Volume 5, Issue 6, December 2016, Page: 78-86
Estimation of Discharge Using LS-SVM and Model Trees
V. G. Sayagavi, Civil Engineering Research Center, Datta Meghe College of Engineering, , Navi Mumbai, India
Shrikant Charhate, Department of Civil Engineering, Pillai HOC College of Engineering & Technology, Rasayani, India
Rajendra Magar, Department of Civil Engineering, AIKTC School of Engineering & Technology, New Panvel, India
Received: Aug. 28, 2016;       Accepted: Oct. 13, 2016;       Published: Nov. 7, 2016
DOI: 10.11648/j.wros.20160506.11      View  3008      Downloads  99
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.
Keywords
Hydrological Processes, Runoff, Least Square Support Vector Machines, Model Trees
To cite this article
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. doi: 10.11648/j.wros.20160506.11
Copyright
Copyright © 2016 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Reference
[1]
A. Elshorbagy et al. (2010), “Experimental investigation of the predictive capabilities “part 1 Hydrol. Earth Syst. Sci., 14, 1931–1941, 2010.
[2]
A. Shabri, and Suhartona, (2012), “Streamflow forecasting using least-square support vector machines”, Hydrological Sciences Journal, 57 (7), pp 1275-1293.
[3]
B. Bhattacharya, and D.P. Solomatine, (2005) “Neural networks and M5 model trees in modelling water level–discharge relationship”, Neurocomputing. 63, 381–396, 2005.
[4]
D. P. Solomatine, & Xue, Y. (2004b). “M5 model trees and neural networks: application to flood forecasting in the upper reach of the Huai River in China”.Journal of Hydrologic Engineering, 9, (6)491-501.2004.
[5]
D. P. Solomatine, and K. Dulal, (2003), “Model tree as an alternative to neural network in rainfall-runoff modeling” Hydrological Sciences,48(3), 399–41, 2003.
[6]
E. K. Onyari and F. M. Ilunga (2013), “Application of MLP Neural Network and M5P Model Tree in Predicting Streamflow: A Model Study of Luvuvhu Catchment, South Africa.” International Journal of Innovation, Management and Technology, Vol. 4, No. 1, February 2013.
[7]
H. Zia, Nick Harris, Geoff Merritt, Mark Rivers. (2015) “Validation of a Low Complexity Machine Learning Discharge Predictive Model.
[8]
I. H. Witten and E. Frank, (2005) “Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations,” Morgan Kaufmann, San Francisco, California, USA, 2005.
[9]
J. R. Quinlan, (1992) “Learning with continuous classes”, Proc., 5thAustralian Joint Conf. on Artificial Intelligence, Adams & Sterling, eds., World Scientific, Singapore, 343–348, 1992.
[10]
M Pawar, Samveda Mohite, Rushikesh Deshmukh, Nivedita Bhirud (2016) “Comparison of Various Data-Driven Modelling Techniques for Inflow Analysis”. IOSR Journal of Computer Engineering (IOSR-JCE) Volume 18, Issue 2, Ver. V (Mar-Apr. 2016), PP 01-02.
[11]
N Zhang, Tilaye Alemayehu, Pradeep Behera (2015),” Nonlinear Autoregressive (NAR) Forecasting Model for Potomac River Stage using Least Squares Support Vector Machines (LS-SVM).” International Journal of Innovative Technology and Exploring Engineering (IJITEE) ISSN: 2278-3075, Volume-4 Issue-9, February 2015.
[12]
Nian Zhang, Charles Williams and Pradeep Behera (2014), ‘‘Water Quantity Prediction Using Least Squares Support Vector Machines (LSSVM) Method” SystemicsCybernetics and Informatics Volume 12-Number 4-Year 2014.
[13]
P. Bhagwat and Maity R., (2013), “Hydro-climatic stream flow prediction using Least Square Support Vector Regression,” ISH Journal of Hydraulic Engineering, vol. 19, No. 3, pp 320-328.
[14]
S Ismail and AniShabri, (2014) ”Stream Flow Forecasting using Principal Component Analysis and Least Square Support Vector Machine.” J. Appl. Sci. & Agric., 9(11): 170-180, 2014.
[15]
S. N. Londhe, and S. B. Charhate, (2010) “Comparison of data driven modeling techniques for river flow forecasting”, Hydrological Sciences. 55(7), pp1163-1174, 2010.
[16]
Suykens, K. A. J, Brabanter, D. J., Lukas, L. and Vandewalle, J. (2001), “Weighted least support vector machines: robustness and sparse approximation”, ELSEVIER Science Neurocomputing: 48, 0925-2312/02, pp 85-105. Doi: 10.1023/A: 101862860974.
[17]
T Mandal and V. Jothiprakash. (2012)“Short-term rainfall prediction using ANN and MT techniques” ISH Journal of Hydraulic Engineering Volume18, Issue 1, 2012 pages 20-26.
[18]
X. Yunrong and J. Liangzhong (2009), "Water Quality Prediction Using LS - SVM and Particle Swarm Optimization".
[19]
Zaher Mundher Yaseen, Ozgur Kisi, Vahdettin Demir (2016), “Enhancing Long-Term Streamflow Forecasting and Predicting using Periodicity Data Component: Application of Artificial Intelligence” Journal of Water Resources Management pp 1-27 on line July 2016 DOI 10.1007/s11269-016-1408-5.
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