Prediction of flow rate of karstic springs using support vector machines
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Publication Date
9-4-2017
Publication Title
Hydrological Science Journal
Volume Number
62
Issue Number
13
Abstract
Complex void space structure and flow patterns in karstic aquifers render behaviour prediction of karstic springs difficult. Four support vector regression-based models are proposed to predict flow rates from two adjacent karstic springs in Greece (Mai Vryssi and Pera Vryssi). Having no accurate estimates of the groundwater flow pattern, we used four kernels: linear, polynomial, Gaussian radial basis function and exponential radial basis function (ERBF). The data used for training and testing included daily and mean monthly precipitation, and spring flow rates. The support vector machine (SVM) performance depends on hyper-parameters, which were optimized using a grid search approach. Model performance was evaluated using root mean square error and correlation coefficient. Polynomial kernel performed better for Mai Vryssi and the ERBF for Pera Vryssi. All models except one performed better for Pera Vryssi. Our models performed better than generalized regression neural network, radial basis function neural network and ARIMA models.
Keywords
Karst, Springs, Support vector machines, Hydrologic models, Streamflow
Document Type
Article
Digital Object Identifier (DOI)
https://doi.org/10.1080/02626667.2017.1371847
Language
English
Recommended Citation
Goyal, Manish Kumar; Sharma, Ashutosh; and Katsifarakis, Konstantinos L., "Prediction of flow rate of karstic springs using support vector machines" (2017). KIP Articles. 8303.
https://digitalcommons.usf.edu/kip_articles/8303
