Flash-flood propagation susceptibility estimation using weights of evidence and their novel ensembles with multicriteria decision making and machine learning

References

• Abderrezzak KEK, Paquier A, Mignot E. 2009. Modelling flash flood propagation in urban areas using a two-dimensional numerical model. Nat Hazards. 50(3):433–460.
   Web of Science ®Google Scholar
• Agarwal E, Agarwal R, Garg R, Garg P. 2013. Delineation of groundwater potential zone: An AHP/ANP approach. J Earth Syst Sci. 122(3):887–898.
   Web of Science ®Google Scholar
• Aguilar MA, del Mar Saldaña M, Aguilar FJ. 2013. Assessing geometric accuracy of the orthorectification process from GeoEye-1 and WorldView-2 panchromatic images. Int J Appl Earth Obs Geoinform. 21:427–435.
   Web of Science ®Google Scholar
• Ahmadlou M, Karimi M, Alizadeh S, Shirzadi A, Parvinnejhad D, Shahabi H, Panahi M. 2019. Flood susceptibility assessment using integration of adaptive network-based fuzzy inference system (ANFIS) and biogeography-based optimization (BBO) and BAT algorithms (BA). Geocarto Int. 34(11):1252–1272.
   Web of Science ®Google Scholar
• Akıncı H, Özalp AY, Turgut B. 2013. Agricultural land use suitability analysis using GIS and AHP technique. Comput Electron Agric. 97:71–82.
   Web of Science ®Google Scholar
• Al-Abadi AM. 2018. Mapping flood susceptibility in an arid region of southern Iraq using ensemble machine learning classifiers: a comparative study. Arab J Geosci. 11:218.
   Web of Science ®Google Scholar
• Al-Abadi AM, Shahid S. 2015. A comparison between index of entropy and catastrophe theory methods for mapping groundwater potential in an arid region. Environ Monit Assess. 187:576.
   PubMed Web of Science ®Google Scholar
• 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..
   Web of Science ®Google Scholar
• Althuwaynee OF, Pradhan B, Park H-J, Lee JH. 2014. A novel ensemble bivariate statistical evidential belief function with knowledge-based analytical hierarchy process and multivariate statistical logistic regression for landslide susceptibility mapping. Catena. 114:21–36.
   Web of Science ®Google Scholar
• Althuwaynee OF, Pradhan B, Park H-J, Lee JH. 2014. A novel ensemble decision tree-based CHi-squared Automatic Interaction Detection (CHAID) and multivariate logistic regression models in landslide susceptibility mapping. Landslides 11(6):1063–1078.
   Web of Science ®Google Scholar
• Ameri AA, Pourghasemi HR, Cerda A. 2018. Erodibility prioritization of sub-watersheds using morphometric parameters analysis and its mapping: A comparison among TOPSIS, VIKOR, SAW, and CF multi-criteria decision making models. Sci Total Environ. 613-614:1385–1400.
   PubMed Web of Science ®Google Scholar
• Anquetin S, Braud I, Vannier O, Viallet P, Boudevillain B, Creutin J-D, Manus C. 2010. Sensitivity of the hydrological response to the variability of rainfall fields and soils for the Gard 2002 flash-flood event. J Hydrol. 394(1-2):134–147.
   Web of Science ®Google Scholar
• Antonetti M, Horat C, Sideris IV, Zappa M. 2019. Ensemble flood forecasting considering dominant runoff processes–Part 1: Set-up and application to nested basins (Emme, Switzerland. Nat Hazards Earth Syst Sci. 19(1):19–40. ).
   Web of Science ®Google Scholar
• Antronico L, Coscarelli R, De Pascale F, Condino F. 2019. Social Perception of Geo-Hydrological Risk in the Context of Urban Disaster Risk Reduction: A Comparison between Experts and Population in an Area of Southern Italy. Sustainability 11(7):2061.
   Web of Science ®Google Scholar
• 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(3):595–618.
   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
• Avand M, Janizadeh S, Naghibi SA, Pourghasemi HR, Khosrobeigi Bozchaloei S, Blaschke T. 2019. A Comparative Assessment of Random Forest and k-Nearest Neighbor Classifiers for Gully Erosion Susceptibility Mapping. Water. 11:2076.
   Web of Science ®Google Scholar
• Azareh A, Rafiei Sardooi E, Choubin B, Barkhori S, Shahdadi A, Adamowski J, Shamshirband S. 2019. Incorporating multi-criteria decision-making and fuzzy-value functions for flood susceptibility assessment. Geocarto Int. :1–21. https://doi.org/10.1080/10106049.2019.1695958
   Google Scholar
• Breiman L, Friedman JH, Olshen RA, Stone CJ. 1984. Classification and regression trees. Belmont, CA. Wadsworth Int Group. 432:151–166.
   Google Scholar
• Bui DT, Bui Q-T, Nguyen Q-P, Pradhan B, Nampak H, Trinh PT. 2017. A hybrid artificial intelligence approach using GIS-based neural-fuzzy inference system and particle swarm optimization for forest fire susceptibility modeling at a tropical area. Agric For Meteorol. 233:32–44.
   Web of Science ®Google Scholar
• Bui DT, Lofman O, Revhaug I, Dick O. 2011. Landslide susceptibility analysis in the Hoa Binh province of Vietnam using statistical index and logistic regression. Nat Hazards. 59(3):1413–1444.
   Web of Science ®Google Scholar
• Bui DT, Moayedi H, Kalantar B, Osouli A, Pradhan B, Nguyen H, Rashid ASA. 2019. A novel swarm intelligence—Harris hawks optimization for spatial assessment of landslide susceptibility. Sensors. 19:3590.
   PubMed 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
• Canbek G, Sagiroglu S, Temizel TT, Baykal N. 2017. Binary classification performance measures/metrics: A comprehensive visualized roadmap to gain new insights. Paper presented at:2017 International Conference on Computer Science and Engineering (UBMK), IEEE, p. 821–826.
   Google Scholar
• 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.
   Web of Science ®Google Scholar
• Chao L, Zhang K, Wang J, Feng J, Zhang M. 2021. A comprehensive evaluation of five evapotranspiration datasets based on ground and grace satellite observations: implications for improvement of evapotranspiration retrieval algorithm. Remote Sens (Basel, Switzerland). 13(12):2414.
   Web of Science ®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.
   Web of Science ®Google Scholar
• Chen Z, Liang S, Ke Y, Yang Z, Zhao H. 2019. Landslide susceptibility assessment using evidential belief function, certainty factor and frequency ratio model at Baxie River basin, NW China. Geocarto Int. 34(4):348–367.
   Web of Science ®Google Scholar
• Chen W, Li W, Hou E, Bai H, Chai H, Wang D, Cui X, Wang Q. 2015. Application of frequency ratio, statistical index, and index of entropy models and their comparison in landslide susceptibility mapping for the Baozhong Region of Baoji, China. Arab J Geosci. 8(4):1829–1841.
   Web of Science ®Google Scholar
• Chen W, Li H, Hou E, Wang S, Wang G, Panahi M, Li T, Peng T, Guo C, Niu C, et al. 2018. GIS-based groundwater potential analysis using novel ensemble weights-of-evidence with logistic regression and functional tree models. Sci Total Environ. 634:853–867.
   PubMed Web of Science ®Google Scholar
• Chen W, Pradhan B, Li S, Shahabi H, Rizeei HM, Hou E, Wang S. 2019. Novel hybrid integration approach of bagging-based fisher’s linear discriminant function for groundwater potential analysis. Nat Resour Res. :1–20.
   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
• Costache R. 2014a. Using GIS techniques for assessing lag time and concentration time in small river basins. Case study: Pecineaga river basin. Romania Geogr Tech. 9:31–38.
   Google Scholar
• Costache R. 2014b. Estimating multiannual average runoff depth in the middle and upper sectors of. Buzău River Basin Geogr Tech. 9:21–29.
   Google Scholar
• Costache R. 2014c. Assessing monthly average runoff depth in Sărățel river basin. Romania Analele Stiintifice Ale Univ Alexandru Ioan Cuza Din Iasi-Ser Geogr. 60:97–110.
   Google Scholar
• Costache R. 2019. Flash-Flood Potential assessment in the upper and middle sector of Prahova river catchment (Romania). A comparative approach between four hybrid models. Sci Total Environ. 659:1115–1134.
   PubMed Web of Science ®Google Scholar
• Costache R, Bao Pham Q, Corodescu-Roșca E, Cîmpianu C, Hong H, Thi Thuy Linh N, Ming Fai C, Najah Ahmed A, Vojtek M, Muhammed Pandhiani S, et al. 2020. Using GIS, Remote Sensing, and Machine Learning to Highlight the Correlation between the Land-Use/Land-Cover Changes and Flash-Flood Potential. Remote Sens 12, 1422. 12(9):1422.,
   Web of Science ®Google Scholar
• Costache R, Bui DT. 2019. Spatial prediction of flood potential using new ensembles of bivariate statistics and artificial intelligence: A case study at the Putna river catchment of Romania. Sci Total Environ. 691:1098–1118.
   PubMed 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
• Costache R, Pham QB, Sharifi E, Linh NTT, Abba S, Vojtek M, Vojteková J, Nhi PTT, Khoi DN. 2020. Flash-flood susceptibility assessment using multi-criteria decision making and machine learning supported by remote sensing and GIS techniques. Remote Sens. 12(1):106.
   Google Scholar
• Costache R, Tien Bui D. 2020. Identification of areas prone to flash-flood phenomena using multiple-criteria decision-making, bivariate statistics, machine learning and their ensembles. Sci Total Environ. 712:136492..
   PubMed Web of Science ®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. 126:59.
   Web of Science ®Google Scholar
• Dahri N, Abida H. 2017. Monte Carlo simulation-aided analytical hierarchy process (AHP) for flood susceptibility mapping in Gabes Basin (southeastern Tunisia). Environ Earth Sci. 76:302.
   Web of Science ®Google Scholar
• Dano UL. 2020. Flash flood impact assessment in Jeddah City: An analytic hierarchy process approach. Hydrology 7(1):10.
   Web of Science ®Google Scholar
• Dano UL. 2021. An AHP-based assessment of flood triggering factors to enhance resiliency in Dammam, Saudi Arabia. GeoJournal 1–16. https://doi.org/10.1007/s10708-020-10363-5.
   Google Scholar
• Dewals B, Giron E, Ernst J, Hecq W, Pirotton M. 2008. Integrated assessment of flood protection measures in the context of climate change: hydraulic modelling and economic approach. WIT Trans Ecol Environ. 108:149–159.
   Google Scholar
• 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.
   PubMed Web of Science ®Google Scholar
• Dottori F, Martina MLV, Figueiredo R. 2018. A methodology for flood susceptibility and vulnerability analysis in complex flood scenarios. J Flood Risk Management. 11:S632–S645.
   Web of Science ®Google Scholar
• Dou J, Yamagishi H, Zhu Z, Yunus AP, Chen CW. 2018. TXT-tool 1.081-6.1 A comparative study of the binary logistic regression (BLR) and artificial neural network (ANN) models for GIS-based spatial predicting landslides at a regional scale. In: Landslide dynamics: ISDR-ICL landslide interactive teaching tools. Berlin, Germany: Springer, p. 139–151.
   Google Scholar
• Fan P, Deng R, Qiu J, Zhao Z, Wu S. 2021. Well logging curve reconstruction based on kernel ridge regression. Arab J Geosci.
   Google Scholar
• Feng S, Zuo C, Zhang L, Yin W, Chen Q. 2021. Generalized framework for non-sinusoidal fringe analysis using deep learning. Photon Res. 9(6):1084.
   Google Scholar
• Fontanine I, Costache R. 2013. Using GIS techniques for surface runoff potential analysis in the Subcarpathian area between Buzãu and Slãnic rivers, in Romania. Cinq Cont. 3:47–57.
   Google Scholar
• Fowler H, Wilby R. 2010. Detecting changes in seasonal precipitation extremes using regional climate model projections: Implications for managing fluvial flood risk. Water Resour Res. 46(3)
   PubMedGoogle Scholar
• Gessesse B, Bewket W, Bräuning A. 2015. Model‐based characterization and monitoring of runoff and soil erosion in response to land use/land cover changes in the Modjo watershed, Ethiopia. Land Degrad Develop. 26(7):711–724.
   Web of Science ®Google Scholar
• Ghosh A, Kar SK. 2018. Application of analytical hierarchy process (AHP) for flood risk assessment: a case study in Malda district of West Bengal, India. Nat Hazards. 94(1):349–368.
   Web of Science ®Google Scholar
• Hapciuc O-E, Romanescu G, Minea I, Iosub M, Enea A, Sandu I. 2016. Flood susceptibility analysis of the cultural heritage in the Sucevita catchment (Romania). Int J Conserv Sci. 7(2):501–510.
   Web of Science ®Google Scholar
• He Y, Dai L, Zhang H. 2020. Multi-branch deep residual learning for clustering and beamforming in user-centric network. IEEE Commun Lett. 24(10):2221–2225.
   Web of Science ®Google Scholar
• Hofmann J, Schüttrumpf H. 2019. Risk-based early warning system for pluvial flash floods: Approaches and foundations. Geosciences 9(3):127.
   Web of Science ®Google Scholar
• 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. 133:266–281.
   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
• Hosseini FS, Choubin B, Mosavi A, Nabipour N, Shamshirband S, Darabi H, Haghighi AT. 2020. Flash-flood hazard assessment using ensembles and Bayesian-based machine learning models: application of the simulated annealing feature selection method. Sci Total Environ. 711:135161.
   PubMed Web of Science ®Google Scholar
• Hosseini FS, Sigaroodi SK, Salajegheh A, Moghaddamnia A, Choubin B. 2021. Towards a flood vulnerability assessment of watershed using integration of decision-making trial and evaluation laboratory, analytical network process, and fuzzy theories. Environ Sci Pollut Res. :1–12. https://doi.org/10.1007/s11356-021-14534-w.
   Google Scholar
• Hu B, Wu Y, Wang H, Tang Y, Wang C. 2021. Risk mitigation for rockfall hazards in steeply dipping coal seam: a case study in Xinjiang, northwestern China. Geom Nat Hazard Risk. 12(1):988–1014.
   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
• Kanani-Sadat Y, Arabsheibani R, Karimipour F, Nasseri M. 2019. A new approach to flood susceptibility assessment in data-scarce and ungauged regions based on GIS-based hybrid multi criteria decision-making method. J Hydrol. 572:17–31.
   Web of Science ®Google Scholar
• Kavzoglu T, Sahin EK, Colkesen I. 2014. Landslide susceptibility mapping using GIS-based multi-criteria decision analysis, support vector machines, and logistic regression. Landslides 11(3):425–439.
   Web of Science ®Google Scholar
• 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.
   Web of Science ®Google Scholar
• Leandro J, Djordjević S, Chen A, Savić D, Stanić M. 2011. Calibration of a 1D/1D urban flood model using 1D/2D model results in the absence of field data. Water Sci Technol. 64(5):1016–1024.
   PubMed Web of Science ®Google Scholar
• Lee B-J, Kim S. 2019. Gridded flash flood risk index coupling statistical approaches and TOPLATS land surface model for mountainous areas. Water 11(3):504.
   Web of Science ®Google Scholar
• Lei X, Chen W, Avand M, Janizadeh S, Kariminejad N, Shahabi H, Costache R, Shahabi H, Shirzadi A, Mosavi A. 2020. GIS-based machine learning algorithms for gully erosion susceptibility mapping in a semi-arid region of Iran. Remote Sens. 12(15):2478.
   Web of Science ®Google Scholar
• Liu H, Shi Z, Li J, Liu C, Meng X, Du Y, Chen J. 2021. Detection of road cavities in urban cities by 3D ground-penetrating radar. Geophysics. 86(3):WA25–A33.
   Web of Science ®Google Scholar
• Markolf SA, Hoehne C, Fraser A, Chester MV, Underwood BS. 2019. Transportation resilience to climate change and extreme weather events–Beyond risk and robustness. Transp Policy. 74:174–186.
   Web of Science ®Google Scholar
• Midi H, Sarkar SK, Rana S. 2010. Collinearity diagnostics of binary logistic regression model. J Interdiscip Math. 13(3):253–267.
   Google Scholar
• Miles J. 2014. Tolerance and variance inflation factor. Hoboken, NJ: Wiley.
   Google Scholar
• Mohammady M, Pourghasemi HR, Amiri M. 2019. Assessment of land subsidence susceptibility in Semnan plain (Iran): a comparison of support vector machine and weights of evidence data mining algorithms. Nat Hazards 99:951–971.
   Web of Science ®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.
   Google Scholar
• Mujumdar P. 2001. Flood wave propagation. Resonance. 6(5):66–73.
   Google Scholar
• Ngo P-T, Hoang N-D, Pradhan B, Nguyen Q, Tran X, Nguyen Q, Nguyen V, 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
• O'Callaghan JF, Mark DM. 1984. The extraction of drainage networks from digital elevation data. Comput Vis Graph Image Process. 28(3):323–344.
   Google Scholar
• Pham BT, Nguyen-Thoi T, Qi C, Van Phong T, Dou J, Ho LS, Van Le H, Prakash I. 2020. Coupling RBF neural network with ensemble learning techniques for landslide susceptibility mapping. Catena. 195:104805.
   Web of Science ®Google Scholar
• Pham BT, Pradhan B, Bui DT, Prakash I, Dholakia M. 2016. A comparative study of different machine learning methods for landslide susceptibility assessment: A case study of Uttarakhand area (India). Environ Model Softw. 84:240–250.
   Web of Science ®Google Scholar
• Pham BT, Shirzadi A, Bui DT, Prakash I, Dholakia M. 2018. A hybrid machine learning ensemble approach based on a radial basis function neural network and rotation forest for landslide susceptibility modeling: A case study in the Himalayan area, India. Int J Sediment Res. 33(2):157–170.
   Web of Science ®Google Scholar
• Pourghasemi HR, Beheshtirad M, Pradhan B. 2016. A comparative assessment of prediction capabilities of modified analytical hierarchy process (M-AHP) and Mamdani fuzzy logic models using Netcad-GIS for forest fire susceptibility mapping. Geomat Nat Hazards Risk 7(2):861–885.
   Web of Science ®Google Scholar
• Pradhan B. 2010. Remote sensing and GIS-based landslide hazard analysis and cross-validation using multivariate logistic regression model on three test areas in Malaysia. Adv Space Res. 45(10):1244–1256.
   Web of Science ®Google Scholar
• Prăvălie R, Costache R. 2013. The vulnerability of the territorial-administrative units to the hydrological phenomena of risk (flash-floods). Case study: the subcarpathian sector of Buzău catchment. Analele Univ Din Oradea–Seria Geogr. 23:91–98.
   Google Scholar
• Prăvălie R, Costache R. 2014. The analysis of the susceptibility of the flash-floods’ genesis in the area of the hydrographical basin of Bāsca Chiojdului river/Analiza susceptibilitatii genezei viiturilor īn aria bazinului hidrografic al rāului Bāsca Chiojdului. Forum Geogr. 13:39–49. http://dx.doi.org/10.5775/fg.2067-4635.2014.089.i.
   Google Scholar
• Qasem SN, Shamsuddin SM. 2011. Radial basis function network based on time variant multi-objective particle swarm optimization for medical diseases diagnosis. Appl Soft Comput. 11(1):1427–1438.
   Web of Science ®Google Scholar
• Saaty TL. 1980. The analytical hierarchy process, planning, priority Resource Allocation. New York, NY: RWS Publ..
   Google Scholar
• Sajedi‐Hosseini F, Choubin B, Solaimani K, Cerdà A, Kavian A. 2018. Spatial prediction of soil erosion susceptibility using a fuzzy analytical network process: application of the fuzzy decision making trial and evaluation laboratory approach. Land Degrad Dev. 29(9):3092–3103.
   Web of Science ®Google Scholar
• Shen X, Liu B, Jiang M, Lu X. 2020. Marshland loss warms local land surface temperature in China. Geophy Res Lett. 47(6).
   PubMed Web of Science ®Google Scholar
• Shen X, Jiang M, Lu X, Liu X, Liu B, Zhang J, Wang X, Tong S, Lei G, Wang S, et al. 2021. Aboveground biomass and its spatial distribution pattern of herbaceous marsh vegetation in China Science. Earth Sci. 64:1115–1125
   Google Scholar
• Sun C-C. 2010. A performance evaluation model by integrating fuzzy AHP and fuzzy TOPSIS methods. Expert Syst Appl. 37(12):7745–7754.
   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. Stoch Environ Res Risk Assess. 34:2277–2300.
   Web of Science ®Google Scholar
• Urbanowicz RJ, Meeker M, La Cava W, Olson RS, Moore JH. 2018. Relief-based feature selection: introduction and review. J Biomed Inform. 85:189–203.
   PubMed Web of Science ®Google Scholar
• Vakhshoori V, Zare M. 2018. Is the ROC curve a reliable tool to compare the validity of landslide susceptibility maps? Geomat Nat Hazards Risk 9(1):249–266.
   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
• Wheeler D, Tiefelsdorf M. 2005. Multicollinearity and correlation among local regression coefficients in geographically weighted regression. J Geograph Syst. 7(2):161–187.
   Google Scholar
• Wu X, Kumar V, Ross Quinlan J, Ghosh J, Yang Q, Motoda H, McLachlan GJ, Ng A, Liu B, Yu PS, et al. 2008. Top 10 algorithms in data mining. Knowl Inf Syst. 14(1):1–37.,
   Web of Science ®Google Scholar
• Yariyan P, Janizadeh S, Phong TV, Nguyen HD, Costache R, Le HV, Pham BT, Pradhan B, Tiefenbacher JP. 2020. Improvement of Best First Decision Trees Using Bagging and Dagging Ensembles for Flood-risk Mapping. Water Resour Manag. 34(9):3037–3053.
   Web of Science ®Google Scholar
• Yeon Y-K, Han J-G, Ryu KH. 2010. Landslide susceptibility mapping in Injae, Korea, using a decision tree. Eng Geol. 116(3-4):274–283.
   Web of Science ®Google Scholar
• Youssef AM, Pourghasemi HR, Pourtaghi ZS, Al-Katheeri MM. 2016. Landslide susceptibility mapping using random forest, boosted regression tree, classification and regression tree, and general linear models and comparison of their performance at Wadi Tayyah Basin, Asir Region, Saudi Arabia. Landslides 13(5):839–856.
   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
• Zaharia L, Costache R, Prăvălie R, Minea G. 2015. Assessment and mapping of flood potential in the Slănic catchment in Romania. J Earth Syst Sci. 124(6):1311–1324.
   Web of Science ®Google Scholar
• Zare M, Pourghasemi HR, Vafakhah M, Pradhan B. 2013. Landslide susceptibility mapping at Vaz Watershed (Iran) using an artificial neural network model: a comparison between multilayer perceptron (MLP) and radial basic function (RBF) algorithms. Arab J Geosci. 6(8):2873–2888.
   Web of Science ®Google Scholar
• Zhang C-X, Wang G-W, Zhang J-S. 2012. An empirical bias–variance analysis of DECORATE ensemble method at different training sample sizes. J Appl Stat. 39(4):829–850.
   Web of Science ®Google Scholar
• Zhang K, Wang S, Bao H, Zhao X. 2019. Characteristics and influencing factors of rainfall-induced landslide and debris flow hazards in Shaanxi Province. Nat Hazards Earth Syst Sci. 19(1):93–105.
   Web of Science ®Google Scholar
• Zhang K, Ali A, Antonarakis A, Moghaddam M, Saatchi S, Tabatabaeenejad A, Chen R, Jaruwatanadilok S, Cuenca R, Crow WT, et al. 2019. The sensitivity of North American terrestrial carbon fluxes to spatial and temporal variation in soil moisture: an analysis using radar‐derived estimates of root‐zone soil moisture. J Geophys Res Biogeosci. 124(11):3208–3231.
   Web of Science ®Google Scholar
• Zhang Z, Luo C, Zhao Z. 2020. Application of probabilistic method in maximum tsunami height prediction considering stochastic seabed topography. Nat Hazards. 104(3):2511–2530.
   Web of Science ®Google Scholar
• Zhang C, Kodestani H, Masri SF, Wang J, Sun L. 2021. Data-driven system parameter change detection for a chain-like uncertainties embedded structure. Struct Control Health Monit.
   PubMed Web of Science ®Google Scholar
• Zhao G, Pang B, Xu Z, Peng D, Xu L. 2019. Assessment of urban flood susceptibility using semi-supervised machine learning model. Sci Total Environ. 659:940–949.
   PubMed Web of Science ®Google Scholar