Anılan, TuğçeNacar, SinanYüksek, Ömer2022-12-082022-12-082020-04-10Anılan, T. vd. (2020). "Prediction of maximum annual flood discharges using artificial neural network approaches". Gradevinar, 72(3), 215-224.0350-2465https://doi.org/10.14256/JCE.2316.2018http://www.casopis-gradjevinar.hr/archive/article/2316http://hdl.handle.net/11452/29757The applicability of artificial neural network (ANN) approaches for estimation of maximum annual flows is investigated in the paper. The performance of three neural network models is compared: multi layer perceptron neural networks (MLP_NN), generalized feed forward neural networks (GFF_NN), and principal component analysis with neural networks (PCA_ NN). The proposed approaches were applied to 33 stream-gauging stations. It was found that the optimal 3-hidden layered PCA_NN method was more appropriate than the optimal MLP_NN and GFF_NN models for the estimation of maximum annual flows.eninfo:eu-repo/semantics/openAccessArtificial neural networksPrincipal component analysisMaximum annual flowsL-moments approachFrequency-analysisIndex-floodFeedforward networksStreamflowBasinClassificationRainfallQualityEngineeringArticle0005346112000022-s2.0-85084148753215224723Engineering, civilFlood Frequency; L-Moment; Catchment Area (Hydrology)