A. 2001. Digital geologic and geophysical data of Bangladesh: U.S. Geological survey open-file report 97-470-h. U.S. Geological Survey.
  • Pham BT, Luu C, Van Dao D, Van Phong T, Nguyen HD, Van Le H, Von Meding J, Prakash I. 2021a. Flood risk assessment using deep learning integrated with multi-criteria decision analysis. Knowledge-Based Syst. 219:106899.
  • Pham BT, Luu C, Van Phong T, Nguyen HD, Van Le H, Tran TQ, Ta HT, Prakash I. 2021b. Flood risk assessment using hybrid artificial intelligence models integrated with multi-criteria decision analysis in Quang Nam Province, Vietnam. J Hydrol. 592:125815.
  • Planchon O, Darboux F. 2002. A fast, simple and versatile algorithm to fill the depressions of digital elevation models. Catena. 46(2–3):159–176.
  • Pradhan B. 2010. Flood susceptible mapping and risk area delineation using logistic regression, GIS and remote sensing. J Spat Hydrol. 9(2):1–18.
  • Rahman M, Chen N, Elbeltagi A, Islam MM, Alam M, Pourghasemi HR, Tao W, Zhang J, Shufeng T, Faiz H, et al. 2021a. Application of stacking hybrid machine learning algorithms in delineating multi-type flooding in Bangladesh. J Environ Manage. 295:113086.
  • Rahman M, Ningsheng C, Islam MM, Dewan A, Iqbal J, Washakh R, Shufeng T. 2019. Flood susceptibility assessment in bangladesh using machine learning and multi-criteria decision analysis. Earth Syst Environ. 3(3):585–601.
  • Rahman M, Ningsheng C, Mahmud GI, Islam MM, Pourghasemi HR, Ahmad H, Habumugisha JM, Washakh RMA, Alam M, Liu E, et al. 2021b. Flooding and its relationship with land cover change, population growth, and road density. Geosci Front. 12(6):101224.
  • Rahmati O, Darabi H, Panahi M, Kalantari Z, Naghibi SA, Ferreira CSS, Kornejady A, Karimidastenaei Z, Mohammadi F, Stefanidis S, et al. 2020. Development of novel hybridized models for urban flood susceptibility mapping. Sci Rep. 10(1):1–19.
  • Rincón D, Khan UT, Armenakis C. 2018. Flood risk mapping using gis and multi-criteria analysis: a greater toronto area case study. Geosciences. 8(8):275. https://www.mdpi.com/2076-3263/8/8/275.
  • Ronco P, Bullo M, Torresan S, Critto A, Olschewski R, Zappa M, Marcomini A. 2015. Kulturisk regional risk assessment methodology for water-related natural hazards–part 2: application to the Zurich case study. Hydrol Earth Syst Sci. 19(3):1561–1576.
  • Saaty TL, Tran LT. 2007. On the invalidity of fuzzifying numerical judgments in the analytic hierarchy process. Math Comput Modell. 46(7–8):962–975.
  • Sarkar D, Mondal P. 2020. Flood vulnerability mapping using frequency ratio (fr) model: a case study on Kulik River basin, Indo-Bangladesh Barind region. Appl Water Sci. 10(1):1–13.
  • Siam ZS, Hasan RT, Anik SS, Noor F, Adnan M, Rahman RM. 2021a. Study of hybridized support vector regression based flood susceptibility mapping for Bangladeshed. International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems. Springer; p. 59–71.
  • Siam ZS, Hasan RT, Rahman RM. 2021b. Effects of label noise on regression performances and model complexities for hybridized machine learning based spatial flood susceptibility modelling. Cybern Syst. 53(4):362–379.
  • Singh VK, Kumar D, Kashyap P, Singh PK, Kumar A, Singh SK. 2020. Modelling of soil permeability using different data driven algorithms based on physical properties of soil. J Hydrol. 580:124223.
  • Steele JE, Sundsøy PR, Pezzulo C, Alegana VA, Bird TJ, Blumenstock J, Bjelland J, Engø-Monsen K, de Montjoye Y-A, Iqbal AM, et al. 2017. Mapping poverty using mobile phone and satellite data. J R Soc Interface. 14(127):20160690.
  • Stefanidis S, Stathis D. 2013. Assessment of flood hazard based on natural and anthropogenic factors using analytic hierarchy process (AHP). Nat Hazards. 68(2):569–585.
  • Talukdar S, Ghose B, Salam R, Mahato S, Pham QB, Linh N, Costache R, Avand M. 2020. Flood susceptibility modeling in teesta river basin, bangladesh using novel ensembles of bagging algorithms. Stochastic Environ Res Risk Assess. 34(12):2277–2300.
  • Tehrany MS, 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.
  • Tehrany MS, Shabani F, Neamah Jebur M, Hong H, Chen W, Xie X. 2017. Gis-based spatial prediction of flood prone areas using standalone frequency ratio, logistic regression, weight of evidence and their ensemble techniques. Geomatics Nat Hazards Risk. 8(2):1538–1561.
  • Thirumurugan P, Krishnaveni M. 2019. Flood hazard mapping using geospatial techniques and satellite images—a case study of coastal district of Tamil Nadu. Environ Monit Assess. 191(3):1–17.
  • Unitar. 2020. Satellite detected water extent in Bangladesh. Geneva (Switzerland): United Nations Institute for Training and Research (UNITAR).
  • Vilasan R, Kapse VS. 2021. Evaluation of the prediction capability of AHP and F_AHP methods in flood susceptibility mapping of Ernakulam district (India).
  • Wang Y, Fang Z, Hong H, Peng L. 2020. Flood susceptibility mapping using convolutional neural network frameworks. J Hydrol. 582:124482.
  • 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.
  • Wang Y, Li Z, Tang Z, Zeng G. 2011. A gis-based spatial multi-criteria approach for flood risk assessment in the dongting lake region, hunan, central china. Water Resour Manage. 25(13):3465–3484.
  • Warpo. 2018. National Water Resources Database (NWRD). Dhaka, Bangladesh: Water Resources Planning Organization (WARPO).
  • Worldpop. 2020. The spatial distribution of population in 2020, Bangladesh. University of Southampton, UK.
  • Youden WJ. 1950. Index for rating diagnostic tests. Cancer. 3(1):32–35.
  • Zadeh LA. 1996. Fuzzy sets. Fuzzy sets, fuzzy logic, and fuzzy systems: selected papers by lotfi a zadeh. Singapore: World Scientific; p. 394–432.
  • Zou Q, Zhou J, Zhou C, Song L, Guo J. 2013. Comprehensive flood risk assessment based on set pair analysis-variable fuzzy sets model and fuzzy AHP. Stoch Environ Res Risk Assess. 27(2):525–546.

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