Flood susceptibility mapping in Brahmaputra floodplain of Bangladesh using deep boost, deep learning neural network, and artificial neural network

References

• Adnan MSG, Dewan A, Zannat KE, Abdullah AYM. 2019. 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.
   Web of Science ®Google Scholar
• Ahmed N, Howlader N, Hoque MA-A, Pradhan B. 2021. Coastal erosion vulnerability assessment along the eastern coast of Bangladesh using geospatial techniques. Ocean Coast Manage. 199:105408.
   Web of Science ®Google Scholar
• Alam A, Ahmed B, Sammonds P. 2021. Flash flood susceptibility assessment using the parameters of drainage basin morphometry in SE Bangladesh. Quat Int. 575–576:295–307.
   Web of Science ®Google Scholar
• Andaryani S, Nourani V, Haghighi AT, Keesstra S. 2021. Integration of hard and soft supervised machine learning for flood susceptibility mapping. J Environ Manage. 291:112731.
   PubMed Web of Science ®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.
   PubMed Web of Science ®Google Scholar
• Arabameri A, Saha S, Chen W, Roy J, Pradhan B, Bui DT. 2020. Flash flood susceptibility modelling using functional tree and hybrid ensemble techniques. J Hydrol. 587:125007.
   Web of Science ®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):2032–2085.
   Web of Science ®Google Scholar
• Band SS, Janizadeh S, Chandra Pal S, Saha A, Chakrabortty R, Melesse AM, Mosavi A. 2020. Flash flood susceptibility modeling using new approaches of hybrid and ensemble tree-based machine learning algorithms. Remote Sensing. 12(21):3568.
   Web of Science ®Google Scholar
• Bhuiyan SR, Baky AA. 2014. Digital elevation based flood hazard and vulnerability study at various return periods in Sirajganj Sadar Upazila, Bangladesh. Int J Disaster Risk Reduct. 10(Part A):48–58.
   Google Scholar
• Breiman L. 2001. Random forests. Mach Learn. 45(1):5–32.
   Web of Science ®Google Scholar
• Bui DT, Tsangaratos P, Ngo P-TT, Pham TD, Pham BT. 2019. Flash flood susceptibility modeling using an optimized fuzzy rule based feature selection technique and tree based ensemble methods. Sci Total Environ. 668:1038–1054.
   PubMed Web of Science ®Google Scholar
• Bui Q-T, Nguyen Q-H, Nguyen XL, Pham VD, Nguyen HD, Pham V-M. 2020. Verification of novel integrations of swarm intelligence algorithms into deep learning neural network for flood susceptibility mapping. J Hydrol. 581:124379.
   Web of Science ®Google Scholar
• Chakraborty P, Sarkar A, Bhatla R, Singh R. 2021. Assessing the skill of NCMRWF global ensemble prediction system in predicting Indian summer monsoon during 2018. Atmos Res. 248:105255.
   Web of Science ®Google Scholar
• Chambers JM, Cleveland WS, Kleiner B, Tukey PA. 2018. Graphical methods for data analysis. Boca Raton, FL: Chapman and Hall/CRC.
   Google Scholar
• Chen H, Ito Y, Sawamukai M, Tokunaga T. 2015. Flood hazard assessment in the Kujukuri Plain of Chiba Prefecture, Japan, based on GIS and multicriteria decision analysis. Nat Hazards. 78(1):105–120.
   Web of Science ®Google Scholar
• Chen W, Lei X, Chakrabortty R, Pal SC, Sahana M, Janizadeh S. 2021. Evaluation of different boosting ensemble machine learning models and novel deep learning and boosting framework for head-cut gully erosion susceptibility. J Environ Manage. 284:112015.
   PubMed Web of Science ®Google Scholar
• Chen W, Pourghasemi HR, Zhao Z. 2017. A GIS-based comparative study of Dempster-Shafer, logistic regression and artificial neural network models for landslide susceptibility mapping. Geocarto Int. 32(4):367–385.
   Web of Science ®Google Scholar
• Cherkassky V, Krasnopolsky V, Solomatine DP, Valdes J. 2006. Computational intelligence in earth sciences and environmental applications: Issues and challenges. Neural Netw. 19(2):113–121.
   PubMed Web of Science ®Google Scholar
• Choubin B, Moradi E, Golshan M, Adamowski J, Sajedi-Hosseini F, Mosavi A. 2019. An ensemble prediction of flood susceptibility using multivariate discriminant analysis, classification and regression trees, and support vector machines. Sci Total Environ. 651(Pt 2):2087–2096.
   PubMed Web of Science ®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.
   Web of Science ®Google Scholar
• Cortes C, Mohri M, Syed U. 2014. Deep boosting. International Conference on Machine Learning, Beijing, China.
   Google Scholar
• Costache R. 2019. Flood susceptibility assessment by using bivariate statistics and machine learning models-a useful tool for flood risk management. Water Resour Manage. 33(9):3239–3256.
   Web of Science ®Google Scholar
• Costache R, Hong H, Pham QB. 2020. Comparative assessment of the flash-flood potential within small mountain catchments using bivariate statistics and their novel hybrid integration with machine learning models. Sci Total Environ. 711:134514.
   PubMed Web of Science ®Google Scholar
• Das S. 2020. Flood susceptibility mapping of the Western Ghat coastal belt using multi-source geospatial data and analytical hierarchy process (AHP). Remote Sens Appl: Soc Environ. 20:100379.
   Google Scholar
• Destro E, Amponsah W, Nikolopoulos EI, Marchi L, Marra F, Zoccatelli D, Borga M. 2018. Coupled prediction of flash flood response and debris flow occurrence: application on an alpine extreme flood event. J Hydrol. 558:225–237.
   Web of Science ®Google Scholar
• Dewan A, Corner R, Saleem A, Rahman MM, Haider MR, Rahman MM, Sarker MH. 2017. Assessing channel changes of the Ganges-Padma River system in Bangladesh using Landsat and hydrological data. Geomorphology. 276:257–279.
   Web of Science ®Google Scholar
• Dou J, Yunus AP, Bui DT, Merghadi A, Sahana M, Zhu Z, Chen C-W, Khosravi K, Yang Y, Pham BT. 2019. Assessment of advanced random forest and decision tree algorithms for modeling rainfall-induced landslide susceptibility in the Izu-Oshima Volcanic Island, Japan. Sci Total Environ. 662:332–346.
   PubMed Web of Science ®Google Scholar
• 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: Hamid Reza Pourghasemi, Candan Gokceoglu, editors. Spatial modeling in GIS and R for Earth and Environmental Sciences. Amsterdam, Netherlands: Elsevier; p. 323–336.
   Google Scholar
• Falaschi F, Giacomelli F, Federici P, Puccinelli A, Avanzi GA, Pochini A, Ribolini A. 2009. Logistic regression versus artificial neural networks: landslide susceptibility evaluation in a sample area of the Serchio River valley, Italy. Nat Hazards. 50(3):551–569.
   Web of Science ®Google Scholar
• Fang Y, Yin J, Wu B. 2016. Flooding risk assessment of coastal tourist attractions affected by sea level rise and storm surge: a case study in Zhejiang Province, China. Nat Hazards. 84(1):611–624.
   Web of Science ®Google Scholar
• Fawagreh K, Gaber MM, Elyan E. 2014. Random forests: from early developments to recent advancements. Syst Sci Control Eng. 2(1):602–609.
   Google Scholar
• Fernandez P, Mourato S, Moreira 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 Haz Risk. 7(4):1367–1389.
   Web of Science ®Google Scholar
• Gazi MY, Islam MA, Hossain S. 2019. Flood-hazard mapping in a regional scale–way forward to the future hazard atlas in Bangladesh. Malays J Geosci. 3(1):01–11.
   Google Scholar
• Geris J, Tetzlaff D, McDonnell J, Soulsby C. 2015. The relative role of soil type and tree cover on water storage and transmission in northern headwater catchments. Hydrol Process. 29(7):1844–1860.
   Web of Science ®Google Scholar
• Ghorbanzadeh O, Blaschke T, Gholamnia K, Meena SR, Tiede D, Aryal J. 2019. Evaluation of different machine learning methods and deep-learning convolutional neural networks for landslide detection. Remote Sensing. 11(2):196.
   Web of Science ®Google Scholar
• Haykin S. 1999. Neural networks: a guided tour. In: N.K. Sinha, M.M. Gupta, editors. Soft computing and intelligent systems: theory and applications. Amsterdam, Netherlands: Academic Press; p. 71.
   Google Scholar
• Hoang N-D, Tien Bui D. 2016. A novel relevance vector machine classifier with cuckoo search optimization for spatial prediction of landslides. J Comput Civ Eng. 30(5):04016001.
   Web of Science ®Google Scholar
• 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. 625:575–588.
   PubMed Web of Science ®Google Scholar
• Islam ARMT, Talukdar S, Mahato S, Kundu S, Eibek KU, Pham QB, Kuriqi A, Linh NTT. 2021. Flood susceptibility modelling using advanced ensemble machine learning models. Geosci Front. 12(3).
   Web of Science ®Google Scholar
• Janizadeh S, Avand M, Jaafari A, Phong TV, Bayat M, Ahmadisharaf E, Prakash I, Pham BT, Lee S. 2019. Prediction success of machine learning methods for flash flood susceptibility mapping in the Tafresh watershed, Iran. Sustainability. 11(19):5426.
   Web of Science ®Google Scholar
• Kawabata D, Bandibas J. 2009. Landslide susceptibility mapping using geological data, a DEM from ASTER images and an Artificial Neural Network (ANN). Geomorphology. 113(1–2):97–109.
   Web of Science ®Google Scholar
• Khosravi K, Pham BT, Chapi K, Shirzadi A, Shahabi H, Revhaug I, Prakash I, Bui DT. 2018. A comparative assessment of decision trees algorithms for flash flood susceptibility modeling at Haraz watershed, northern Iran. Sci Total Environ. 627:744–755.
   PubMed Web of Science ®Google Scholar
• Khosravi K, Shahabi H, Pham BT, Adamowski J, Shirzadi A, Pradhan B, Dou J, Ly H-B, Gróf G, Ho HL, et al. 2019. A comparative assessment of flood susceptibility modeling using multi-criteria decision-making analysis and machine learning methods. J Hydrol. 573:311–323.
   Web of Science ®Google Scholar
• Kim DKD, Madison TP. 2020. Public risk perception attitude and information-seeking efficacy on floods: a formative study for disaster preparation campaigns and policies. Int J Disaster Risk Sci. 11(5):592–601.
   Web of Science ®Google Scholar
• Kim P. 2017. Matlab deep learning: with machine learning, neural networks and artificial intelligence. Vol. 130. Createspace Independent Pub.
   Google Scholar
• Komi K, Neal J, Trigg MA, Diekkrüger B. 2017. Modelling of flood hazard extent in data sparse areas: a case study of the Oti River basin, West Africa. J Hydrol: Reg Stud. 10:122–132.
   Google Scholar
• Lewis NDC. 2016. Deep learning made easy with R: a gentle introduction for data science. Switzerland: AusCov.
   Google Scholar
• Lin L, Di L, Tang J, Yu E, Zhang C, Rahman M, Shrestha R, Kang L. 2019. Improvement and validation of NASA/MODIS NRT global flood mapping. Remote Sensing. 11(2):205.
   Web of Science ®Google Scholar
• Mandal S, Mondal S. 2019. Machine learning models and spatial distribution of landslide susceptibility. In: Sujit Mandal and Subrata Mondal, editors. Geoinformatics and modelling of landslide susceptibility and risk. Switzerland: Springer; p. 165–175.
   Google Scholar
• Moghaddam DD, Pourghasemi HR, Rahmati O. 2019. Assessment of the contribution of geo-environmental factors to flood inundation in a semi-arid region of SW Iran: comparison of different advanced modeling approaches. In: Hamid Reza Pourghasemi, Mauro Rossi, editors. Natural hazards GIS-based spatial modeling using data mining techniques. Switzerland: Springer; p. 59–78.
   Google Scholar
• 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. Geomat Nat Haz Risk. 8:1–23.
   Web of Science ®Google Scholar
• Nachappa TG, Piralilou ST, Gholamnia K, Ghorbanzadeh O, Rahmati O, Blaschke T. 2020. Flood susceptibility mapping with machine learning, multi-criteria decision analysis and ensemble using Dempster Shafer Theory. J Hydrol. 590:125275.
   Web of Science ®Google Scholar
• Ngo P-TT, Hoang N-D, Pradhan B, Nguyen QK, Tran XT, Nguyen QM, Nguyen VN, Samui P, Tien Bui D. 2018. A novel hybrid swarm optimized multilayer neural network for spatial prediction of flash floods in tropical areas using sentinel-1 SAR imagery and geospatial data. Sensors. 18(11):3704.
   PubMed Web of Science ®Google Scholar
• Nohani E, Moharrami M, Sharafi S, Khosravi K, Pradhan B, Pham BT, Lee S, Melesse AM. 2019. Landslide susceptibility mapping using different GIS-based bivariate models. Water. 11(7):1402.
   Web of Science ®Google Scholar
• Pal SC, Arabameri A, Blaschke T, Chowdhuri I, Saha A, Chakrabortty R, Lee S, Band S. 2020. Ensemble of machine-learning methods for predicting gully erosion susceptibility. Remote Sens. 12(22):3675.
   PubMedGoogle Scholar
• 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.
   Google Scholar
• Pham BT, Phong TV, Nguyen HD, Qi C, Al-Ansari N, Amini A, Ho LS, Tuyen TT, Yen HPH, Ly H-B, 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.
   Web of Science ®Google Scholar
• Pham QB, Yang T-C, Kuo C-M, Tseng H-W, Yu P-S. 2019. Combing random forest and least square support vector regression for improving extreme rainfall downscaling. Water. 11(3):451.
   Web of Science ®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.
   Google Scholar
• Rahmati O, Pourghasemi HR, Zeinivand H. 2016. Flood susceptibility mapping using frequency ratio and weights-of-evidence models in the Golastan Province, Iran. Geocarto Int. 31(1):42–70.
   Web of Science ®Google Scholar
• Reed BC, Brown JF, VanderZee D, Loveland TR, Merchant JW, Ohlen DO. 1994. Measuring phenological variability from satellite imagery. J Vegetation Sci. 5(5):703–714.
   Web of Science ®Google Scholar
• Rimba AB, Setiawati MD, Sambah AB, Miura F. 2017. Physical flood vulnerability mapping applying geospatial techniques in Okazaki City, Aichi Prefecture, Japan. Urban Sci. 1(1):7.
   Google Scholar
• Sahana M, Patel PP. 2019. A comparison of frequency ratio and fuzzy logic models for flood susceptibility assessment of the lower Kosi River Basin in India. Environ Earth Sci. 78(10):289.
   Web of Science ®Google Scholar
• Samanta RK, Bhunia GS, Shit PK, Pourghasemi HR. 2018. Flood susceptibility mapping using geospatial frequency ratio technique: a case study of Subarnarekha River Basin, India. Model Earth Syst Environ. 4(1):395–408.
   Web of Science ®Google Scholar
• Santos PP, Reis E, Pereira S, Santos M. 2019. A flood susceptibility model at the national scale based on multicriteria analysis. Sci Total Environ. 667:325–337.
   PubMed Web of Science ®Google Scholar
• 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.
   Web of Science ®Google Scholar
• Shahabi H, Shirzadi A, Ghaderi K, Omidvar E, Al-Ansari N, Clague JJ, Geertsema M, Khosravi K, Amini A, Bahrami S, et al. 2020. Flood detection and susceptibility mapping using sentinel-1 remote sensing data and a machine learning approach: Hybrid intelligence of bagging ensemble based on k-nearest neighbor classifier. Remote Sensing. 12(2):266.
   Web of Science ®Google Scholar
• Talukdar S, Ghose B, Salam R, Mahato S, Pham QB, Linh NTT, Costache R, Avand M. 2020. Flood susceptibility modeling in Teesta River basin, Bangladesh using novel ensembles of bagging algorithms. Stochast Environ Res Risk Assess. 34(12):2277–2300.
   Web of Science ®Google Scholar
• Tehrany MS, Pradhan B, Jebur MN. 2014. Flood susceptibility mapping using a novel ensemble weights-of-evidence and support vector machine models in GIS. J Hydrol. 512:332–343.
   Web of Science ®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.
   PubMed Web of Science ®Google Scholar
• Tien Bui D, Hoang N-D. 2017. A Bayesian framework based on a Gaussian mixture model and radial-basis-function Fisher discriminant analysis (BayGmmKda V1. 1) for spatial prediction of floods. Geosci Model Dev. 10(9):3391–3409.
   Web of Science ®Google Scholar
• Uddin K, Matin MA, Meyer FJ. 2019. Operational flood mapping using multi-temporal sentinel-1 SAR images: a case study from Bangladesh. Remote Sensing. 11(13):1581.
   Web of Science ®Google Scholar
• Wang Y, Fang Z, Wang M, Peng L, Hong H. 2020. Comparative study of landslide susceptibility mapping with different recurrent neural networks. Comput Geosci. 138:104445.
   Web of Science ®Google Scholar
• 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.
   PubMed Web of Science ®Google Scholar
• 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.
   Web of Science ®Google Scholar
• Zaharia L, Costache R, Prăvălie R, Ioana-Toroimac G. 2017. Mapping flood and flooding potential indices: a methodological approach to identifying areas susceptible to flood and flooding risk. Case study: the Prahova catchment (Romania). Front Earth Sci. 11(2):229–247.
   Web of Science ®Google Scholar