Runoff Estimation by Machine Learning Methods and Application to the Euphrates Basin in Turkey
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VIEW THE REPLYPublication: Journal of Hydrologic Engineering
Volume 19, Issue 5
Abstract
Machine learning (ML) techniques have been popular data-driven approaches for hydrological studies during the last few decades owing to their capability to identify complex nonlinear relationships between input and output data without the requirement for physical understanding of the system. This paper aims to predict river flows using various ML methods [feed forward neural network (FFNN), adaptive neuro fuzzy inference system (ANFIS), and genetic programming (GP)] and also a non-ML method (multiple linear regression) in the Euphrates Basin in Turkey. Infilling the missing data in the runoff record of the selected stations in Euphrates Basin is also an objective of this study. The ML methods were applied to the three main sub-basins of the Euphrates Basin, namely the Upper, Middle, and Lower Euphrates Basins. ANFIS and FFNN methods were the most successful ML methods for runoff estimation in the Upper and Lower Euphrates Basins, whereas GP and ANFIS models were the best ones in the Middle Euphrates Basin. Missing flow data were constructed successfully in the selected stations.
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Published In
Journal of Hydrologic Engineering
Volume 19 • Issue 5 • May 2014
Pages: 1015 - 1025
Copyright
© 2014 American Society of Civil Engineers.
History
Received: Nov 27, 2012
Accepted: Jun 13, 2013
Published online: Jun 15, 2013
Discussion open until: Nov 15, 2013
Published in print: May 1, 2014
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