Earth Systems and Environment

, Volume 3, Issue 3, pp 585–601 | Cite as

Flood Susceptibility Assessment in Bangladesh Using Machine Learning and Multi-criteria Decision Analysis

  • Mahfuzur Rahman
  • Chen NingshengEmail author
  • Md Monirul Islam
  • Ashraf Dewan
  • Javed Iqbal
  • Rana Muhammad Ali Washakh
  • Tian Shufeng
Original Article


This work proposes a new approach by integrating statistical, machine learning, and multi-criteria decision analysis, including artificial neural network (ANN), logistic regression (LR), frequency ratio (FR), and analytical hierarchy process (AHP). Dependent (flood inventory) and independent variables (flood causative factors) were prepared using remote sensing data and the Mike-11 hydrological model and secondary data from different sources. The flood inventory map was randomly divided into training and testing datasets, where 334 flood locations (70%) were used for training and the remaining 141 locations (30%) were employed for testing. Using the area under the receiver operating curve (AUROC), predictive power of the model was tested. The results revealed that LR model had the highest success rate (81.60%) and prediction rate (86.80%), among others. Furthermore, different combinations of the models were evaluated for flood susceptibility mapping and the best combination (11C) was used for generating a new flood hazard map for Bangladesh. The performance of the 11C integrated models was also evaluated using the AUROC and found that integrated LR-FR model had the highest predictive power with an AUROC value of 88.10%. This study offers a new opportunity to the relevant authority for planning and designing flood control measures.


AHP ANN Bangladesh Flood susceptibility map FR LR 



The authors acknowledge and appreciate the provision of rainfall data by the Bangladesh Water Development Board (BWDB), without which this study would not have been possible. Thanks to AFM Kamal Chowdhury, Nirdesh Nepal and Soumik Nafis Sadeek for their valuable comments which helped us to improve the quality of the manuscript. This research was funded by the National Natural Science Foundation of China [Grant no. 41861134008 and 41671112] and the 135 Strategic Program of the Institute of Mountain Hazards and Environment (IMHE), Chinese Academy of Sciences (CAS) [Grant no. SDS-135-1705].

Compliance with Ethical Standards

Conflict of Interest

On behalf of all the authors, the corresponding author states that there is no conflict of interest.


  1. Arabameri A, Pourghasemi HR, Yamani M (2017) Applying different scenarios for landslide spatial modeling using computational intelligence methods. Environ Earth Sci 76:832CrossRefGoogle Scholar
  2. Arabameri A, Pradhan B, Rezaei K, Yamani M, Pourghasemi HR, Lombardo L (2018) Spatial modelling of gully erosion using evidential belief function, logistic regression, and a new ensemble of evidential belief function–logistic regression algorithm. Land Degrad Dev 29:4035–4049CrossRefGoogle Scholar
  3. Arabameri A, Pradhan B, Rezaei K, Sohrabi M, Kalantari Z (2019) GIS-based landslide susceptibility mapping using numerical risk factor bivariate model and its ensemble with linear multivariate regression and boosted regression tree algorithms. J Mt Sci 16:595–618CrossRefGoogle Scholar
  4. Arora M, Das Gupta A, Gupta R (2004) An artificial neural network approach for landslide hazard zonation in the Bhagirathi (Ganga) Valley, Himalayas. Int J Remote Sens 25:559–572CrossRefGoogle Scholar
  5. Asare-Kyei D, Forkuor G, Venus V (2015) Modeling flood hazard zones at the sub-district level with the rational model integrated with GIS and remote sensing approaches. Water 7:3531–3564CrossRefGoogle Scholar
  6. Ashley WS, Strader S, Rosencrants T, Krmenec AJ (2014) Spatiotemporal changes in tornado hazard exposure: the case of the expanding bull’s-eye effect in Chicago, Illinois. Weather Clim Soc 6:175–193CrossRefGoogle Scholar
  7. Bangladesh Bureau of Statistics B (2019) Gender Statistics of Bangladesh, 2018. Bangladesh Bureau of Statistics (BBS)
  8. Barua U, Akhter MS, Ansary MA (2016) District-wise multi-hazard zoning of Bangladesh. Nat Hazards 82:1895–1918CrossRefGoogle Scholar
  9. Bates PD (2004) Remote sensing and flood inundation modelling. Hydrol Process 18:2593–2597CrossRefGoogle Scholar
  10. Bui DT et al (2018) Novel hybrid evolutionary algorithms for spatial prediction of floods. Sci Rep 8:15364CrossRefGoogle Scholar
  11. Bui DT, Ngo PTT, 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 179:184–196CrossRefGoogle Scholar
  12. 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 Modell Softw 95:229–245CrossRefGoogle Scholar
  13. Chen W, Pourghasemi HR, Naghibi SA (2018) A comparative study of landslide susceptibility maps produced using support vector machine with different kernel functions and entropy data mining models in China. Bull Eng Geol Environ 77:647CrossRefGoogle Scholar
  14. Cho S, Kim J, Heo E (2015) Application of fuzzy analytic hierarchy process to select the optimal heating facility for Korean horticulture and stockbreeding sectors. Renew Sustain Energy Rev 49:1075–1083CrossRefGoogle Scholar
  15. 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. Sci Total Environ 651:2087–2096CrossRefGoogle Scholar
  16. 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 126:59CrossRefGoogle Scholar
  17. Danumah JH et al (2016) Flood risk assessment and mapping in Abidjan district using multi-criteria analysis (AHP) model and geoinformation techniques,(cote d’ivoire). Geoenviron Disasters 3:10CrossRefGoogle Scholar
  18. 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 569:142–154CrossRefGoogle Scholar
  19. de Brito MM, Evers M (2016) Multi-criteria decision-making for flood risk management: a survey of the current state of the art. Nat Hazards Earth Syst Sci 16:1019–1033CrossRefGoogle Scholar
  20. Department BF (2016) National Land Cover Classification System using LCCS v3. Accessed 30 Dec 2018
  21. Dewan AM, Islam MM, Kumamoto T, Nishigaki M (2007) Evaluating flood hazard for land-use planning in Greater Dhaka of Bangladesh using remote sensing and GIS techniques. Water Resour Manag 21:1601CrossRefGoogle Scholar
  22. Elsafi SH (2014) Artificial neural networks (ANNs) for flood forecasting at Dongola Station in the River Nile, Sudan. Alex Eng J 53:655–662CrossRefGoogle Scholar
  23. Falah F, Rahmati O, Rostami M, Ahmadisharaf E, Daliakopoulos IN, Pourghasemi HR (2019) Artificial neural networks for flood susceptibility mapping in data-scarce urban areas. In: Pourghasemi HR, Gokceoglu C (eds) Spatial modeling in GIS and R for earth and environmental sciences. Elsevier, pp 323–336Google Scholar
  24. Fenicia F, Kavetski D, Savenije HH, Clark MP, Schoups G, Pfister L, Freer J (2014) Catchment properties, function, and conceptual model representation: is there a correspondence? Hydrol Process 28:2451–2467CrossRefGoogle Scholar
  25. Fernández D, Lutz M (2010) Urban flood hazard zoning in Tucumán Province, Argentina, using GIS and multicriteria decision analysis. Eng Geol 111:90–98CrossRefGoogle Scholar
  26. Gazendam E, Gharabaghi B, Ackerman JD, Whiteley H (2016) Integrative neural networks models for stream assessment in restoration projects. J Hydrol 536:339–350CrossRefGoogle Scholar
  27. Hasan S, 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 9:505CrossRefGoogle Scholar
  28. Hong H, Pradhan B, Xu C, Bui DT (2015) Spatial prediction of landslide hazard at the Yihuang area (China) using two-class kernel logistic regression, alternating decision tree and support vector machines. Catena 133:266–281CrossRefGoogle Scholar
  29. Hong H, Tsangaratos P, Ilia I, Liu J, Zhu A-X, Chen W (2018) Application of fuzzy weight of evidence and data mining techniques in construction of flood susceptibility map of Poyang County, China. Sci Total Environ 625:575–588CrossRefGoogle Scholar
  30. Hossain S (2015) Local level flood forecasting system using mathematical model incorporating WRF model predicted rainfallGoogle Scholar
  31. ICIMOD (2017) Bangladesh Flood Mapping 2017. Accessed 01 Jan 2018 2017
  32. Islam M, Sado K (2000a) Flood hazard assessment in Bangladesh using NOAA AVHRR data with geographical information system. Hydrol Process 14:605–620CrossRefGoogle Scholar
  33. Islam MM, Sado K (2000b) Development of flood hazard maps of Bangladesh using NOAA-AVHRR images with GIS. Hydrol Sci J 45:337–355CrossRefGoogle Scholar
  34. Islam MM, Sado K (2002) Development priority map for flood countermeasures by remote sensing data with geographic information system. J Hydrol Eng 7:346–355CrossRefGoogle Scholar
  35. Islam MA, Hasan MA, Farukh MA (2017) Application of GIS in general soil mapping of Bangladesh. J Geogr Inf Syst 9:604Google Scholar
  36. Jain AK, Mao J, Mohiuddin K (1996) Artificial neural networks: a tutorial. Computer 29:31–44CrossRefGoogle Scholar
  37. Karsoliya S (2012) Approximating number of hidden layer neurons in multiple hidden layer BPNN architecture. Int J Eng Trends Technol 3:714–717Google Scholar
  38. Khosravi K, Nohani E, Maroufinia E, Pourghasemi HR (2016a) A GIS-based flood susceptibility assessment and its mapping in Iran: a comparison between frequency ratio and weights-of-evidence bivariate statistical models with multi-criteria decision-making technique. Nat Hazards 83:947–987CrossRefGoogle Scholar
  39. Khosravi K, Pourghasemi HR, Chapi K, Bahri M (2016b) 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. Environ Monit Assess 188:656CrossRefGoogle Scholar
  40. Khosravi K et al (2018) A comparative assessment of decision trees algorithms for flash flood susceptibility modeling at Haraz watershed, northern Iran. Sci Total Environ 627:744–755CrossRefGoogle Scholar
  41. Kia MB, Pirasteh S, Pradhan B, Mahmud AR, Sulaiman WNA, Moradi A (2012) An artificial neural network model for flood simulation using GIS: Johor River Basin, Malaysia. Environ Earth Sci 67:251–264CrossRefGoogle Scholar
  42. Kourgialas NN, Karatzas GP (2011) Flood management and a GIS modelling method to assess flood-hazard areas—a case study. Hydrol Sci J 56:212–225CrossRefGoogle Scholar
  43. Lee MJ, Kang Je, Jeon S (2012)Application of frequency ratio model and validation for predictive flooded area susceptibility mapping using GIS. In: 2012 IEEE international geoscience and remote sensing symposium. IEEE, pp 895–898Google Scholar
  44. Lin L et al (2019) Improvement and Validation of NASA/MODIS NRT Global Flood Mapping Remote Sensing 11:205Google Scholar
  45. 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. Nat Hazards 90:1031–1050CrossRefGoogle Scholar
  46. Masood M, Takeuchi K (2012) Assessment of flood hazard, vulnerability and risk of mid-eastern Dhaka using DEM and 1D hydrodynamic model. Nat hazards 61:757–770CrossRefGoogle Scholar
  47. Mojaddadi H, Pradhan B, Nampak H, Ahmad N, Ghazali AHb (2017) Ensemble machine-learning-based geospatial approach for flood risk assessment using multi-sensor remote-sensing data and GIS Geomatics. Nat Hazards Risk 8:1080–1102CrossRefGoogle Scholar
  48. Mosavi A, Ozturk P, Chau K-w (2018) Flood prediction using machine learning models: literature review. Water 10:1536CrossRefGoogle Scholar
  49. Nguyen AT, Nguyen LD, Le-Hoai L, Dang CN (2015) Quantifying the complexity of transportation projects using the fuzzy analytic hierarchy process. Int J Project Manage 33:1364–1376CrossRefGoogle Scholar
  50. NOAA (2007) Risk and vulnerability assessment steps. Hazards analysis extended discussion. NOAA Coastal Services Center, Charleston, SCGoogle Scholar
  51. Nyarko BK (2002) Application of a rational model in GIS for flood risk assessment in Accra. Ghana J Spat Hydrol 2:1–14Google Scholar
  52. Ouma Y, Tateishi R (2014) Urban flood vulnerability and risk mapping using integrated multi-parametric AHP and GIS: methodological overview and case study assessment. Water 6:1515–1545CrossRefGoogle Scholar
  53. Pham BT, Bui DT, Prakash I, Dholakia M (2017) Hybrid integration of Multilayer Perceptron Neural Networks and machine learning ensembles for landslide susceptibility assessment at Himalayan area (India) using GIS. Catena 149:52–63CrossRefGoogle Scholar
  54. Pourghasemi HR, Yousefi S, Kornejady A, Cerdà A (2017) Performance assessment of individual and ensemble data-mining techniques for gully erosion modeling. Sci Total Environ 609:764–775CrossRefGoogle Scholar
  55. Pradhan B, Lee S (2010) Landslide susceptibility assessment and factor effect analysis: backpropagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modelling. Environ Modell Softw 25:747–759CrossRefGoogle Scholar
  56. Rahman AA, Alam M, Alam SS, Uzzaman MR, Rashid M, Rabbani G (2007) Risks, vulnerability and adaptation in Bangladesh. Hum Dev Rep 8Google Scholar
  57. Rahmati O, Haghizadeh A, Pourghasemi HR, Noormohamadi F (2016a) Gully erosion susceptibility mapping: the role of GIS-based bivariate statistical models and their comparison. Nat Hazards 82:1231–1258CrossRefGoogle Scholar
  58. Rahmati O, Pourghasemi HR, Zeinivand H (2016b) Flood susceptibility mapping using frequency ratio and weights-of-evidence models in the Golastan Province, Iran. Geocarto Int 31:42–70CrossRefGoogle Scholar
  59. Rahmati O, Zeinivand H, Besharat M (2016c) Flood hazard zoning in Yasooj region, Iran, using GIS and multi-criteria decision analysis. Geomat Nat Hazards Risk 7:1000–1017CrossRefGoogle Scholar
  60. Rao D (2017) Hydrological and hydrodynamic modeling for flood damage mitigation in Brahmaniâ Baitarani River Basin, India. Geocarto Int 32:1004–1016CrossRefGoogle Scholar
  61. Rauter M, Winkler D (2018) Predicting Natural Hazards with Neuronal Networks arXiv preprint arXiv:180207257
  62. Rizeei HM, Pradhan B, Saharkhiz MA (2019) Allocation of emergency response centres in response to pluvial flooding-prone demand points using integrated multiple layer perceptron and maximum coverage location problem models. Int J Disaster Risk Reduction:101205CrossRefGoogle Scholar
  63. Saaty TL (1980) The Analytic (Hierarchy) Process. St Louis ua, New YorkGoogle Scholar
  64. Saaty TL (2000) Fundamentals of decision making and priority theory with the analytic hierarchy process, vol 6. Rws Publications, PittsburghGoogle Scholar
  65. Saaty TL (2001) The seven pillars of the analytic hierarchy process. In: Köksalan M, Zionts S (eds) Multiple criteria decision making in the new millennium. Springer, Berlin, Heidelberg, pp 15–37CrossRefGoogle Scholar
  66. Saaty TL (2008) Decision making with the analytic hierarchy process. Int J serv Sci 1:83–98Google Scholar
  67. Sahoo SN, Sreeja P (2015) Development of Flood Inundation Maps and quantification of flood risk in an Urban catchment of Brahmaputra River ASCE-ASME. J Risk Uncertain Eng Syst 3:A4015001Google Scholar
  68. Samanta RK, Bhunia GS, Shit PK, Pourghasemi HR (2018a) Flood susceptibility mapping using geospatial frequency ratio technique: a case study of Subarnarekha River Basin, India. Model Earth Syst Environ 4:395–408CrossRefGoogle Scholar
  69. Samanta S, Pal DK, Palsamanta B (2018b) Flood susceptibility analysis through remote sensing, GIS and frequency ratio model. Appl Water Sci 8:66CrossRefGoogle Scholar
  70. Seejata K, Yodying A, Wongthadam T, Mahavik N, Tantanee S (2018) Assessment of flood hazard areas using Analytical Hierarchy Process over the Lower Yom Basin, Sukhothai. Province Procedia Eng 212:340–347CrossRefGoogle Scholar
  71. Shafapour Tehrany M, 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. Geomat Nat Hazards Risk 8:1538–1561CrossRefGoogle Scholar
  72. Shafapour Tehrany M, Kumar L, Neamah Jebur M, Shabani F (2019) Evaluating the application of the statistical index method in flood susceptibility mapping and its comparison with frequency ratio and logistic regression methods. Geomat Nat Hazards Risk 10:79–101CrossRefGoogle Scholar
  73. Sinha DK (2007) Natural disaster reduction: South East Asian realities, risk perception and global strategies. Anthem Press, LondonGoogle Scholar
  74. Tehrany MS, Lee M-J, Pradhan B, Jebur MN, Lee S (2014a) Flood susceptibility mapping using integrated bivariate and multivariate statistical models. Environ Earth Sci 72:4001–4015CrossRefGoogle Scholar
  75. Tehrany MS, Pradhan B, Jebur MN (2014b) Flood susceptibility mapping using a novel ensemble weights-of-evidence and support vector machine models in GIS. J Hydrol 512:332–343CrossRefGoogle Scholar
  76. Tehrany MS, Pradhan B, Jebur MN (2015a) Flood susceptibility analysis and its verification using a novel ensemble support vector machine and frequency ratio method. Stoch Environ Res Risk Assess 29:1149–1165CrossRefGoogle Scholar
  77. Tehrany MS, Pradhan B, Mansor S, Ahmad N (2015b) Flood susceptibility assessment using GIS-based support vector machine model with different kernel types. CATENA 125:91–101CrossRefGoogle Scholar
  78. Tingsanchali T, Karim MF (2005) Flood hazard and risk analysis in the southwest region of Bangladesh. Hydrol Process 19:2055–2069CrossRefGoogle Scholar
  79. Todini F, De Filippis T, De Chiara G, Maracchi G, Martina M, Todini E (2004) Using a GIS approach to asses flood hazard at national scale. In: Proceedings of the European Geosciences Union, 1st General Assembly, Nice, 25–30 April 2004Google Scholar
  80. Uddin K, Matin MA, Meyer FJ (2019) Operational flood mapping using multi-temporal sentinel-1 SAR images: a case study from Bangladesh. Remote Sens 11:1581CrossRefGoogle Scholar
  81. Valencia JA, Graña AM (2018) A neural network model applied to landslide susceptibility analysis (Capitanejo, Colombia) Geomatics. Nat Hazards Risk 9:1106–1128CrossRefGoogle Scholar
  82. Yang T-H, Ho J-Y, Hwang G-D, Lin G-F (2014) An indirect approach for discharge estimation: a combination among micro-genetic algorithm, hydraulic model, and in situ measurement. Flow Meas Instrum 39:46–53CrossRefGoogle Scholar
  83. Zhang W, Lu J, Zhang Y (2016) Comprehensive evaluation index system of low carbon road transport based on fuzzy evaluation method. Procedia Eng 137:659–668CrossRefGoogle Scholar

Copyright information

© King Abdulaziz University and Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Mahfuzur Rahman
    • 1
    • 2
    • 3
  • Chen Ningsheng
    • 1
    Email author
  • Md Monirul Islam
    • 3
  • Ashraf Dewan
    • 4
  • Javed Iqbal
    • 1
    • 5
  • Rana Muhammad Ali Washakh
    • 1
    • 2
  • Tian Shufeng
    • 1
    • 2
  1. 1.Key Laboratory for Mountain Hazards and Earth Surface Process, Institute of Mountain Hazards and EnvironmentChinese Academy of Sciences (CAS)ChengduPeople’s Republic of China
  2. 2.University of Chinese Academy of SciencesBeijingPeople’s Republic of China
  3. 3.Department of Civil EngineeringInternational University of Business Agriculture and Technology (IUBAT)DhakaBangladesh
  4. 4.Spatial Sciences Discipline, School of Earth and Planetary SciencesCurtin UniversityBentleyAustralia
  5. 5.Department of Earth SciencesAbbottabad University of Science and TechnologyAbbottabadPakistan

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