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Research Articles

Examining LightGBM and CatBoost models for wadi flash flood susceptibility prediction

ORCID Icon, ORCID Icon, ORCID Icon, , ORCID Icon, ORCID Icon, ORCID Icon, & ORCID Icon show all
Pages 7462-7487 | Received 25 May 2021, Accepted 26 Aug 2021, Published online: 27 Sep 2021

References

  • Abdel-Fattah M, Kantoush SA, Saber M, Sumi T. 2018. Rainfall-runoff modeling for extreme flash floods in wadi Samail, Oman. J Jsce, Ser B1. 74(5):I_691–I_696.
  • Abdel-Fattah M, Kantoush S, Sumi T. 2015. Integrated management of flash flood in wadi system of egypt: disaster prevention and water harvesting. 京都大学防災研究所年報. B= Disaster Prevention Research Institute Annuals. B, 58(B):485–496.
  • Abdelkader MM, Al-Amoud AI, El Alfy M, El-Feky A, Saber M. 2021. Assessment of flash flood hazard based on morphometric aspects and rainfall-runoff modeling in Wadi Nisah, Central Saudi Arabia. Remote Sens Appl: Soc Environ. 23:100562.
  • Abdrabo KI, Kantoush SA, Saber M, Sumi T, Habiba OM, Elleithy D, Elboshy B. 2020. Integrated methodology for urban flood risk mapping at the microscale in ungauged regions: a case study of Hurghada, Egypt. Remote Sensing. 12(21):3548.
  • Abdulwahab Aboualnaga. 2014. The Zafarana-Hurghada road was closed due to the floods. Mobtda [Internet]. https://www.mobtada.com/details/186604.
  • Abushandi EH, Merkel BJ. 2011. Application of IHACRES rainfall-runoff model to the Wadi Dhuliel arid catchment, Jordan. J Water Climate Change. 2(1):56–71.
  • Achour Y, Pourghasemi HR. 2020. How do machine learning techniques help in increasing accuracy of landslide susceptibility maps? Geosci Front. 11(3):871–883.
  • 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.
  • Ahmed A, Hewa G, Alrajhi A. 2021. Flood susceptibility mapping using a geomorphometric approach in South Australian basins. Nat Hazards. 106(1):629–653.
  • 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.
  • 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.
  • Apollonio C, Balacco G, Novelli A, Tarantino E, Piccinni AF. 2016. Land use change impact on flooding areas: the case study of Cervaro Basin (Italy). Sustainability. 8(10):996.
  • Arabameri A, Saha S, Mukherjee K, Blaschke T, Chen W, Ngo PTT, Band SS. 2020. Modeling spatial flood using novel ensemble artificial intelligence approaches in Northern Iran. Remote Sensing. 12(20):3423.
  • Arora A, Arabameri A, Pandey M, Siddiqui MA, Shukla UK, Bui DT, Mishra VN, Bhardwaj A. 2021. Optimization of state-of-the-art fuzzy-metaheuristic ANFIS-based machine learning models for flood susceptibility prediction mapping in the Middle Ganga Plain, India. Sci Total Environ. 750:141565.
  • Arora A, Pandey M, Siddiqui MA, Hong H, Mishra VN. 2019. Spatial flood susceptibility prediction in Middle Ganga Plain: comparison of frequency ratio and Shannon’s entropy models. Geocarto International. :1–32.
  • Aryal SK, Mein RG, O'Loughlin EM. 2003. The concept of effective length in hillslopes: assessing the influence of climate and topography on the contributing areas of catchments. Hydrol Process. 17(1):131–151.
  • Bachmair S, Svensson C, Prosdocimi I, Hannaford J, Stahl K. 2017. Developing drought impact functions for drought risk management. Nat Hazards Earth Syst Sci. 17(11):1947–1960.
  • 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.
  • Bellu A, Fernandes LFS, Cortes RM, Pacheco FA. 2016. A framework model for the dimensioning and allocation of a detention basin system: the case of a flood-prone mountainous watershed. J Hydrol. 533:567–580.
  • Beven KJ, Kirkby MJ. 1979. A physically based, variable contributing area model of basin hydrology/Un modèle à base physique de zone d’appel variable de l’hydrologie du bassin versant. Hydrol Sci J. 24(1):43–69.
  • Bisht S, Chaudhry S, Sharma S, Soni S. 2018. Assessment of flash flood vulnerability zonation through Geospatial technique in high altitude Himalayan watershed, Himachal Pradesh India. Remote Sens Appl: Soc Environ. 12:35–47.
  • Breiman L. 2001. Random forests. Mach Learn. 45(1):5–32.
  • Bui DT, Hoang N-D, Pham T-D, Ngo P-TT, Hoa PV, Minh NQ, Tran X-T, Samui P. 2019. A new intelligence approach based on GIS-based multivariate adaptive regression splines and metaheuristic optimization for predicting flash flood susceptible areas at high-frequency tropical typhoon area. J Hydrol. 575:314–326.
  • Bui DT, Pradhan B, Lofman O, Revhaug I, Dick OB. 2012. Spatial prediction of landslide hazards in Hoa Binh province (Vietnam): a comparative assessment of the efficacy of evidential belief functions and fuzzy logic models. Catena. 96:28–40.
  • Bui DT, Panahi M, Shahabi H, Singh VP, Shirzadi A, Chapi K, Khosravi K, Chen W, Panahi S, Li S, et al. 2018. Novel hybrid evolutionary algorithms for spatial prediction of floods. Sci Rep. 8(1):15364–14.
  • Cao C, Xu P, Wang Y, Chen J, Zheng L, Niu C. 2016. Flash flood hazard susceptibility mapping using frequency ratio and statistical index methods in coalmine subsidence areas. Sustainability. 8(9):948.
  • Cardenas MB, Wilson J, Zlotnik VA. 2004. Impact of heterogeneity, bed forms, and stream curvature on subchannel hyporheic exchange. Water Resour Res. 40(8):1–14, W08307. https://doi.org/10.1029/2004WR003008.
  • 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.
  • Chen T, Guestrin C. 2016. Xgboost: a scalable tree boosting system. KDD '16: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 2016; p. 785–794. https://doi.org/10.1145/2939672.2939785
  • Chen T-HK, Qiu C, Schmitt M, Zhu XX, Sabel CE, Prishchepov AV. 2020. Mapping horizontal and vertical urban densification in Denmark with Landsat time-series from 1985 to 2018: a semantic segmentation solution. Remote Sens Environ. 251:112096.
  • Chen C, Zhang Q, Ma Q, Yu B. 2019. LightGBM-PPI: predicting protein-protein interactions through LightGBM with multi-information fusion. Chemomet Intell Lab Syst. 191:54–64.
  • Chen W, Zhang S, Li R, Shahabi H. 2018. Performance evaluation of the GIS-based data mining techniques of best-first decision tree, random forest, and naïve Bayes tree for landslide susceptibility modeling. Sci Total Environ. 644:1006–1018.
  • 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. 651:2087–2096.
  • Chourushi S, Lodha P, Prakash I. 2019. A critical review of hydrological modeling practices for flood management. Pramana Res J. 9:352–362.
  • Climate Hurguada. 2021. Tutiempo Network, SL. [accessed 2021 Mar 3]. https://en.tutiempo.net/climate/ws-624630.html. Tutiempo Network, S.L.
  • 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.
  • Costache R, Popa MC, T, Bui D, Diaconu DC, Ciubotaru N, Minea G, Pham QB. 2020. Spatial predicting of flood potential areas using novel hybridizations of fuzzy decision-making, bivariate statistics, and machine learning. J Hydrol. 585:124808.
  • Courtesy Abdelhameed. 2016. In pictures: Heavy rains hit Egypt’s Red Sea governorate. Al Arabiya News. https://english.alarabiya.net/News/middle-east/2016/10/27/In-pictures-Heavy-rains-hit-Egypt-s-Red-Sea-governorate.
  • 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.
  • Devkota KC, Regmi AD, Pourghasemi HR, Yoshida K, Pradhan B, Ryu IC, Dhital MR, Althuwaynee OF. 2013. Landslide susceptibility mapping using certainty factor, index of entropy and logistic regression models in GIS and their comparison at Mugling–Narayanghat road section in Nepal Himalaya. Nat Hazards. 65(1):135–165.
  • Dodangeh E, Choubin B, Eigdir AN, Nabipour N, Panahi M, Shamshirband S, Mosavi A. 2020. Integrated machine learning methods with resampling algorithms for flood susceptibility prediction. Sci Total Environ. 705:135983. https://doi.org/10.1016/j.scitotenv.2019.135983.
  • Dorogush AV, Ershov V, Gulin A. 2018. CatBoost: gradient boosting with categorical features support. Cornell University. 1–7. arXiv preprint arXiv:181011363.
  • 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.
  • Earth Resources Observation and Science (EROS) Center. 2021. USGS EROS Archive - Vegetation Monitoring - EROS Moderate Resolution Imaging Spectroradiometer (eMODIS). USGS [Internet]. https://www.usgs.gov/centers/eros/science/usgs-eros-archive-vegetation-monitoring-eros-moderate-resolution-imaging?qt-science_center_objects=0#qt-science.
  • El-Haddad BA, Youssef AM, Pourghasemi HR, Pradhan B, El-Shater A-H, El-Khashab MH. 2020. Flood susceptibility prediction using four machine learning techniques and comparison of their performance at Wadi Qena Basin, Egypt. Nat Hazards. 105(1):83–114. https://doi.org/10.1007/s11069-020-04296-y
  • Elsadek WM, Ibrahim MG, Mahmod WE, Kanae S. 2019. Developing an overall assessment map for flood hazard on large area watershed using multi-method approach: case study of Wadi Qena watershed. Nat Hazards. 95(3):739–767.
  • Esfandiari M, Jabari S, McGrath H, Coleman D. 2020. Flood mapping using random forest and identifying the essential conditioning factors; a case study in fredericton, New Brunswick, Canada. Ann Photogrammet, Remote Sens Spat Inf Sci. 5(3):1–7.
  • Fan J, Ma X, Wu L, Zhang F, Yu X, Zeng W. 2019. Light Gradient Boosting Machine: an efficient soft computing model for estimating daily reference evapotranspiration with local and external meteorological data. Agric Water Manag. 225:105758.
  • Farhan Y, Anaba O. 2016. Flash flood risk estimation of Wadi Yutum (Southern Jordan) watershed using GIS based morphometric analysis and remote sensing techniques. OJMH. 06(02):79–100.
  • Fawcett R, Stone R. 2010. A comparison of two seasonal rainfall forecasting systems for Australia. Aust Meteorol Oceanogr J. 60(1):15–24.
  • Fenicia F, Savenije HH, Matgen P, Pfister L. 2008. Understanding catchment behavior through stepwise model concept improvement. Water Resour Res. 44(1):1–13, W01402. https://doi.org/10.1029/2006WR005563.
  • Friedman JH. 2002. Stochastic gradient boosting. Comput Stat Data Anal. 38(4):367–378.
  • Ge X, Sun J, Lu B, Chen Q, Xun W, Jin Y. 2019. Classification of oolong tea varieties based on hyperspectral imaging technology and BOSS‐LightGBM model. J Food Process Eng. 42(8):e13289.
  • Geospatial Information Authority of Japan 2021. Global Map Global version. Geospatial Information Authority of Japan. https://www.gsi.go.jp/kankyochiri/gm_global_e.html.
  • Glenn EP, Morino K, Nagler PL, Murray RS, Pearlstein S, Hultine KR. 2012. Roles of saltcedar (Tamarix spp.) and capillary rise in salinizing a non-flooding terrace on a flow-regulated desert river. J Arid Environ. 79:56–65.
  • Gong R, Fonseca E, Bogdanov D, Slizovskaia O, Gomez E, Serra X. 2017. Acoustic scene classification by fusing LightGBM and VGG-net multichannel predictions. Detection and Classification of Acoustic Scenes and Events 2017, Munich, Germany; p. 1–4.
  • González-Arqueros ML, Mendoza ME, Bocco G, Castillo BS. 2018. Flood susceptibility in rural settlements in remote zones: the case of a mountainous basin in the Sierra-Costa region of Michoacán, Mexico. J Environ Manage. 223:685–693.
  • Hancock JT, Khoshgoftaar TM. 2020. CatBoost for big data: an interdisciplinary review. J Big Data. 7(1):94–45.
  • Hirabayashi Y, Mahendran R, Koirala S, Konoshima L, Yamazaki D, Watanabe S, Kim H, Kanae S. 2013. Global flood risk under climate change. Nature Clim Change. 3(9):816–821.
  • Hölting B, Coldewey WG. 2019. Surface water infiltration. In: Hydrogeology. Springer Textbooks in Earth Sciences, Geography and Environment. Berlin (Heidelberg): Springer; p. 33–37. https://doi.org/10.1007/978-3-662-56375-5_5
  • 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.
  • Huang G, Wu L, Ma X, Zhang W, Fan J, Yu X, Zeng W, Zhou H. 2019. Evaluation of CatBoost method for prediction of reference evapotranspiration in humid regions. J Hydrol. 574:1029–1041.
  • 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. https://doi.org/10.3390/su11195426.
  • Jhaveri S, Khedkar I, Kantharia Y, Jaswal S. 2019. Success prediction using random forest, catboost, xgboost and adaboost for kickstarter campaigns. 2019 3rd International Conference on Computing Methodologies and Communication (ICCMC): IEEE Xplore Part Number: CFP19K25-ART; p. 1170–1173.
  • Jihad Alansary 2014. Hurghada road collapse due to floods. Misr News. https://masralarabia.net/%D8%A7%D8%AE%D8%A8%D8%A7%D8%B1-%D9%85%D8%B5%D8%B1/230487-%D9%81%D9%8A%D8%AF%D9%8A%D9%88-%D8%A7%D9%86%D9%87%D9%8A%D8%A7%D8%B1-%D8%A8%D8%B7%D8%B1%D9%8A%D9%82-%D8%A7%D9%84%D8%BA%D8%B1%D8%AF%D9%82%D8%A9-%D8%A8%D8%B3%D8%A8%D8%A8-%D8%A7%D9%84%D8%B3%D9%8A%D9%88%D9%84.
  • Ju Y, Sun G, Chen Q, Zhang M, Zhu H, Rehman MU. 2019. A model combining convolutional neural network and LightGBM algorithm for ultra-short-term wind power forecasting. IEEE Access. 7:28309–28318.
  • Kazakis N, Kougias I, Patsialis T. 2015. Assessment of flood hazard areas at a regional scale using an index-based approach and Analytical Hierarchy Process: Application in Rhodope–Evros region, Greece. Sci Total Environ. 538:555–563.
  • Ke G, Meng Q, Finley T, Wang T, Chen W, Ma W, Ye Q, Liu T-Y. 2017. Lightgbm: a highly efficient gradient boosting decision tree. Advances in Neural Information Processing Systems. 30:3146–3154.
  • Khosravi K, Nohani E, Maroufinia E, Pourghasemi HR. 2016. 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(2):947–987.
  • 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.
  • 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(1):251–264.
  • Kobayashi T, Tateishi R, Alsaaideh B, Sharma RC, Wakaizumi T, Miyamoto D, Bai X, Long BD, Gegentana G, Maitiniyazi A. 2017. Production of global land cover data - GLCNMO2013. J Geogr Geology. 9(3):1–15. https://doi.org/10.5539/jgg.v9n3p1
  • Kopecký M, Čížková Š. 2010. Using topographic wetness index in vegetation ecology: does the algorithm matter? Appl Veg Sci. 13(4):450–459.
  • Kumar R, Singh R, Gautam H, Pandey MK. 2018. Flood hazard assessment of August 20, 2016 floods in Satna district, Madhya Pradesh, India. Remote Sens Appl: Soc Environ. 11:104–118.
  • Kusiak A, Zheng H, Song Z. 2009. Models for monitoring wind farm power. Renew Energy. 34(3):583–590.
  • Lee S, Kim J-C, Jung H-S, Lee MJ, Lee S. 2017. Spatial prediction of flood susceptibility using random-forest and boosted-tree models in Seoul metropolitan city, Korea. Geomat Nat Hazards Risk. 8(2):1185–1203.
  • Lehner B, Döll P, Alcamo J, Henrichs T, Kaspar F. 2006. Estimating the impact of global change on flood and drought risks in Europe: a continental, integrated analysis. Clim Change. 75(3):273–299.
  • Li X, Yan D, Wang K, Weng B, Qin T, Liu S. 2019. Flood risk assessment of global watersheds based on multiple machine learning models. Water. 11(8):1654.
  • Liu W, Deng K, Zhang X, Cheng Y, Zheng Z, Jiang F, Peng J. 2020. A semi-supervised tri-catboost method for driving style recognition. Symmetry. 12(3):336.
  • Ma X, Sha J, Wang D, Yu Y, Yang Q, Niu X. 2018. Study on a prediction of P2P network loan default based on the machine learning LightGBM and XGboost algorithms according to different high dimensional data cleaning. Electron Commer Res Appl. 31:24–39.
  • Malekipirbazari M, Aksakalli V. 2015. Risk assessment in social lending via random forests. Expert Syst Appl. 42(10):4621–4631.
  • Martín-Vide JP, Llasat M. 2018. The 1962 flash flood in the Rubí stream (Barcelona, Spain). J Hydrol. 566:441–454.
  • 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(2):757–770.
  • Meraj G, Khan T, Romshoo SA, Farooq M, Rohitashw K, Sheikh BA. 2018. An integrated geoinformatics and hydrological modelling-based approach for effective flood management in the Jhelum Basin, NW Himalaya. Multidiscipl Digital Publ Instit Proc. 7(1):8.
  • Miao H, Zhang J, Gu B, Gao A, Hong J, Zhang Y, Gu W. 2019. Prognosis for residual islet β‐cell secretion function in young patients with newly diagnosed type 1 diabetes. J Diabetes. 11(10):818–825.
  • Mosavi A, Ozturk P, Chau K. 2018. Flood prediction using machine learning models: Literature review. Water. 10(11):1536.
  • Negm AM, editor. 2020. Flash floods in Egypt. Cham: Springer International Publishing [accessed 2020 Oct 21]. https://doi.org/10.1007/978-3-030-29635-3
  • Nguyen P, Ombadi M, Gorooh VA, Shearer EJ, Sadeghi M, Sorooshian S, Hsu K, Bolvin D, Ralph MF. 2020. PERSIANN dynamic infrared–rain rate (PDIR-Now): a near-real-time, quasi-global satellite precipitation dataset. J Hydrometeorol. 21(12):2893–2906.
  • Nguyen V-N, Tien Bui D, Ngo P-TT, Nguyen Q-P, Nguyen VC, Long NQ, Revhaug I. 2018. An integration of least squares support vector machines and firefly optimization algorithm for flood susceptible modeling using GIS. In: Tien Bui D, Ngoc Do A, Bui H-B, Hoang N-D, editors. Advances and applications in geospatial technology and earth resources. Cham: Springer International Publishing [accessed 2020 Dec 11]; p. 52–64. https://doi.org/10.1007/978-3-319-68240-2_4
  • Öztürk F, Akdeniz F. 2000. Ill-conditioning and multicollinearity. Linear Algebra Appl. 321(1-3):295–305.
  • Pachauri RK, Allen MR, Barros VR, Broome J, Cramer W, Christ R, Church JA, Clarke L, Dahe Q, Dasgupta P. 2014. Climate change 2014: synthesis report. Contribution of Working Groups I, II and III to the fifth assessment report of the Intergovernmental Panel on Climate Change. IPCC.
  • Pal M. 2005. Random forest classifier for remote sensing classification. Int J Remote Sens. 26(1):217–222.
  • Park S-J, Lee D-K. 2020. Prediction of coastal flooding risk under climate change impacts in South Korea using machine learning algorithms. Environ Res Lett. 15(9):094052.
  • Predick KI, Turner MG. 2008. Landscape configuration and flood frequency influence invasive shrubs in floodplain forests of the Wisconsin River (USA). J Ecol. 96(1):91–102.
  • Prokhorenkova L, Gusev G, Vorobev A, Dorogush AV, Gulin A. 2019. CatBoost: unbiased boosting with categorical features. Cornell University arXiv preprint arXiv:170609516:1–23.
  • Qin C-Z, Zhu A-X, Pei T, Li B-L, Scholten T, Behrens T, Zhou C-H. 2011. An approach to computing topographic wetness index based on maximum downslope gradient. Precision Agric. 12(1):32–43.
  • Quinlan JR. 1986. Induction of decision trees. Mach Learn. 1(1):81–106.
  • 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.
  • 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.
  • Raju KP, Kumar S, Mohan K, Pandey MK. 2008. Urban cadastral mapping using very high resolution remote sensing data. J Indian Soc Remote Sens. 36(3):283–288.
  • Rätsch G, Onoda T, Müller K-R. 2001. Soft margins for AdaBoost. Mach Learn. 42(3):287–320.
  • Saber M, Abdrabo KI, Habiba OM, Kantosh SA, Sumi T. 2020. Impacts of triple factors on flash flood vulnerability in Egypt: urban growth, extreme climate, and mismanagement. Geosciences. 10(1):24.
  • Safarov RZ, Shomanova ZK, Nossenko YG, Berdenov ZG, Bexeitova ZB, Shomanov AS, Mansurova M. 2020. Solving of classification problem in spatial analysis applying the technology of gradient boosting catboost. Folia Geographica. 62(1):112–126.
  • Samanta S, Pal DK, Palsamanta B. 2018. Flood susceptibility analysis through remote sensing, GIS and frequency ratio model. Appl Water Sci. 8(2):1–14.
  • Schoppa L, Disse M, Bachmair S. 2020. Evaluating the performance of random forest for large-scale flood discharge simulation. J Hydrol. 590:125531.
  • Shafizadeh-Moghadam H, Valavi R, Shahabi H, Chapi K, Shirzadi A. 2018. Novel forecasting approaches using combination of machine learning and statistical models for flood susceptibility mapping. J Environ Manage. 217:1–11.
  • Shahabi H, Shirzadi A, Ronoud S, Asadi S, Pham BT, Mansouripour F, Geertsema M, Clague JJ, Bui DT. 2021. Flash flood susceptibility mapping using a novel deep learning model based on deep belief network, back propagation and genetic algorithm. Geosci Front. 12(3):101100.
  • Shirzadi A, Asadi S, Shahabi H, Ronoud S, Clague JJ, Khosravi K, Pham BT, Ahmad BB, Bui DT. 2020. A novel ensemble learning based on Bayesian Belief Network coupled with an extreme learning machine for flash flood susceptibility mapping. Eng Appl Artif Intell. 96:103971.
  • Siahkamari S, Haghizadeh A, Zeinivand H, Tahmasebipour N, Rahmati O. 2018. Spatial prediction of flood-susceptible areas using frequency ratio and maximum entropy models. Geocarto Int. 33(9):927–941.
  • Soliman M. 2015. Rain fell in Hurghada … and the emergency was raised in anticipation of its turning into floods. Al masry Elyoum. https://www.almasryalyoum.com/news/details/830677.
  • 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. Geocarto Int. 35(9):991–1017.
  • Sun Z, Li X, Fu W, Li Y, Tang D. 2013. Long-term effects of land use/land cover change on surface runoff in urban areas of Beijing, China. J Appl Remote Sens. 8(1):084596.
  • Sun X, Liu M, Sima Z. 2020. A novel cryptocurrency price trend forecasting model based on LightGBM. Financ Res Lett. 32:101084.
  • 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. Stoch Environ Res Risk Assess. 34(12):2277–2300.
  • 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.
  • Tehrany M, Jones S. 2017. Evaluating the variations in the flood susceptibility maps accuracies due to the alterations in the type and extent of the flood inventory. In Kuala Lumpur: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. 42; p. 4.
  • Tehrany MS, Jones S, Shabani F. 2019. Identifying the essential flood conditioning factors for flood prone area mapping using machine learning techniques. CATENA. 175:174–192.
  • 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.
  • 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.
  • Tien Bui D, Pradhan B, Nampak H, Bui Q-T, Tran Q-A, Nguyen Q-P. 2016. Hybrid artificial intelligence approach based on neural fuzzy inference model and metaheuristic optimization for flood susceptibilitgy modeling in a high-frequency tropical cyclone area using GIS. J Hydrol. 540:317–330.
  • Torabi Haghighi A, Menberu MW, Darabi H, Akanegbu J, Kløve B. 2018. Use of remote sensing to analyse peatland changes after drainage for peat extraction. Land Degrad Develop. 29(10):3479–3488.
  • Townshend J, Justice C, Li W, Gurney C, McManus J. 1991. Global land cover classification by remote sensing: present capabilities and future possibilities. Remote Sens Environ. 35(2-3):243–255.
  • Unduche F, Tolossa H, Senbeta D, Zhu E. 2018. Evaluation of four hydrological models for operational flood forecasting in a Canadian Prairie watershed. Hydrol Sci J. 63(8):1133–1149.
  • Unisdr C. 2015. The human cost of natural disasters: a global perspective. https://reliefweb.int/sites/reliefweb.int/files/resources/PAND_report.pdf.
  • Vinet F. 2008. Geographical analysis of damage due to flash floods in southern France: the cases of 12–13 November 1999 and 8–9 September 2002. Appl Geogr. 28(4):323–336.
  • Vojtek M, Vojteková J. 2019. Flood susceptibility mapping on a national scale in Slovakia using the analytical hierarchy process. Water. 11(2):364.
  • Wang Y, Hong H, Chen W, Li S, Panahi M, Khosravi K, Shirzadi A, Shahabi H, Panahi S, Costache R. 2019. Flood susceptibility mapping in Dingnan County (China) using adaptive neuro-fuzzy inference system with biogeography based optimization and imperialistic competitive algorithm. J Environ Manage. 247:712–729.
  • Wang D, Zhang Y, Zhao Y. 2017. LightGBM: an effective miRNA classification method in breast cancer patients. ICCBB 2017: Proceedings of the 2017 International Conference on Computational Biology and Bioinformatics October 2017; p. 7–11. https://doi.org/10.1145/3155077.3155079.
  • Wilson JP, Gallant JC. 2000. Terrain analysis: principles and applications. John Wiley & Sons; p. 520.
  • Yamazaki D, Ikeshima D, Tawatari R, Yamaguchi T, O'Loughlin F, Neal JC, Sampson CC, Kanae S, Bates PD. 2017. A high‐accuracy map of global terrain elevations. Geophys Res Lett. 44(11):5844–5853.
  • Yariyan P, Janizadeh S, Van Phong T, Nguyen HD, Costache R, Van Le H, Pham BT, Pradhan B, Tiefenbacher JP. 2020. Improvement of best first decision trees using bagging and dagging ensembles for flood probability mapping. Water Resour Manage. 34(9):3037–3053.
  • Ye B, Liu B, Tian Y, Wan L. 2020. A methodology for predicting aggregate flight departure delays in airports based on supervised learning. Sustainability. 12(7):2749.
  • Young RA, Mutchler CK. 1969. Soil movement on irregular slopes. Water Resour Res. 5(5):1084–1089.
  • Youssef AM, Hegab MA. 2019. Flood-hazard assessment modeling using multicriteria analysis and GIS: a case study—Ras Gharib area, Egypt. In: Hamid RP, Candan G, editors. Spatial modeling in GIS and R for earth and environmental sciences. Elsevier; p. 229–257.
  • Youssef AM, Pradhan B, Sefry SA. 2016. Flash flood susceptibility assessment in Jeddah city (Kingdom of Saudi Arabia) using bivariate and multivariate statistical models. Environ Earth Sci. 75(1):12.
  • Zhang J, Fogelman-Soulié F. 2018. KKbox’s music recommendation challenge solution with feature engineering. In 11th ACM International Conference on Web Search and Data Mining WSDM, Los Angeles, California, USA, P. 1–8.
  • Zhang Y, Zhao Z, Zheng J. 2020. CatBoost: a new approach for estimating daily reference crop evapotranspiration in arid and semi-arid regions of Northern China. J Hydrol. 588:125087.
  • Zhang X, Aguilar E, Sensoy S, Melkonyan H, Tagiyeva U, Ahmed N, Kutaladze N, Rahimzadeh F, Taghipour A, Hantosh TH, et al. 2005. Trends in Middle East climate extreme indices from 1950 to 2003. J Geophys Res. 110(D22):1–12, D22104. https://doi.org/10.1029/2005JD006181.

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