Civil Engineering / İnşaat Mühendisliği

Permanent URI for this collectionhttps://hdl.handle.net/11147/13

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Subject: search.filters.subject.SimulationWoS Q: search.filters.wosQuartile.Q2

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  • Article
    Citation - WoS: 103
    Citation - Scopus: 126
    Ann and Fuzzy Logic Models for Simulating Event-Based Rainfall-Runoff
    (American Society of Civil Engineers (ASCE), 2006) Tayfur, Gökmen; Singh, Vijay P.
    This study presents the development of artificial neural network (ANN) and fuzzy logic (FL) models for predicting event-based rainfall runoff and tests these models against the kinematic wave approximation (KWA). A three-layer feed-forward ANN was developed using the sigmoid function and the backpropagation algorithm. The FL model was developed employing the triangular fuzzy membership functions for the input and output variables. The fuzzy rules were inferred from the measured data. The measured event based rainfall-runoff peak discharge data from laboratory flume and experimental plots were satisfactorily predicted by the ANN, FL, and KWA models. Similarly, all the three models satisfactorily simulated event-based rainfall-runoff hydrographs from experimental plots with comparable error measures. ANN and FL models also satisfactorily simulated a measured hydrograph from a small watershed 8.44 km2 in area. The results provide insights into the adequacy of ANN and FL methods as well as their competitiveness against the KWA for simulating event-based rainfall-runoff processes.
  • Article
    Citation - WoS: 27
    Citation - Scopus: 101
    Predicting Longitudinal Dispersion Coefficient in Natural Streams by Artificial Neural Network
    (American Society of Civil Engineers (ASCE), 2005) Tayfur, G; Singh, VP
    An artificial neural network (ANN) model was developed to predict the longitudinal dispersion coefficient in natural streams and rivers. The hydraulic variables [flow discharge (Q), flow depth (H), flow velocity (U), shear velocity (u*), and relative shear velocity (U/u*)] and geometric characteristics [channel width (B), channel sinuosity (sigma), and channel shape parameter (beta)] constituted inputs to the ANN model, whereas the dispersion coefficient (K-x) was the target model output. The model was trained and tested using 71 data sets of hydraulic and geometric parameters and dispersion coefficients measured on 29 streams and rivers in the United States. The training of the ANN model was accomplished with an explained variance of 90% of the dispersion coefficient. The dispersion coefficient values predicted by the ANN model satisfactorily compared with the measured values corresponding to different hydraulic and geometric characteristics. The predicted values were also compared with those predicted using several equations that have been suggested in the literature and it was found that the ANN model was superior in predicting the dispersion coefficient. The results of sensitivity analysis indicated that the Q data alone would be sufficient for predicting more frequently occurring low values of the dispersion coefficient (K-x < 100 m(2)/s). For narrower channels (B/H < 50) using only U/u* data would be sufficient to predict the coefficient. If beta and sigma were used along with the flow variables, the prediction capability of the ANN model would be significantly improved.