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

Onur Genç () and Ali Dağ ()
Additional contact information
Onur Genç: Melikşah University
Ali Dağ: Auburn University

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2016, vol. 30, issue 1, No 3, 43-61

Abstract: 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.

Keywords: Velocity profile; Small stream; Machine learning; ANNs; SVMs; k-NN (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://link.springer.com/10.1007/s11269-015-1123-7 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:waterr:v:30:y:2016:i:1:d:10.1007_s11269-015-1123-7

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11269

DOI: 10.1007/s11269-015-1123-7

Access Statistics for this article

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA) is currently edited by G. Tsakiris

More articles in Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA) from Springer, European Water Resources Association (EWRA)
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().