Civil Engineering / İnşaat Mühendisliği

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

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Publisher: search.filters.publisher.Springer VerlagSubject: search.filters.subject.Rivers

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  • Article
    Citation - WoS: 27
    Citation - Scopus: 28
    Reverse flood routing in natural channels using genetic algorithm
    (Springer Verlag, 2015) Zucco, Graziano; Tayfur, Gökmen; Moramarco, Tommaso
    Establishing a clear overview of data discharge availability for water balance modelling in basins is a priority in Europe, and in the particular in the framework of the system of Economic and Environmental Accounts for Water (SEEAW) developed by the EU Directorate-General for the Environment. However, accurate discharge estimation at a river site depends on rating curve reliability usually defined by recording the water level at a gauged section and carrying out streamflow measurements. Local stage monitoring is fairly straightforward and relatively inexpensive compared to the cost to carry out flow velocity measurements which are, in addition, hindered by high flow. Moreover, hydraulic models may not be ideally suitable to serve the purpose of rating curve extension or its development at a river site upstream/downstream where the discharge is known due to their prohibitive requirement of channel cross-section details and roughness information at closer intervals. Likewise, rainfall-runoff transformation might be applied but its accuracy is tightly linked to detailed information in terms of geomorphological characteristics of intermediate basins as well as rainfall pattern data. On this basis, a procedure for reverse flood routing in natural channels is here proposed for three different configurations of hydrometric monitoring of a river reach where lateral flow is significant and no rainfall data are available for the intermediate basin. The first considers only the downstream channel end as a gauged site where discharge and stages are recorded. The second configuration assumes the downstream end as a gauged site but only in terms of stage. The third configuration envisages both channel ends equipped to recording stages. The channel geometry is known only at channel ends. The developed model has basically four components: (1) the inflow hydrograph is expressed by a Pearson Type-III distribution, involving parameters of peak discharge, time to peak, and a shape factor; (2) the basic continuity equation for flow routing written in the characteristic form is employed; (3) the lateral flow is related to stages at channel ends. (4) the relation between local stage and remote discharge as found by Moramarco et al. (2005b) is exploited. The parameters, coefficients and exponents of the model are obtained, for each configuration, using the genetic algorithm method. Three equipped river branches along the Tiber River in central Italy are used to validate the procedure. Analyses are carried out for three significant flood events occurred along the river and where the lateral flow was significant. Results show the good performance of the procedure for all three monitoring configurations. Specifically, the discharge hydrographs assessed at channel ends are found satisfactory both in terms of shape with a Nash-Sutcliffe ranging overall in the interval (0.755–0.972) and in the reproduction of rating curves at channel ends. Finally, by a synthetic test the performance of the developed procedure is compared to that of the hydraulic model coupled with a hydrologic model. Two river reaches are considered, the first along the Tiber River and the second one located in the Rio Grande basin which is a tributary of the Tiber River. Detailed channel geometry data are available for both the river sections. Results showed the effectiveness of the reverse flood routing to reproducing fairly well the hydrographs simulated by the hydraulic model in the three monitoring investigated configurations.
  • Article
    Citation - WoS: 22
    Citation - Scopus: 29
    Predicting Mean and Bankfull Discharge From Channel Cross-Sectional Area by Expert and Regression Methods
    (Springer Verlag, 2011) Tayfur, Gökmen; Singh, Vijay P.
    This study employed four methods-non-linear regression, fuzzy logic (FL), artificial neural networks (ANNs), and genetic algorithm (GA)-based nonlinear equation-for predicting mean discharge and bank-full discharge from cross-sectional area. The data compiled from the literature were separated into two groups-training (calibration) and testing (verification). Using training data sets, the methods were calibrated to obtain optimal values of the coefficients of the non-linear regression method; optimal number of fuzzy subsets, their base widths and fuzzy rules for the fuzzy method; and the optimal number of neurons in the hidden layer, the learning rate and momentum factor values for the ANN model. The GA-based method employed 100 chromosomes in the initial gene pool, 80% cross over rate and 4% mutation rate in determining the optimal values of the coefficients of the constructed nonlinear equation. The calibrated methods were then applied to the test data sets. The test results showed that the non-linear regression, ANN and GA-based methods were comparable in predicting the mean discharge while the fuzzy method produced high errors and low accuracy. The GA-based method had the highest accuracy of 75%. In terms of predicting bankfull discharge, all methods produced satisfactory results, although the fuzzy method had the lowest accuracy of 33%. The results of sensitivity analysis, which is limited to the GA-based and nonlinear regression methods, showed that the GA-based method calibrated with low bankfull discharge values can be successfully applied to predict high bankfull discharge values. This has important implications for predicting bankfull rates at ungauged sites. On the other hand, the sensitivity analysis results also showed that both the non-linear regression and GA-based methods have poor extrapolation capability for predicting mean discharge data.