MATEC Web Conf.
Volume 111, 2017Fluids and Chemical Engineering Conference (FluidsChE 2017)
|Number of page(s)||7|
|Section||Advances in Fluids Flow and Mechanics|
|Published online||20 June 2017|
Prediction of Stream Flow in Humid Tropical Rivers by Support Vector Machines
1 Civil Engineering Programme., University College of Technology Sarawak, Sibu, Sarawak
2 Civil Engineering Dept., Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia
* Corresponding author: email@example.com
Stream flow (SF) prediction is considered as a very complex due to the hydrological systems of surface water are complex and dynamic. The reliable prediction of stream flow (SF) can be performed by either conceptual or data-driven based models. In the modelling of hydrological processes, the support vector machine (SVM) is a novel, data-driven approach. Hence, six SVM-based models were generated in this study to predict real time hourly SF in the Selangor River Basin from the water level and rainfall of upstream stations. These models composed of six different combinations of input variables and were trained and tested under hourly records of SF, rainfall, and water level over one year (2011). Among the SVM-based models, SVM-M6, which has nine input variables, was the most effective. Under the training and testing data sets, its correlation coefficient and mean absolute error values were 0.992, 0.953, 0.061 and 0.253 respectively.
Key words: stream flow / data-driven based models / support vector machine / prediction / hydrological modelling
© The Authors, published by EDP Sciences, 2017
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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