Please use this identifier to cite or link to this item: http://www.aiktcdspace.org:8080/jspui/handle/123456789/1849
Title: Estimation of Discharge Using LS-SVM and Model Trees
Authors: Magar, Rajendra
Keywords: Staff Publication - SoET
Staff Publication - CE
Issue Date: Nov-2016
Publisher: Journal of Water Resources and Ocean Science
Citation: V. G.Sayagavi, Shrikant Charhate, Rajendra Magar. Estimation of Discharge Using LS-SVM and Model Trees. Journal of Water Resources and Ocean Science. Vol. 5, No. 6, 2016, pp. 78-86.
Series/Report no.: Vol. 5;Issue 6
Abstract: In planning and management of any water resource systems prediction or estimation of runoff over the catchment is considered as a crucial factor. Many researchers over the past two decades addressed these problems by traditional methods as well as with some new techniques. This paper is describable and is focused on the capability of some data driven techniques such as Least Square Support Vector Machines (LS-SVM) and Model Trees with M5 algorithm. These methods were used to estimate runoff of various stations in the catchment area in Upper Krishna basin, Maharashtra State, India, and discussed here two stations namely Shigaon and Gudhe. The specialty of these catchment areas is Shigaon has large area and Gudhe has small area. This was done to see the model performance in both conditions. Additionally metrological data was used in the process to see the performance of models. The quantitative analysis was carried out to check the performance of the accuracy by considering standard statistical performance evaluation metrics and the scatter plots are drawn for evaluating qualitative performances of the developed models. The effect of the various metrological parameters as an input parameter for the rainfall was also investigated.The performance of both the tools was judged with various performance measures and it is found that the results are quite encouraging. LS-SVM models performed better since it has captured all the higher peak discharges for both catchments, indicating LS-SVM is best suited for large sized catchments and MT tool is best suited for the smaller sized catchments. However LS-SVM performance is better as compared to MT as modeling approaches are examined, using the long-term observations of yearly river flow discharges.
Description: Keywords: Hydrological Processes, Runoff, Least Square Support Vector Machines, Model Trees
URI: http://www.aiktcdspace.org:8080/jspui/handle/123456789/1849
ISSN: 2328-7969
Appears in Collections:Publications - CE

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