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Predicting Mean and Bankfull Discharge from Channel Cross-Sectional Area by Expert and Regression Methods

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  • Gokmen Tayfur
  • Vijay Singh

Abstract

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. Copyright Springer Science+Business Media B.V. 2011

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  • Gokmen Tayfur & Vijay Singh, 2011. "Predicting Mean and Bankfull Discharge from Channel Cross-Sectional Area by Expert and Regression Methods," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(5), pages 1253-1267, March.
    • Handle: RePEc:spr:waterr:v:25:y:2011:i:5:p:1253-1267
    DOI: 10.1007/s11269-010-9741-6
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    References listed on IDEAS

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    1. Saeid Eslamian & Mehdi Ghasemizadeh & Monireh Biabanaki & Mansoor Talebizadeh, 2010. "A Principal Component Regression Method for Estimating Low Flow Index," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(11), pages 2553-2566, September.
    2. M. Akbari & A. Afshar & M. Sadrabadi, 2009. "Fuzzy Rule Based Models Modification by New Data: Application to Flood Flow Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 23(12), pages 2491-2504, September.
    3. Yong-Ying Zhu & Hui-Cheng Zhou, 2009. "Rough Fuzzy Inference Model and its Application in Multi-factor Medium and Long-term Hydrological Forecast," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 23(3), pages 493-507, February.
    4. K. Engeland & H. Hisdal, 2009. "A Comparison of Low Flow Estimates in Ungauged Catchments Using Regional Regression and the HBV-Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 23(12), pages 2567-2586, September.
    5. Chang-Shian Chen & Frederick Chou & Boris Chen, 2010. "Spatial Information-Based Back-Propagation Neural Network Modeling for Outflow Estimation of Ungauged Catchment," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(14), pages 4175-4197, November.
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    1. Asli Ulke & Gokmen Tayfur & Sevinc Ozkul, 2017. "Investigating a Suitable Empirical Model and Performing Regional Analysis for the Suspended Sediment Load Prediction in Major Rivers of the Aegean Region, Turkey," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(3), pages 739-764, February.
    2. G. Zucco & G. Tayfur & T. Moramarco, 2015. "Reverse Flood Routing in Natural Channels using Genetic Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(12), pages 4241-4267, September.
    3. Gokmen Tayfur & Luca Brocca, 2015. "Fuzzy Logic for Rainfall-Runoff Modelling Considering Soil Moisture," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(10), pages 3519-3533, August.
    4. Gokmen Tayfur, 2017. "Modern Optimization Methods in Water Resources Planning, Engineering and Management," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(10), pages 3205-3233, August.
    5. Roohollah Noori & Hossien Sheikhian & Farhad Hooshyaripor & Ali Naghikhani & Jan Franklin Adamowski & Behzad Ghiasi, 2017. "Granular Computing for Prediction of Scour Below Spillways," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(1), pages 313-326, January.
    6. Gokmen Tayfur & Ata Nadiri & Asghar Moghaddam, 2014. "Supervised Intelligent Committee Machine Method for Hydraulic Conductivity Estimation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(4), pages 1173-1184, March.

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