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A machine learning-based approach to predict the velocity profiles in small streams

Author

Listed:
  • Onur Genç

    (Melikşah University)

  • Ali Dağ

    (Auburn University)

Abstract

This article addresses the determination of velocity profile in small streams by employing powerful machine learning algorithms that include artificial neural networks (ANNs), support vector machine (SVMs), and k-nearest neighbor algorithms (k-NN). Therefore, this study also aims to present a reliable and low-cost method for predicting velocity profile. The data set used in this study was achieved by field measurements performed by using the acoustic Doppler velocimeter (ADV) between 2005 and 2010, in Central Turkey. The eight observational variables and calculated non-dimensional parameters were used as inputs to the models for predicting the target values, u (point velocity in measured verticals). Performances of prediction methods were determined via 10-fold cross-validation approach. The comparative results revealed that k-NN algorithms outperformed the other two machine learning models, with the R value of 0.98 ± 0.0069 and the MAE value of 0.053 ± 0.0075, while ANNs and SVMs models have the R values of 0.95 ± 0.0085 and 0.89 ± 0.0046, the MAE values of 0.085 ± 0.0077 and 0.099 ± 0.0117, respectively. Importance of the predictor variables for ANNs and SVMs models were also presented by using sensitivity analysis.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:waterr:v:30:y:2016:i:1:d:10.1007_s11269-015-1123-7
    DOI: 10.1007/s11269-015-1123-7
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    References listed on IDEAS

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    1. 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.
    2. Afiq Hipni & Ahmed El-shafie & Ali Najah & Othman Karim & Aini Hussain & Muhammad Mukhlisin, 2013. "Daily Forecasting of Dam Water Levels: Comparing a Support Vector Machine (SVM) Model With Adaptive Neuro Fuzzy Inference System (ANFIS)," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(10), pages 3803-3823, August.
    3. Afiq Hipni & Ahmed El-shafie & Ali Najah & Othman Karim & Aini Hussain & Muhammad Mukhlisin, 2013. "Erratum to: Daily Forecasting of Dam Water Levels: Comparing a Support Vector Machine (SVM) Model With Adaptive Neuro Fuzzy Inference System (ANFIS)," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(11), pages 4113-4113, September.
    4. Mahmood Akbari & Peter Overloop & Abbas Afshar, 2011. "Clustered K Nearest Neighbor Algorithm for Daily Inflow Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(5), pages 1341-1357, March.
    5. Mack, Y. P. & Rosenblatt, M., 1979. "Multivariate k-nearest neighbor density estimates," Journal of Multivariate Analysis, Elsevier, vol. 9(1), pages 1-15, March.
    6. Fereshteh Modaresi & Shahab Araghinejad, 2014. "A Comparative Assessment of Support Vector Machines, Probabilistic Neural Networks, and K-Nearest Neighbor Algorithms for Water Quality Classification," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(12), pages 4095-4111, September.
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