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Determination of Mean Velocity and Discharge in Natural Streams Using Neuro-Fuzzy and Neural Network Approaches

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  • Onur Genç
  • Özgür Kişi
  • Mehmet Ardıçlıoğlu

Abstract

The applicability of artificial neural networks (ANNs) and the adaptive neuro-fuzzy inference system (ANFIS) for determination of mean velocity and discharge of natural streams is investigated. The 2,184 field data obtained from four different sites on the Sarimsakli and Sosun streams in central Turkey were used in the study. ANNs and ANFIS models use the inputs, water surface velocity and water surface slope, to estimate the mean velocity and discharges of natural streams. The accuracies of both models were compared with the multiple-linear regression (MLR) model. The comparison results showed that the ANFIS model performed better than the ANNs and regression models for estimating mean velocity and discharge. The ANN model also showed better accuracy than the MLR model. The root mean square errors (RMSE) and mean absolute relative errors (MARE) of the MLR model were reduced by 88 and 91 % using the ANFIS model in estimating discharges, respectively. It is found that the optimal ANFIS model with RMSE of 0,063, MARE of 3,47 and determination coefficient (R 2 ) of 0,996 in the test period is superior in estimation of discharge than the MLR model with RMSE of 0,532, MARE of 38,9 and R 2 of 0,776, respectively. The study reveals that the ANFIS technique can be successfully used for estimating the mean velocity and discharge of natural streams by using only the inputs of water surface velocity and water surface slope. Copyright Springer Science+Business Media Dordrecht 2014

Suggested Citation

  • Onur Genç & Özgür Kişi & Mehmet Ardıçlıoğlu, 2014. "Determination of Mean Velocity and Discharge in Natural Streams Using Neuro-Fuzzy and Neural Network Approaches," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(9), pages 2387-2400, July.
    • Handle: RePEc:spr:waterr:v:28:y:2014:i:9:p:2387-2400
    DOI: 10.1007/s11269-014-0574-6
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    References listed on IDEAS

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    1. Hone-Jay Chu & Liang-Cheng Chang, 2009. "Application of Optimal Control and Fuzzy Theory for Dynamic Groundwater Remediation Design," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 23(4), pages 647-660, March.
    2. C. Iglesias & J. Martínez Torres & P. García Nieto & J. Alonso Fernández & C. Díaz Muñiz & J. Piñeiro & J. Taboada, 2014. "Turbidity Prediction in a River Basin by Using Artificial Neural Networks: A Case Study in Northern Spain," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(2), pages 319-331, January.
    3. Muhammet Emiroglu & Ozgur Kisi, 2013. "Prediction of Discharge Coefficient for Trapezoidal Labyrinth Side Weir Using a Neuro-Fuzzy Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(5), pages 1473-1488, March.
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    1. Onur Genç & Ali Dağ, 2016. "A machine learning-based approach to predict the velocity profiles in small streams," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(1), pages 43-61, January.
    2. Onur Genç & Ali Dağ, 2016. "A machine learning-based approach to predict the velocity profiles in small streams," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(1), pages 43-61, January.
    3. Tarate Suryakant Bajirao & Pravendra Kumar & Manish Kumar & Ahmed Elbeltagi & Alban Kuriqi, 2021. "Superiority of Hybrid Soft Computing Models in Daily Suspended Sediment Estimation in Highly Dynamic Rivers," Sustainability, MDPI, vol. 13(2), pages 1-29, January.

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