Evaluating Flood Susceptibility in the Brahmaputra River Basin: An Insight into Asia's Eastern Himalayan Floodplains Using Machine Learning and Multi-Criteria Decision-Making
Tóm tắt
Floods represent a significant threat to human life, property, and agriculture, especially in low-lying floodplains. This study assesses flood susceptibility in the Brahmaputra River basin, which spans China, India, Bhutan, and Bangladesh—an area notorious for frequent flooding due to the saturation of river water intake capacity. We developed and evaluated several innovative models for predicting flood susceptibility by employing Multi-Criteria Decision Making (MCDM) and Machine Learning (ML) techniques. The models showed robust performance, evidenced by Area Under the Receiver Operating Characteristic Curve (AUC-ROC) scores exceeding 70% and Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE) scores below 30%. Our findings indicate that approximately one-third of the studied region is categorized as moderately to highly flood-prone, while over 40% is classified as low to very low flood-risk areas. Specific regions with high to very high flood susceptibility include Dhemaji, Dibrugarh, Lakhimpur, Majuli, Darrang, Nalbari, Barpeta, Bongaigaon, and Dhubri districts in Assam; Coochbihar and Jalpaiguri districts in West Bengal; and Kurigram, Gaibandha, Bogra, Sirajganj, Pabna, Jamalpur, and Manikganj districts in Bangladesh. Owing to their strong performance and the suitability of the training datasets, we recommend the application of the developed MCDM techniques and ML algorithms in geographically similar areas. This study holds significant implications for policymakers, regional administrators, environmentalists, and engineers by informing flood management and prevention strategies, serving as a climate change adaptive response within the Brahmaputra River basin.
Tài liệu tham khảo
Ahmed I, Das N, Debnath J, Bhowmik M (2018) Erosion induced channel migration and its impact on dwellers in the lower Gumti River, Tripura, India. Spat Inform Res 26:537–549
Ahmed IA, Talukdar S, Parvez A, MohdRihan Baig MRI, Rahman A (2022) Flood susceptibility modeling in the urban watershed of Guwahati using improved metaheuristic-based ensemble machine learning algorithms. Geocarto Int. https://doi.org/10.1080/10106049.2022.2066200
Akay H (2021) Flood hazards susceptibility mapping using statistical, fuzzy logic, and MCDM methods. Soft Comput 25:9325–9346. https://doi.org/10.1007/s00500-021-05903-1
Ali SA, Parvin F, Pham QB, Vojtek M, Vojteková J, Costache R, Linh NTT, Nguyen HQ, Ahmad A, Ghorbani MA (2020) 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
Altaf F, Meraj G, Romshoo SA (2013) Morphometric analysis to infer hydrological behaviour of Lidder watershed, Western Himalaya, India. Geogr J 2013:178021
Balogun A, Sheng TY, Sallehuddin MH, Aina YA, Dano UL, Pradhan B, Yekeen S, Tella A (2022) Assessment of data mining, multi-criteria decision making and fuzzy-computing techniques for spatial flood susceptibility mapping: a comparative study. Geocarto Int 37(26):12989–13015. https://doi.org/10.1080/10106049.2022.2076910
Bera A, Meraj G, Kanga S, Farooq M, Singh SK, Sahu N, Kumar P (2022) Vulnerability and risk assessment to climate change in Sagar Island, India. Water 14(5):823
Bhattachaiyya NN, Bora AK (1997) Floods of the Brahmaputra River in India. Water Int 22(4):222–229. https://doi.org/10.1080/02508069708686709
Bhowmik M, Das C, Ahmed I, Debnath J (2018) Bank material characteristics and its impact on river bank erosion, West Tripura district, Tripura. North-East India Curr Sci 115(8):1571–1576
Borah L, Kalita B, Boro P, Kulnu AS, Hazarika N (2022) Climate change impacts on socio-hydrological spaces of the Brahmaputra floodplain in Assam Northeast India: a review. Front Water 4:913840. https://doi.org/10.3389/frwa.2022.913840
Breiman L (2001) Random forests. Mach Learn 45(5–32):1200S
Bubeck P, Botzen WJ, Aerts JC (2012) A review of risk perceptions and other factors that influence flood mitigation behavior. Risk Anal 32(9):14811495
Bui DT, Tsangaratos P, Ngo PTT, 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
Cetin M (2013) Landscape engineering, protecting soil, and runoff storm water. In Advances in landscape architecture, Intech Open
Cetin M (2016a) Determination of bioclimatic comfort areas in landscape planning: a case study of Cide Coastline. Turk J Agric-Food Sci Technol 4:800–804. https://doi.org/10.24925/turjaf.v4i9.800-804.872
Cetin M (2016b) Sustainability of urban coastal area management: A case study on Cide. J Sustain for 35(7):527–541
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
Chau K, Wu C, Li Y (2005) Comparison of several flood forecasting models in Yangtze River. J Hydraul Eng 10(6):485–491
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
Das S, Gupta A (2021) Multi-criteria decision based geospatial mapping of flood susceptibility and temporal hydro-geomorphic changes in the Subarnarekha basin. India Geosci Front 12(5):101206
Dash P, Sar J (2020) Identification and validation of potential flood hazard area using GIS-based multi-criteria analysis and satellite data-derived water index. J Flood Risk Manag 13:e12620.Sw
Debnath J, Pan ND, Ahmed I, Bhowmik M (2017) Channel migration and its impact on land use/land cover using RS and GIS: A study on Khowai River of Tripura, North-East India. Egypt J Remote Sens Space Sci 20(2):197–210
Debnath J, Meraj G, Das Pan N, Chand K, Debbarma S, Sahariah D, Kumar P (2022a) Integrated remote sensing and field-based approach to assess the temporal evolution and future projection of meanders: a case study on River Manu in North-Eastern India. PLoS ONE 17(7):e0271190
Debnath J, Sahariah D, Lahon D, Nath N, Chand K, Meraj G, Singh SK (2022b) Geospatial modeling to assess the past and future land use-land cover changes in the Brahmaputra Valley, NE India, for sustainable land resource management. Environ Sci Pollut Res. https://doi.org/10.1007/s11356-022-24248-2
Debnath J, Sahariah D, Saikia A, Meraj G, Nath N, Lahon D, Kanga S (2023a) Shifting sands: assessing bankline shift using an automated approach in the Jia Bharali river India. Land 12(3):703
Debnath J, Sahariah D, Lahon D, Nath N, Chand K, Meraj G, Farooq M (2023b) Assessing the impacts of current and future changes of the planforms of river Brahmaputra on its land use-land cover. Geosci Front 14(4):101557
Degiorgis M, Gnecco G, Gorni S, Roth G, Sanguineti M, Celeste Taramasso A (2012) Classifiers for the detection of flood-prone areas using remote sensed elevation data. J Hydrol 470–471:302–315. https://doi.org/10.1016/j.jhydrol.2012.09.006
Devrani R, Srivastava P, Kumar R, Kasana P (2021) Characterization and assessment of flood inundated areas of lower Brahmaputra River Basin using multitemporal Synthetic Aperture Radar data: a case study from NE India. Geol J 57(2):622–646
Duckstein L, Opricovic S (1980) Multiobjective optimization in river basin development. Water Resour Res 16(1):14–20. https://doi.org/10.1029/WR016i001p00014
Duque EL, Aquino PT (2019) Anthropometric analysis in automotive manual transmission gearshift quality perception. CTI Symp 2018:97–109
El-Magd SA (2022) Random forest and naïve Bayes approaches as tools for flash flood hazard susceptibility prediction, South Ras El-Zait, Gulf of Suez Coast. Egypt Arab J Geosci 15(3):1–12
Fayaz M, Meraj G, Khader SA, Farooq M, Kanga S, Singh SK, Kumar P, Sahu N (2022) Management of landslides in a rural–urban transition zone using machine learning algorithms—a case study of a national highway (NH-44), India, in the Rugged Himalayan Terrains. Land 11(6):884
Ghorabaee MK, Amiri M, Sadaghiani JS, Zavadskas EK (2015) Multicriteria project selection using an extended VIKOR method with interval type-2 fuzzy sets. Int J Inf Technol Decis Mak 14(5):993–1016. https://doi.org/10.1142/S0219622015500212
Ghorabaee MK, Amiri M, Zavadskas EK, Turskis Z, Antucheviciene J (2017) A new multi-criteria model based on interval type-2 fuzzy sets and EDAS method for supplier evaluation and order allocation with environmental considerations. Comput Ind Eng 112:156–174. https://doi.org/10.1016/j.cie.2017.08.017
Ghosh A, Dey P (2021) Flood severity assessment of the coastal tract situated between Muriganga and Saptamukhi estuaries of Sundarban delta of India using Frequency Ratio (FR), Fuzzy Logic (FL), Logistic Regression (LR) and Random Forest (RF) models. Reg Stud Mar Sci 42:101624. https://doi.org/10.1016/j.rsma.2021.101624
Goswami G, Prasad RK, Kumar D (2023) Hydrodynamic flood modeling of Dikrong River in Arunachal Pradesh, India: a simplified approach using HEC-RAS 6.1. Model Earth Syst Environ 9:331–345. https://doi.org/10.1007/s40808-022-01507-2
Gupta L, Dixit J (2022) A GIS-based flood risk mapping of Assam, India, using the MCDA-AHP approach at the regional and administrative level. Geocarto Int. https://doi.org/10.1080/10106049.2022.2060329
Hadian S, Afzalimehr H, Soltani N, ShahiriTabarestani E, Pham QB (2022) Application of MCDM methods for flood susceptibility assessment and evaluation the impacts of past experiences on flood preparedness. Geocart Inter 37(27):16283–16306
Haque A, Nicholls RJ (2018) Floods and the Ganges-Brahmaputra-Meghna Delta. In: Nicholls R, Hutton C, Adger W, Hanson S, Rahman M, Salehin M (eds) Ecosystem services for well-being in Deltas. Palgrave Macmillan, Cham, pp 147–159
Haque CE, Azad MAK, Choudhury MUI (2019) Discourse of flood management approaches and policies in bangladesh: mapping the changes, drivers, and actors. Water 11:2654. https://doi.org/10.3390/w11122654
Haque MM, Islam S, Sikder MB et al (2023) Assessment of flood vulnerability in Jamuna floodplain: a case study in Jamalpur district, Bangladesh. Nat Hazards 116:341–363. https://doi.org/10.1007/s11069-022-05677-1
Hazarika N, Barman D, Das AK, Sarma AK, Borah SB (2018) Assessing and mapping flood hazard, vulnerability and risk in the Upper Brahmaputra River valley using stakeholders’ knowledge and multicriteria evaluation (MCE). J Flood Risk Management 11:S700–S716. https://doi.org/10.1111/jfr3.12237
Ho TK (1995) Random decision forests. Proceedings of 3rd International Conference on Document Analysis and Rec-ognition. New York (NY): IEEE
Huq M, Bracken L (2015) From risk to opportunity: Climate change and flood Policy in Bangladesh. In: Filho WL (ed) Handbook of climate change adaptation. Springer, Germany, pp 1023–1043
Hwang CL, Yoon K (1981) Methods for multiple attribute decision making. multiple attribute decision making. Springer, Berlin, Heidelberg, pp 58–191. 10. 1007/978-3- 642- 48318-9_3
IPCC (2018) IPCC special report on the impacts of global warming of 1.5 °C - Summaryfor policymakers (Issue October 2018). http://www.ipcc.ch/report/sr15/
Islam AKM, Paul S, Mohammed K, Billah M, Fahad M, Rabbani G et al (2018) Hydrological response to climate change of the Brahmaputra basin using CMIP5 general circulation model ensemble. J Water Clim Change 9:434–448. https://doi.org/10.2166/wcc.2017.076
Islam S, Tahir M, Parveen S (2022) GIS-based flood susceptibility mapping of the lower Bagmati basin in Bihar, using Shannon’s entropy model. Model Earth Syst Environ 8:3005–3019. https://doi.org/10.1007/s40808-021-01283-5
Jahangir MH, Mousavi Reineh SM, Abolghasemi M (2019) Spatial predication of flood zonation mapping in Kan River Basin, Iran, using artificial neural network algorithm. Weather Clim Extrem 25:100215. https://doi.org/10.1016/j.wace.2019.100215
Jenks GF (1967) The data model concept in statistical mapping. Int Year Book Cartogr 7:186–190
Jongman B (2018) Effective adaptation to rising flood risk. Nat Commun 9:1986. https://doi.org/10.1038/s41467-018-04396-1
Kamal R, Matin MA, Nasreen S (2013) Response of river flow regime to various climate change scenarios in Ganges-Brahmaputra-Meghna basin. J Water Resour Ocean Sci 2:15–24. https://doi.org/10.11648/j.wros.20130202.12
Kazakis N, Voudouris KS (2015) Groundwater vulnerability and pollution risk assessment of porous aquifers to nitrate: modifying the drastic method using quantitative parameters. J Hydrol 525:13–25
Khosravi K, Pham BT, Chapi K, Shirzadi A, Shahabi H, Revhaug I, Prakash I, Tien Bui D (2018) A comparative assessment of decision trees algorithms for flash flood susceptibility modeling at Haraz watershed, northern Iran. Sci Total Environ 627:744–755. https://doi.org/10.1016/j.scitotenv.2018.01.266
Khosravi K, Shahabi H, Pham BT, Adamowski J, Shirzadi A, Pradhan B, Dou J, Ly HB, Grof G, Ho HL (2019) A comparative assessment of flood susceptibility modelling using multi-criteria decision-making analysis and machine learning methods. J Hydrol 573:311–323
Kumar A, Mondal S, Lal P (2022) Analyzing frequent extreme flood incidences in Brahmaputra basin. South Asia Plos ONE 17(8):e0273384. https://doi.org/10.1371/journal.pone.0273384
Liu Y, Cai W, Sun C, Song H, Cobb KM, Li J, Leavitt SW, Wu L, Cai Q, Liu R, Ng B (2019) Anthropogenic aerosols cause recent pronounced weakening of Asian Summer Monsoon relative to last four centuries. Geophys Res Lett 46(10):5469–5479
López PL, Sultana T, Kafi MAH et al (2020) Evaluation of global water resources reanalysis data for estimating flood events in the Brahmaputra River Basin. Water Resour Manage 34:2201–2220. https://doi.org/10.1007/s11269-020-02546-z
Meraj G (2021) Assessing the impacts of climate change on ecosystem service provisioning in Kashmir valley India. PhD thesis, Suresh Gyan Vihar University. http://hdl.handle.net/10603/354338
Meraj G, Singh SK, Kanga S, Islam MN (2022a) Modeling on comparison of ecosystem services concepts, tools, methods and their ecological-economic implications: a review. Model Earth Syst Environ 8:15–34
Meraj G, Farooq M, Singh SK, Islam MN, Kanga S (2022b) Modeling the sediment retention and ecosystem provisioning services in the Kashmir valley, India, Western Himalayas. Model Earth Syst Environ 8(3):3859–3884
Mitra R, Das J (2023) A comparative assessment of flood susceptibility modelling of GIS-based TOPSIS, VIKOR, and EDAS techniques in the Sub-Himalayan foothills region of Eastern India. Environ Sci Pollut Res. https://doi.org/10.1007/s11356-022-23168-5
Mitra R, Saha P, Das J (2022) Assessment of the performance of GIS-based analytical hierarchical process (AHP) approach for flood modelling in Uttar Dinajpur district of West Bengal. India Geomat Nat Hazards Risk 13(1):2183–2226. https://doi.org/10.1080/19475705.2022.2112094
Mousavi SM, Ataie-Ashtiani B, Hosseini SM (2022) Comparison of statistical and mcdm approaches for flood susceptibility mapping in northern iran. J Hydrol 612:128072
Msabi MM, Makonyo M (2021) Flood susceptibility map-ping using GIS and multi-criteria decision analysis: a case ofDodoma region, central Tanzania. Remote Sens Appl-Tions Soc Environ 21:100445. https://doi.org/10.1016/j.rsase.2020.100445
Nachappa TG, Piralilou ST, Ghorbanzadeh GK, O, Rahmati O, Blaschke T, (2020) Flood susceptibility mapping withmachine learning, multi-criteria decision analysis and ensembleusing Dempster Shafer Theory. J Hydrol. https://doi.org/10.1016/j.jhydrol.2020.125275
Nahin KTK, Islam SB, Mahmud S, Hossain I (2023) Flood vulnerability assessment in the Jamuna river floodplain using multi-criteria decision analysis: A case study in Jamalpur district Bangladesh. Heliyon 9:e14520
Negese A, Worku D, Shitaye A et al (2022) Potential flood-prone area identification and mapping using GIS-based multi-criteria decision-making and analytical hierarchy process in DegaDamot district, northwestern Ethiopia. Appl Water Sci 12:255. https://doi.org/10.1007/s13201-022-01772-7
Nepal S, Shrestha AB (2015) Impact of climate change on the hydrological regime of the Indus, Ganges and Brahmaputra River basins: a review of the literature. Int J Water Resour Dev 31:201–218. https://doi.org/10.1080/07900627.2015.1030494
Nie Y, Liu W, Liu Q, Hu X, Westoby MJ (2020) Reconstructing the ChongbaxiaTsho glacial lake outburst flood in the EasternHimalaya: Evolution, process and impacts. Geomorphology 370:107393
Nsangou D, Kpoumié A, Mfonka Z, Ngouh AN, Fossi DH, Jourdan C, Ngoupayou JRN (2022) Urban flood susceptibility modelling using AHP and GIS approach: case of the Mfoundi watershed at Yaoundé in the South-Cameroon plateau. Sci Afr 15:e01043
Opricovic S, Tzeng GH (2004) Compromise solution by MCDM methods: a comparative analysis of VIKOR and TOPSIS. Eur J Oper Res 156(2):445–455. https://doi.org/10.1016/S0377-2217(03)00020-1
Pareta K (2021) Multi-criteria analysis (MCA) for identification of vulnerable areas along Brahmaputra River in Assam and their field assessment. J Environ Protect Sustain Dev 7(2):15–29
Pathan AI, Girish Agnihotri P, Said S, Patel D (2022) AHP and TOPSIS based flood risk assessment-a case study of the Navsari City, Gujarat, India. Environ Monit Assess 194(7):509. https://doi.org/10.1007/s10661-022-10111-x
Pham BT, Jaafari A, Van Phong T, Yen HP, Tuyen TT, Van Luong V, Nguyen HD, Van Le H, Foong LK (2021) Improved flood susceptibility mapping using a best first decision tree integrated with ensemble learning techniques. Geosci Front 12(3):101105. https://doi.org/10.1016/J.GSF.2020.11.003
Pourghasemi HR, GoliJirandeh A, Pradhan B, Xu C, Gokceoglu C (2013) Landslide susceptibility mapping using support vector machine and GIS at the Golestan Province. Iran J Earth Sys Sci 122(2):349–369
Pradhan B, Lee S (2010) Delineation of landslide hazard areas on Penang Island, Malaysia, by using frequency ratio, logistic regression, and artificial neural network models. Environ Earth Sci 60:1037–1054
Pradhan B, Youssef AM (2010) Manifestation of remote sensing data and GIS on landslide hazard analysis using spatial-based statistical models. Arab J Geosci 3(3):319–326. https://doi.org/10.1007/s12517-009-0089-2
Rahman M, Ningsheng C, Islam MM et al (2019) Flood susceptibility assessment in Bangladesh using machine learning and multi-criteria decision analysis. Earth Syst Environ 3:585–601. https://doi.org/10.1007/s41748-019-00123-y
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 31:42–70
Rakib MA, Islam S, Nikolaos I, Bodrud-Doza M, Bhuiyan MA (2017) Flood vulnerability, local perception and gender role judgment using multivariate analysis: a problem-based “participatory action to Future Skill Management” to cope with flood impacts. Weather Clim Extremes 18:29–43
Rao MP, Cook ER, Cook BI et al (2020) Seven centuries of reconstructed Brahmaputra River discharge demonstrate underestimated high discharge and flood hazard frequency. Nat Commun 11:6017
Ravindranath N, Rao S, Sharma N, Nair M, Gopalakrishnan R, Rao AS, Malaviya S, Tiwari R, Sagadevan A, Munsi M (2011) Climate change vulnerability profiles for North-East India. Curr Sci 101:384–404
Roy S, Bose A, Mandal G (2022) Modeling and mapping geospatial distribution of groundwater potential zones in Darjeeling Himalayan region of India using analytical hierarchy process and GIS technique. Model Earth Syst Environ 8:1563–1584. https://doi.org/10.1007/s40808-021-01174-9
Saaty TL (1980) The analytical hierarchy process. McGraw-Hill, New York
Sahin G, Cabuk SN, Cetin M (2022) The change detection in coastal settlements using image processing techniques: a case study of Korfez. Environ Sci Pollut Res 29(10):15172–15187
Saikh NI, Mondal P (2023) Gis-based machine learning algorithm for flood susceptibility analysis in the Pagla river basin, Eastern India. Nat Hazard Res. https://doi.org/10.1016/j.nhres.2023.05.004.
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
Shawki D, Voulgarakis A, Chakraborty A, Kasoar M, Srinivasan J (2018) The South Asian monsoon response to remote aerosols: global and regional mechanisms. J Geophys Res Atmos 123:511–585. https://doi.org/10.1029/2018JD028623
Shivaprasad Sharma SV, Roy PS, Chakravarthi V, Srinivasa Rao G (2018) Flood risk assessment using multi-criteria analysis: a case study from Kopili River Basin, Assam, India. Geomat Nat Hazards Risk 9(1):79–93. https://doi.org/10.1080/19475705.2017.1408705
Solaimani K, Shokrian F, Darvishi S (2023) An assessment of the integrated multi-criteria and new models efficiency in watershed flood mapping. Water Resour Manage 37:403–425. https://doi.org/10.1007/s11269-022-03380-1
Souissi D, Zouhri L, Hammami S, Msaddek MH, Zghibi A, Dlala M (2020) GIS-based MCDM–AHP modeling for flood susceptibility mapping of arid areas, southeastern Tunisia. Geocart Int 35(9):991–1017
Rather MA, Meraj G, Farooq M, Shiekh BA, Kumar P, Kanga S, Singh SK, Sahu N, Tiwari SP (2022) Identifying the potential dam sites to avert the risk of catastrophic floods in the Jhelum basin, Kashmir, NW Himalaya, India. Remote Sens 14(7):1538. https://doi.org/10.3390/rs14071538
Tempa K (2022) District flood vulnerability assessment using analytic hierarchy process (AHP) with historical flood events in Bhutan. PLoS ONE 17(6):e0270467. https://doi.org/10.1371/journal.pone.0270467
Vapnik VN (1995) The Nature of Statistical Learning Theory. Springer Verlag, New York
Varmazyar M, Dehghanbaghi M, Afkhami M (2016) A novel hybrid MCDM model for performance evaluation of research and technology organizations based on BSC approach. Eval Program Plann 58:125–140. https://doi.org/10.1016/j.evalprogplan.2016.06.005
WHO (2003) World disasters report, Chapter 8: disaster data: key trends and statistics. http://www.ifrc.org/PageFiles/89755/2003/43800-WDR2003_En.pdf
Zavadskas EK, Turskis Z, Antucheviciene J, Zakarevicius A (2012) Optimization of weighted aggregated sum product assessment. Elektron Elektrotech 122(6):3–6. https://doi.org/10.5755/j01.eee.122.6.1810
Zavadskas EK, Kalibatas D, Kalibatiene D (2016) A multi-attribute assessment using WASPAS for choosing an optimal indoor environment. Arch Civ Mech Eng 16:76–85. https://doi.org/10.1016/j.acme.2015.10.002
Zhang C, Ma YQ (2012) Ensemble Machine Learning: Methods and Applications. Springer, Verlag, New York. https://doi.org/10.1007/978-1-4419-9326-7.