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Urban flood vulnerability assessment in a densely urbanized city using multi-factor analysis and machine learning algorithms

Abstract

Flood is considered as the most devastating natural hazards that cause the death of many lives worldwide. The present study aimed to predict flood vulnerability for Warsaw, Poland, using three machine learning models, such as the Bayesian logistic regression (BLR), the artificial neural networks (ANN), and the deep learning neural networks (DLNNs). The perfomance of these three methods was assessed in order to select the best method for flood vulnerability mapping in densely urbanized city. Thus, initially, thirteen flood predictors were evaluated using the information gain ratio (IGR), and eight most important predictors were considered from model training and testing. The performance of the applied models and accuracy of the result was evaluated through the area under the curve (AUC) and statistical measures. By using the testing dataset, the result reveals that DLNN (AUC = 0.877) is the more performant model in comparison to ANN (AUC = 0.851) and BLR (AUC = 0.697). However, the BLR model has the lowest predictive capability. The results of the present study could be effectively used for the urban flood management strategies.

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Data availability

The data that support the findings of this study are available from the author [Quoc Bao Pham, quoc_bao.pham@us.edu.pl], upon reasonable request.

References

  • Abarghouei HB, Kousari MR, Zarch MAA (2013) Prediction of drought in dry lands through feedforward artificial neural network abilities. Arab J Geosci 6(5):1417–1433

    Article  Google Scholar 

  • Abedini M, Ghasemian B, Shirzadi A, Shahabi H, Chapi K, Pham BT et al (2019) A novel hybrid approach of bayesian logistic regression and its ensembles for landslide susceptibility assessment. Geocarto Int 34(13):1427–1457

    Article  Google Scholar 

  • Abu El-Magd SA, Ali SA, Pham QB (2021) Spatial modeling and susceptibility zonation of landslides using random forest, naïve bayes and K-nearest neighbor in a complicated terrain. Earth Sci Inform 14(3):1227–1243

    Article  Google Scholar 

  • Ahmadi MA (2015) Developing a robust surrogate model of chemical flooding based on the artificial neural network for enhanced oil recovery implications. Math Probl Eng 706897:1–9. https://doi.org/10.1155/2015/706897

    Article  Google Scholar 

  • Ahmadlou M, Al-Fugara AK, Al-Shabeeb AR, Arora A, Al-Adamat R, Pham QB et al (2021) Flood susceptibility mapping and assessment using a novel deep learning model combining multilayer perceptron and autoencoder neural networks. J Flood Risk Manag 14(1):e12683

    Article  Google Scholar 

  • Al-Abadi AM, Pradhan B (2020) In flood susceptibility assessment, is it scientifically correct to represent flood events as a point vector format and create flood inventory map? J Hydrol 590:125475

    Article  Google Scholar 

  • Ali M, Deo RC, Downs NJ, Maraseni T (2018) An ensemble-ANFIS based uncertainty assessment model for forecasting multi-scalar standardized precipitation index. Atmos Res 207:155–180

    Article  Google Scholar 

  • Ali SA, Khatun R, Ahmad A, Ahmad SN (2019) Application of GIS-based analytic hierarchy process and frequency ratio model to flood vulnerable mapping and risk area estimation at Sundarban region, India. Model Earth Syst Environ 5(3):1083–1102

    Article  Google Scholar 

  • Ali SA, Khatun R, Ahmad A, Ahmad SN (2020a) Assessment of cyclone vulnerability, hazard evaluation and mitigation capacity for analyzing cyclone risk using gis technique: a study on sundarban biosphere reserve, india. Earth Syst Environ 4(1):71–92

    Article  Google Scholar 

  • Ali SA, Parvin F, Pham QB, Vojtek M, Vojteková J, Costache R, Linh NTT, Nguyen HQ, Ahmad A, Ghorbani MA (2020b) GIS-based comparative assessment of flood susceptibility mapping using hybrid multi-criteria decision-making approach, naïve Bayes tree, bivariate statistics and logistic regression: a case of Topľa basin, Slovakia. Ecol Indic 117:106620

    Article  Google Scholar 

  • Ali SA, Parvin F, Vojteková J, Costache R, Linh NTT, Pham QB, Vojtek M, Gigović L, Ahmad A, Ghorbani MA (2021) GIS-based landslide susceptibility modeling: a comparison between fuzzy multi-criteria and machine learning algorithms. Geosci Front 12(2):857–876

    Article  Google Scholar 

  • Alipour A, Ahmadalipour A, Abbaszadeh P, Moradkhani H (2020) Leveraging machine learning for predicting flash flood damage in the Southeast US. Environ Res Lett 15(2):024011

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Arora A, Pandey M, Siddiqui MA, Hong H, Mishra VN (2021) Spatial flood susceptibility prediction in Middle Ganga Plain: comparison of frequency ratio and Shannon’s entropy models. Geocarto Int 36(18):2085–2116

    Article  Google Scholar 

  • Avand M, Moradi H (2021) Using machine learning models, remote sensing, and GIS to investigate the effects of changing climates and land uses on flood probability. J Hydrol 595:125663

    Article  Google Scholar 

  • Barredo JI, Engelen G (2010) Land use scenario modeling for flood risk mitigation. Sustainability 2(5):1327–1344

    Article  Google Scholar 

  • Bielecka E, Calka B, Bitner A (2018) Spatial distribution of urban greenery in Warsaw. A quantitative approach. In: Bandrova T, Konecný M (eds) Proceedings of 7th International Conference on Cartography and GIS.; Sozopol, Bulgaria, 18–23 June 2018; Bulgarian Cartographic Association: Sofia, Bulgaria, 2018; pp 408–416. Available online: https://iccgis2018.cartography-gis.com/proceedings. Accessed 25 Sept 2021

  • Biswajeet P, Mardiana S (2009) Flood hazrad assessment for cloud prone rainy areas in a typical tropical environment. Disaster Adv 2(2):7–15

    Google Scholar 

  • Borowska-Stefańska M, Kobojek S, Kowalski M, Lewicki M, Tomalski P, Wiśniewski S (2021) Changes in the spatial development of flood hazard areas in Poland between 1990 and 2018 in the light of legal conditions. Land Use Policy 102:105274

    Article  Google Scholar 

  • Bradshaw CJ, Sodhi NS, Peh KSH, Brook BW (2007) Global evidence that deforestation amplifies flood risk and severity in the developing world. Glob Chang Biol 13(11):2379–2395

    Article  Google Scholar 

  • Bubeck P, Thieken AH (2018) What helps people recover from floods? Insights from a survey among flood-affected residents in Germany. Reg Environ Chang 18(1):287–296

    Article  Google Scholar 

  • Büchele B, Kreibich H, Kron A, Thieken A, Ihringer J, Oberle P, Merz B, Nestmann F (2006) Flood-risk mapping: contributions towards an enhanced assessment of extreme events and associated risks. Nat Hazards Earth Syst Sci 6(4):485–503

    Article  Google Scholar 

  • Bui DT, Ho TC, Pradhan B, Pham BT, Nhu VH, Revhaug I (2016) GIS-based modeling of rainfall-induced landslides using data mining-based functional trees classifier with AdaBoost, Bagging, and MultiBoost ensemble frameworks. Environ Earth Sci 75(14):1–22

    Google Scholar 

  • Bui DT, Tsangaratos P, Nguyen VT, Van Liem N, Trinh PT (2020a) Comparing the prediction performance of a deep learning neural network model with conventional machine learning models in landslide susceptibility assessment. Catena 188:104426

    Article  Google Scholar 

  • Bui DT, Hoang ND, Martínez-Álvarez F, Ngo PTT, Hoa PV, Pham TD et al (2020b) 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

    Article  Google Scholar 

  • Capon SJ (2005) Flood variability and spatial variation in plant community composition and structure on a large arid floodplain. J Arid Environ 60(2):283–302

    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 

  • Chaplot V, Poesen J (2012) Sediment, soil organic carbon and runoff delivery at various spatial scales. Catena 88(1):46–56

    Article  Google Scholar 

  • Chen J, Li Q, Wang H, Deng M (2020) A machine learning ensemble approach based on random forest and radial basis function neural network for risk evaluation of regional flood disaster: a case study of the Yangtze River Delta, China. Int J Environ Res Public Health 17(1):49

    Article  Google Scholar 

  • Chowdhuri I, Pal SC, Chakrabortty R (2020) Flood susceptibility mapping by ensemble evidential belief function and binomial logistic regression model on river basin of eastern India. Adv Space Res 65(5):1466–1489

    Article  Google Scholar 

  • Costache R, Zaharia L (2017) Flash-flood potential assessment and mapping by integrating the weights-of-evidence and frequency ratio statistical methods in GIS environment–case study: Bâsca Chiojdului River catchment (Romania). J Earth Syst Sci 126(4):1–19

    Article  Google Scholar 

  • Costache R, Țîncu R, Elkhrachy I, Pham QB, Popa MC, Diaconu DC et al (2020) New neural fuzzy-based machine learning ensemble for enhancing the prediction accuracy of flood susceptibility mapping. Hydrol Sci J 65(16):2816–2837

    Article  Google Scholar 

  • Costache R, Arabameri A, Blaschke T, Pham QB, Pham BT, Pandey M et al (2021) Flash-flood potential mapping using deep learning, alternating decision trees and data provided by remote sensing sensors. Sensors 21(1):280

    Article  Google Scholar 

  • Cyberski J, Grześ M, Gutry-Korycka M, Nachlik E, Kundzewicz ZW (2006) History of floods on the River Vistula. Hydrol Sci J 51(5):799–817

    Article  Google Scholar 

  • Dai J, Xu Q (2013) Attribute selection based on information gain ratio in fuzzy rough set theory with application to tumor classification. Appl Soft Comput 13(1):211–221

    Article  Google Scholar 

  • Dang NM, Babel MS, Luong HT (2011) Evaluation of food risk parameters in the day river flood diversion area, Red River delta, Vietnam. Nat Hazards 56(1):169–194

    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 

  • Darabi H, Haghighi AT, Mohamadi MA, Rashidpour M, Ziegler AD, Hekmatzadeh AA, Kløve B (2020) Urban flood risk mapping using data-driven geospatial techniques for a flood-prone case area in Iran. Hydrol Res 51(1):127–142

    Article  Google Scholar 

  • Dargan S, Kumar M, Ayyagari MR, Kumar G (2020) A survey of deep learning and its applications: a new paradigm to machine learning. Arch Comput Methods Eng 27(4):1071–1092

    Article  Google Scholar 

  • Dawod GM, Mirza MN, Al-Ghamdi KA (2012) GIS-based estimation of flood hazard impacts on road network in Makkah city, Saudi Arabia. Environ Earth Sci 67(8):2205–2215

    Article  Google Scholar 

  • de Moel H, van Vliet M, Aerts JC (2014) Evaluating the effect of flood damage-reducing measures: a case study of the unembanked area of Rotterdam, the Netherlands. Reg Environ Chang 14(3):895–908

    Google Scholar 

  • De Risi R, Jalayer F, De Paola F, Carozza S, Yonas N, Giugni M, Gasparini P (2020) From flood risk mapping toward reducing vulnerability: the case of Addis Ababa. Nat Hazards 100(1):387–415

    Article  Google Scholar 

  • Deng H, Fannon D, Eckelman MJ (2018) Predictive modeling for US commercial building energy use: a comparison of existing statistical and machine learning algorithms using CBECS microdata. Energ Buildings 163:34–43

    Article  Google Scholar 

  • Deo RC, Adamowski JF, Begum K, Salcedo-Sanz S, Kim D-W, Dayal KS, Byun H-R (2019) Quantifying flood events in Bangladesh with a daily-step flood monitoring index based on the concept of daily effective precipitation. Theor Appl Climatol 137:1201–1215. https://doi.org/10.1007/s00704-018-2657-4

    Article  Google Scholar 

  • Doshi M (2014) Correlation based feature selection (CFS) technique to predict student Perfromance. Int J Comput Netw Commun Secur 6(3):197

    Article  Google Scholar 

  • Driessen PP, Hegger DL, Kundzewicz ZW, Van Rijswick HF, Crabbé A, Larrue C, Matczak P, Pettersson M, Priest S, Suykens C, Raadgever GT (2018) Governance strategies for improving flood resilience in the face of climate change. Water 10(11):1595

    Article  Google Scholar 

  • Działek J, Biernacki W, Bokwa A (2013) Challenges to social capacity building in flood-affected areas of southern Poland. Nat Hazards Earth Syst Sci 13(10):2555–2566

    Article  Google Scholar 

  • Eini M, Kaboli HS, Rashidian M, Hedayat H (2020) Hazard and vulnerability in urban flood risk mapping: machine learning techniques and considering the role of urban districts. Int J Disaster Risk Reduct 50:101687

    Article  Google Scholar 

  • El-Haddad BA, Youssef AM, Pourghasemi HR, Pradhan B, El-Shater AH, El-Khashab MH (2021) Flood susceptibility prediction using four machine learning techniques and comparison of their performance at Wadi Qena Basin, Egypt. Nat Hazards 105(1):83–114

    Article  Google Scholar 

  • Evers M, Jonoski A, Almoradie A, Lange L (2016) Collaborative decision making in sustainable flood risk management: a socio-technical approach and tools for participatory governance. Environ Sci Pol 55:335–344

    Article  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 

  • Ganguli P, Nandamuri YR, Chatterjee C (2020) Analysis of persistence in the flood timing and the role of catchment wetness on flood generation in a large river basin in India. Theor Appl Climatol 139:373–388. https://doi.org/10.1007/s00704-019-02964-z

    Article  Google Scholar 

  • Glinski J, Ostrowski J (2011) Mapping of soil physical properties. In: Gliński J, Horabik J, Lipiec J (eds) Encyclopedia of agrophysics. Encyclopedia of Earth Sciences Series. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-3585-1_200

    Chapter  Google Scholar 

  • Gupta S, Collier JS, Palmer-Felgate A, Potter G (2007) Catastrophic flooding origin of shelf valley systems in the English Channel. Nature 448(7151):342–345

    Article  Google Scholar 

  • Haer T, Botzen WW, de Moel H, Aerts JC (2017) Integrating household risk mitigation behavior in flood risk analysis: an agent-based model approach. Risk Anal 37(10):1977–1992

    Article  Google Scholar 

  • Haggag M, Siam AS, El-Dakhakhni W, Coulibaly P, Hassini E (2021) A deep learning model for predicting climate-induced disasters. Nat Hazards 107(1):1009–1034

    Article  Google Scholar 

  • Haghizadeh A, Siahkamari S, Haghiabi AH, Rahmati O (2017) Forecasting flood-prone areas using Shannon’s entropy model. J Earth Syst Sci 126(3):39

    Article  Google Scholar 

  • Halgamuge MN, Daminda E, Nirmalathas A (2020) Best optimizer selection for predicting bushfire occurrences using deep learning. Nat Hazards 103:845–860

    Article  Google Scholar 

  • Haq M, Akhtar M, Muhammad S, Paras S, Rahmatullah J (2012) Techniques of remote sensing and GIS for flood monitoring and damage assessment: a case study of Sindh province, Pakistan. Egypt J Remote Sens Space Sci 15(2):135–141

    Google Scholar 

  • Heidari A (2014) Flood vulnerability of the Karun River System and short-term mitigation measures. J Flood Risk Manag 7(1):65–80

    Article  Google Scholar 

  • Hudson PGMB, Botzen WJW, Kreibich H, Bubeck P, Aerts JCJH (2014) Evaluating the effectiveness of flood damage mitigation measures by the application of propensity score matching. Nat Hazards Earth Syst Sci 14(7):1731–1747

    Article  Google Scholar 

  • Hussein K, Alkaabi K, Ghebreyesus D, Liaqat MU, Sharif HO (2020) Land use/land cover change along the Eastern Coast of the UAE and its impact on flooding risk. Geomat Nat Haz Risk 11(1):112–130

    Article  Google Scholar 

  • Islam ARMT, Talukdar S, Mahato S, Kundu S, Eibek KU, Pham QB et al (2021) Flood susceptibility modelling using advanced ensemble machine learning models. Geosci Front 12(3):101075

    Article  Google Scholar 

  • Jan B, Farman H, Khan M, Imran M, Islam IU, Ahmad A et al (2019) Deep learning in big data analytics: a comparative study. Comput Electr Eng 75:275–287

    Article  Google Scholar 

  • Kabała C, Charzyński P, Chodorowski J, Drewnik M, Glina B, Greinert A et al (2019) Polish soil classification: principles, classification scheme and correlations. Soil Sci Annu 70(2)

  • Karegowda AG, Manjunath AS, Jayaram MA (2010) Comparative study of attribute selection using gain ratio and correlation based feature selection. Int J Inform Technol Knowl Manag 2(2):271–277

    Google Scholar 

  • Ke Q, Tian X, Bricker J, Tian Z, Guan G, Cai H, Huang X, Yang H, Liu J (2020) Urban pluvial flooding prediction by machine learning approaches–a case study of Shenzhen city, China. Adv Water Resour 145:103719

    Article  Google Scholar 

  • Kelleher C, McPhillips L (2020) Exploring the application of topographic indices in urban areas as indicators of pluvial flooding locations. Hydrol Process 34(3):780–794

    Article  Google Scholar 

  • Khamparia A, Singh KM (2019) A systematic review on deep learning architectures and applications. Expert Syst 36(3):e12400

    Article  Google Scholar 

  • Khosravi K, Shahabi H, Pham BT, Adamowski J, Shirzadi A, Pradhan B, Dou J, Ly HB, Gróf G, Ho HL, Hong H (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 

  • Kron W, Eichner J, Kundzewicz ZW (2019) Reduction of flood risk in Europe–reflections from a reinsurance perspective. J Hydrol 576:197–209

    Article  Google Scholar 

  • Kundzewicz ZW (2001) Water problems of central and eastern Europe-a region in transition. Hydrol Sci J 46(6):883–896

    Article  Google Scholar 

  • Kundzewicz ZW (2014) Adapting flood preparedness tools to changing flood risk conditions: the situation in Poland. Oceanologia 56(2):385–407

    Article  Google Scholar 

  • Kundzewicz ZW, Szamalek K, Kowalczak P (1999) The great flood of 1997 in Poland. Hydrol Sci J 44(6):855–870

    Article  Google Scholar 

  • Kundzewicz ZW, Piniewski M, Mezghani A, Okruszko T, Pińskwar I, Kardel I et al (2018) Assessment of climate change and associated impact on selected sectors in Poland. Acta Geophys 66(6):1509–1523

    Article  Google Scholar 

  • Kuźmiński Ł, Nadolny M, Wojtaszek H (2020) Probabilistic quantification in the analysis of flood risks in cross-border areas of Poland and Germany. Energies 13(22):6020

    Article  Google Scholar 

  • Latif SD, Ahmed AN, Sathiamurthy E, Huang YF, El-Shafie A (2021) Evaluation of deep learning algorithm for inflow forecasting: a case study of Durian Tunggal Reservoir, Peninsular Malaysia. Nat Hazards 109(1):351–369

    Article  Google Scholar 

  • LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444

    Article  Google Scholar 

  • Lee S, Kim JC, Jung HS, Lee MJ, Lee S (2017) Spatial prediction of flood susceptibility using random-forest and boosted-tree models in Seoul metropolitan city, Korea. Geomat Nat Haz Risk 8(2):1185–1203

    Article  Google Scholar 

  • Magnuszewski A, Gutry-Korycka M, Mikulski Z (2012) Historyczne i współczesne warunki przepływu wód wielkich Wisły w Warszawie. Część I Gospodarka Wodna 1:9–18

    Google Scholar 

  • Mai T, Mushtaq S, Reardon-Smith K, Webb P, Stone R, Kath J, An-Vo DA (2020) Defining flood risk management strategies: a systems approach. Int J Disaster Risk Reduct 47:101550

    Article  Google Scholar 

  • Manfreda S, Di Leo M, Sole A (2011) Detection of flood-prone areas using digital elevation models. J Hydrol Eng 16(10):781–790

    Article  Google Scholar 

  • Markantonis V, Meyer V, Lienhoop N (2013) Evaluation of the environmental impacts of extreme floods in the Evros River basin using Contingent Valuation Method. Nat Hazards 69(3):1535–1549

    Article  Google Scholar 

  • Minár J, Evans IS, Jenčo M (2020) A comprehensive system of definitions of land surface (topographic) curvatures, with implications for their application in geoscience modelling and prediction. Earth Sci Rev 211:103414

  • Minea G (2013) Assessment of the flash flood potential of Basca river catchment (Romania) based on physiographic factors. Open Geosci 5(3):344–353

    Article  Google Scholar 

  • Moore ID, Wilson JP (1992) Length-slope factors for the Revised Universal Soil Loss Equation: simplified method of estimation. J Soil Water Conserv 47(5):423–428

    Google Scholar 

  • Mosavi A, Golshan M, Janizadeh S, Choubin B, Melesse AM, Dineva AA (2020) Ensemble models of GLM, FDA, MARS, and RF for flood and erosion susceptibility mapping: a priority assessment of sub-basins. Geocarto Int 1–20. https://doi.org/10.1080/10106049.2020.1829101

  • Naghibi SA, Vafakhah M, Hashemi H, Pradhan B, Alavi SJ (2020) Water resources management through flood spreading project suitability mapping using frequency ratio, k-nearest neighbours, and random forest algorithms. Nat Resour Res 29(3):1915–1933

    Article  Google Scholar 

  • Niedzielski T (2007) A data-based regional scale autoregressive rainfall-runoff model: a study from the Odra River. Stoch Env Res Risk A 21(6):649–664

    Article  Google Scholar 

  • Nowak Da Costa J, Calka B, Bielecka E (2021) Urban population flood impact applied to a Warsaw scenario. Resources 10(6):62. https://doi.org/10.3390/resources10060062

    Article  Google Scholar 

  • Panakkat A, Adeli H (2007) Neural network models for earthquake magnitude prediction using multiple seismicity indicators. Int J Neural Syst 17(01):13–33

    Article  Google Scholar 

  • Pantaleoni E, Engel BA, Johannsen CJ (2007) Identifying agricultural flood damage using Landsat imagery. Precis Agric 8(1):27–36

    Article  Google Scholar 

  • Parvaze S, Khan JN, Kumar R, Allaie SP (2021) Temporal flood forecasting for trans-boundary Jhelum River of Greater Himalayas. Theor Appl Climatol 144:493–506. https://doi.org/10.1007/s00704-021-03562-8

    Article  Google Scholar 

  • Perera EDP, Lahat L (2015) Fuzzy logic based flood forecasting model for the Kelantan River basin, Malaysia. J Hydro Environ Res 9(4):542–553. https://doi.org/10.1016/j.jher.2014.12.001

    Article  Google Scholar 

  • Pham BT, Tien Bui D, Indra P, Dholakia M (2015) Landslide susceptibility assessment at a part of Uttarakhand Himalaya, India using GIS–based statistical approach of frequency ratio method. Int J Eng Res Technol 4(11):338–344

    Google Scholar 

  • Pham BT, Phong TV, Nguyen HD, Qi C, Al-Ansari N, Amini A et al (2020) A comparative study of kernel logistic regression, radial basis function classifier, multinomial naïve bayes, and logistic model tree for flash flood susceptibility mapping. Water 12(1):239

    Article  Google Scholar 

  • Pham QB, Achour Y, Ali SA, Parvin F, Vojtek M, Vojteková J, Al-Ansari N, Achu AL, Costache R, Khedher KM, Anh DT (2021) A comparison among fuzzy multi-criteria decision making, bivariate, multivariate and machine learning models in landslide susceptibility mapping. Geomat Nat Haz Risk 12(1):1741–1777

    Article  Google Scholar 

  • Phongsapan K, Chishtie F, Poortinga A, Bhandari B, Meechaiya C, Kunlamai T et al (2019) Operational flood risk index mapping for disaster risk reduction using Earth Observations and cloud computing technologies: a case study on Myanmar. Front Environ Sci 7:191

    Article  Google Scholar 

  • Pourali SH, Arrowsmith C, Chrisman N, Matkan AA, Mitchell D (2016) Topography wetness index application in flood-risk-based land use planning. Appl Spat Anal Policy 9(1):39–54

    Article  Google Scholar 

  • Poussin JK, Botzen WW, Aerts JC (2014) Factors of influence on flood damage mitigation behaviour by households. Environ Sci Pol 40:69–77

    Article  Google Scholar 

  • Pradhan B (2013) A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS. Comput Geosci 51:350–365

    Article  Google Scholar 

  • Pradhan B, Hagemann U, Tehrany MS, Prechtel N (2014) An easy to use ArcMap based texture analysis program for extraction of flooded areas from TerraSAR-X satellite image. Comput Geosci 63:34–43

    Article  Google Scholar 

  • Pradhan B, Sameen MI, Kalantar B (2017) Optimized rule-based flood mapping technique using multitemporal RADARSAT-2 images in the tropical region. IEEE J Sel Top Appl Earth Observ Remote Sens 10(7):3190–3199

    Article  Google Scholar 

  • Prasad P, Loveson VJ, Das B, Kotha M (2021) Novel ensemble machine learning models in flood susceptibility mapping. Geocarto Int 1–23. https://doi.org/10.1080/10106049.2021.1892209

  • Qin CZ, Zhu AX, Pei T, Li BL, Scholten T, Behrens T, Zhou CH (2011) An approach to computing topographic wetness index based on maximum downslope gradient. Precis Agric 12(1):32–43

    Article  Google Scholar 

  • Rahman M, Ningsheng C, Islam MM, Dewan A, Iqbal J, Washakh RMA, Shufeng T (2019) Flood susceptibility assessment in Bangladesh using machine learning and multi-criteria decision analysis. Earth Syst Environ 3(3):585–601

    Article  Google Scholar 

  • Rahmati O, Pourghasemi HR (2017) Identification of critical flood prone areas in data-scarce and ungauged regions: a comparison of three data mining models. Water Resour Manag 31(5):1473–1487

    Article  Google Scholar 

  • Rahmati O, Pourghasemi HR, Zeinivand H (2016a) Flood susceptibility mapping using frequency ratio and weights-of-evidence models in the Golastan Province, Iran. Geocarto Int 31(1):42–70. https://doi.org/10.1080/10106049.2015.1041559

    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 Haz Risk 7(3):1000–1017

    Article  Google Scholar 

  • Rahmati O, Darabi H, Haghighi AT, Stefanidis S, Kornejady A, Nalivan OA, Tien Bui D (2019) Urban flood hazard modeling using self-organizing map neural network. Water 11(11):2370

    Article  Google Scholar 

  • Ray A, Kumar V, Kumar A, Rai R, Khandelwal M, Singh TN (2020) Stability prediction of Himalayan residual soil slope using artificial neural network. Nat Hazards 103(3):3523–3540

    Article  Google Scholar 

  • Razafindrabe BH, Kada R, Arima M, Inoue S (2014) Analyzing flood risk and related impacts to urban communities in central Vietnam. Mitig Adapt Strateg Glob Chang 19(2):177–198

    Article  Google Scholar 

  • Roy DC, Blaschke T (2015) Spatial vulnerability assessment of floods in the coastal regions of Bangladesh. Geomat Nat Haz Risk 6(1):21–44

    Article  Google Scholar 

  • Roy SS, Mallik A, Gulati R, Obaidat MS, Krishna PV (2017) A deep learning based artificial neural network approach for intrusion detection. In: International Conference on Mathematics and Computing. Springer, Singapore, pp 44–53

    Google Scholar 

  • Saha TK, Pal S, Talukdar S, Debanshi S, Khatun R, Singha P, Mandal I (2021) How far spatial resolution affects the ensemble machine learning based flood susceptibility prediction in data sparse region. J Environ Manag 297:113344

    Article  Google Scholar 

  • Sahana M, Rehman S, Sajjad H, Hong H (2020) Exploring effectiveness of frequency ratio and support vector machine models in storm surge flood susceptibility assessment: a study of Sundarban Biosphere Reserve, India. Catena 189:104450

    Article  Google Scholar 

  • Samanta S, Koloa C, Kumar Pal D, Palsamanta B (2016) Flood risk analysis in lower part of Markham river based on multi-criteria decision approach (MCDA). Hydrology 3(3):29

    Article  Google Scholar 

  • Sanyal J, Lu XX (2009) Ideal location for flood shelter: a geographic information system approach. J Flood Risk Manag 2(4):262–271

    Article  Google Scholar 

  • Schanze J (2006) Flood risk management–a basic framework. In: Flood risk management: hazards, vulnerability and mitigation measures. Springer, Dordrecht, pp 1–20

    Chapter  Google Scholar 

  • Scionti F, Miguez MG, Barbaro G, De Sousa MM, Foti G, Canale C (2018) Integrated methodology for urban flood risk mitigation in Cittanova, Italy. J Water Resour Plan Manag 144(10):05018013

    Article  Google Scholar 

  • Strobl RO, Forte F, Lonigro T (2012) Comparison of the feasibility of three flood-risk extent delineation techniques using geographic information system: case study in Tavoliere delle Puglie, Italy. J Flood Risk Manag 5(3):245–257

    Article  Google Scholar 

  • Szamalek K (2000) The great flood of 1997 in Poland: the truth and myth. In: Flood issues in contemporary water management. Springer, Dordrecht, pp 67–74

    Chapter  Google Scholar 

  • Szwagrzyk M, Kaim D, Price B, Wypych A, Grabska E, Kozak J (2018) Impact of forecasted land use changes on flood risk in the Polish Carpathians. Nat Hazards 94(1):227–240

    Article  Google Scholar 

  • Talukdar S, Ghose B, Salam R, Mahato S, Pham QB, Linh NTT et al (2020) Flood susceptibility modeling in Teesta River basin, Bangladesh using novel ensembles of bagging algorithms. Stoch Env Res Risk A 34(12):2277–2300

    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

    Article  Google Scholar 

  • Taylor J, Man Lai K, Davies M, Clifton D, Ridley I, Biddulph P (2011) Flood management: prediction of microbial contamination in large-scale floods in urban environments. Environ Int 37(5):1019–1029

    Article  Google Scholar 

  • Tehrany MS, Pradhan B, Jebur MN (2013) Spatial prediction of flood susceptible areas using rule based decision tree (DT) and a novel ensemble bivariate and multivariate statistical models in GIS. J Hydrol 504:69–79

    Article  Google Scholar 

  • Tehrany MS, Lee MJ, Pradhan B, Jebur MN, Lee S (2014) Flood susceptibility mapping using integrated bivariate and multivariate statistical models. Environ Earth Sci 72(10):4001–4015

    Article  Google Scholar 

  • Termeh SVR, Kornejady A, Pourghasemi HR, Keesstra S (2018) Flood susceptibility mapping using novel ensembles of adaptive neuro fuzzy inference system and metaheuristic algorithms. Sci Total Environ 615:438–451

    Article  Google Scholar 

  • Tien Bui D, Khosravi K, Shahabi H, Daggupati P, Adamowski JF, Melesse AM et al (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 Sens 11(13):1589

    Article  Google Scholar 

  • Tsakiri K, Marsellos A, Kapetanakis S (2018) Artificial neural network and multiple linear regression for flood prediction in Mohawk River, New York. Water 10(9):1158

    Article  Google Scholar 

  • Tsakiris G (2014) Flood risk assessment: concepts, modelling, applications. Nat Hazards Earth Syst Sci 14(5):1361–1369

    Article  Google Scholar 

  • Umer M, Gabriel HF, Haider S, Nusrat A, Shadid M, Umer M (2021) (2021) Application of precipitation products for flood modeling of transboundary river basin: a case study of Jhelum Basin. Theor Appl Climatol 143:989–1004. https://doi.org/10.1007/s00704-020-03471-2

    Article  Google Scholar 

  • Vahidnia MH, Alesheikh A, Alimohammadi A, Bassiri A (2008) Fuzzy analytical hierarchy process in GIS application. Int Arch Photogramm Remote Sens Spat Inf Sci 37(B2):593–596

    Google Scholar 

  • Vahidnia MH, Alesheikh AA, Alimohammadi A, Hosseinali F (2010) A GIS-based neuro-fuzzy procedure for integrating knowledge and data in landslide susceptibility mapping. Comput Geosci 36(9):1101–1114

    Article  Google Scholar 

  • Van Appledorn M, Baker ME, Miller AJ (2019) River-valley morphology, basin size, and flow-event magnitude interact to produce wide variation in flooding dynamics. Ecosphere 10(1):e02546

    Google Scholar 

  • Van Dao D, Jaafari A, Bayat M, Mafi-Gholami D, Qi C, Moayedi H et al (2020) A spatially explicit deep learning neural network model for the prediction of landslide susceptibility. Catena 188:104451

    Article  Google Scholar 

  • Van den Honert RC, McAneney J (2011) The 2011 Brisbane floods: causes, impacts and implications. Water 3(4):1149–1173

    Article  Google Scholar 

  • Vorogushyn S, Lindenschmidt KE, Kreibich H, Apel H, Merz B (2012) Analysis of a detention basin impact on dike failure probabilities and flood risk for a channel-dike-floodplain system along the river Elbe, Germany. J Hydrol 436:120–131

    Article  Google Scholar 

  • Wang Y, Fang Z, Hong H, Peng L (2020) Flood susceptibility mapping using convolutional neural network frameworks. J Hydrol 582:124482

    Article  Google Scholar 

  • Wasko C, Nathan R (2019) Influence of changes in rainfall and soil moisture on trends in flooding. J Hydrol 575:432–441

    Article  Google Scholar 

  • White I, Kingston R, Barker A (2010) Participatory geographic information systems and public engagement within flood risk management. J Flood Risk Manag 3(4):337–346

    Article  Google Scholar 

  • Wierzbicki G, Ostrowski P, Falkowski T (2020) Applying floodplain geomorphology to flood management (The Lower Vistula River upstream from Plock, Poland). Open Geosci 12(1):1003–1016

    Article  Google Scholar 

  • Wierzbicki G, Ostrowski P, Bartold P, Bujakowski F, Falkowski T, Osiński P (2021) Urban geomorphology of the Vistula River valley in Warsaw. J Maps 1–16

  • Winsemius HC, Van Beek LPH, Jongman B, Ward PJ, Bouwman A (2013) A framework for global river flood risk assessments. Hydrol Earth Syst Sci 17(5):1871–1892

    Article  Google Scholar 

  • Xu C, Chen Y, Chen Y, Zhao R, Ding H (2013) Responses of surface runoff to climate change and human activities in the arid region of Central Asia: a case study in the Tarim River Basin, China. Environ Manag 51(4):926–938

    Article  Google Scholar 

  • Xu FG, Yang XG, Zhou JW (2014) An empirical approach for evaluation of the potential of debris flow occurrence in mountainous areas. Environ Earth Sci 71(7):2979–2988

    Article  Google Scholar 

  • Yang YE, Ray PA, Brown CM, Khalil AF, Winston HY (2015) Estimation of flood damage functions for river basin planning: a case study in Bangladesh. Nat Hazards 75(3):2773–2791

    Article  Google Scholar 

  • Yang Y, Chen G, Reniers G (2020) Vulnerability assessment of atmospheric storage tanks to floods based on logistic regression. Reliab Eng Syst Saf 196:106721

    Article  Google Scholar 

  • Yariyan P, Janizadeh S, Van Phong T, Nguyen HD, Costache R, Van Le H et al (2020) Improvement of best first decision trees using bagging and dagging ensembles for flood probability mapping. Water Resour Manag 34(9):3037–3053

    Article  Google Scholar 

  • Zhao J, Jin J, Xu J, Gu Q, Hang Q, Chen Y (2018) Risk assessment of flood disaster and forewarning model at different spatial-temporal scales. Theor Appl Climatol 132:791–808. https://doi.org/10.1007/s00704-017-2086-9

    Article  Google Scholar 

  • Zhou X, Bai Z, Yang Y (2017) Linking trends in urban extreme rainfall to urban flooding in China. Int J Climatol 37(13):4586–4593

    Article  Google Scholar 

  • Bates PD (2004) Remote sensing and flood inundation modelling. Hydrol Process 18(13):2593–2597

    Article  Google Scholar 

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Acknowledgements

Data used in this study were derived from the Polish public administration geoportals (e.g., geoportal.gov.pl, https://geodezja.mazovia.pl/mapy.html#tematyczne) and other open data sources.

Funding

This research was funded by Military University of Technology, Faculty of Civil Engineering and Geodesy (grant number 531-4000-22-785/UGB/2022). The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

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F.P., S.A.A: conceptualization, writing—original draft, software, formal analysis, visualization, BC: data acquisition, analysis, writing, review; EB: data preparation, writing, review; Q.B.P: suppervision, conceptualization, wrting, review

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Correspondence to Nguyen Thi Thuy Linh.

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Parvin, F., Ali, S.A., Calka, B. et al. Urban flood vulnerability assessment in a densely urbanized city using multi-factor analysis and machine learning algorithms. Theor Appl Climatol 149, 639–659 (2022). https://doi.org/10.1007/s00704-022-04068-7

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