The week's pick
Random Articles
Reseach Article
Time Series Data Mining in Real Time Surface Runoff Forecasting through Support Vector Machine
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 98 - Number 3 |
| Year of Publication: 2014 |
| Authors: Vinayak Choubey, Satanand Mishra, S. K. Pandey |
10.5120/17163-7223
|
Vinayak Choubey, Satanand Mishra, S. K. Pandey . Time Series Data Mining in Real Time Surface Runoff Forecasting through Support Vector Machine. International Journal of Computer Applications. 98, 3 ( July 2014), 23-28. DOI=10.5120/17163-7223
Abstract
This study presents support vector machine based model for forecasting the runoff-rainfall events. A SVM based model is either implemented through Radial base or Gaussian based Kernel functions. SVM uses precipitation, temperature, sediment, rainfall, water level and discharge as input variable parameters. In this research the Sequential minimal optimization algorithm (SMO) has been implemented as an effective method for training support vector machines (SVMs) on classification tasks defined on large and sparse real time data sets. In this work, we generalized the SMO so that it can handle regression problem and by dividing datasets into test data and trained data performed future forecasting keeping four major evaluation parameters Root Mean Square Error (RMSE), Mean Absolute error (MAE), Mean Squared error (MSE) and correlation coefficient (CC). Study site for this research is Narmada basin reservoir hosahangabad catchment area and the experimental results on predicting the full natural flow of Narmada River indicates that support vector machine method performs far better and more accurate from the current forecasting practices (Artificial Neural Network).
References
- Ben-Hur A. and Weston J. , A User's Guide to Support Vector Machines.
- Behzad M. , Asghari K. , Eazi M. and Palhang M. , Generalized performance of SVM and NN in runoff modelling, 2009, ELSEVIER SCIENCES, Expert System with Application, Vol. 36, Issue 4, pp 7624-7629
- Botsis D. , Latinopulos P. and Diamantaras K. , 2011, Rainfall-Runoff Modeling Using Support Vector Regression and Artificial Neural Networks, CEST2011- Rhodes, Ref No. XXX, Greece.
- Bray M. and Han D. , 2004, Identification of Support Vector Machines for runoff modeling, journal of hydroinformatics, 06. 4, IWA Publishing.
- Burbridge Robert and Buxton Bernard, An Introduction to Support Vector Machines for Data Mining, UCL, Gower Street, WC1E 6BT, UK. .
- Choy Y. K. and Chan W. C. , 2010, Modeling of river discharges and rainfall using radial basis function networks based on support vector regression, International Journal of Systems Science, vol. 34, numbers14-15, pp763-773.
- Hopson T. M. and Webster P. J. , 2010, A 1-10-Day Ensemble forecasting Scheme for the major River Basins of Bangladesh: Forecasting Severe Floods of 2003-07, Journal of Hydrometeorology, DOI: 10. 1175/2009 JHM1006. 1, Vol. 11, pp 618-638.
- Mishra S. , Majumder S. , and Dwivedi V. K. , pattern discovery in hydrological time series data mining in a Sustainable Water resources Management And Climate Change Adaptation, Vol. -II, pp. 107-115, February 17-19, 2011, NIT Durgapur.
- Mishra S. , Dwivedi V. K. , Sarvanan C. and Pathak K. K. , Pattern Discovery in Hydrological Time Series Data Mining during the Monsoon Period of the High Flood Years in Brahmaputra River Basin, IJCA(0975-8887), doi-10. 5120/11397-6698, Vol. 67, No. 6, pp. 7-14,April, 2013.
- Mishra S. , Sarvanan C. , Dwivedi V. K. , and Pathak K. K. , Discovering Flood Rising Pattern in Hydrological Time Series Data Mining during the Pre Monsoon Period, Indian. Journal of Geo-Marine Science, Accepted on 12/01/2014.
- Mishra S. , Choubey V. , Pandey S. K. , and Shukla, J. P. , An Efficient Approach of Support Vector Machine for Runoff Forecasting, IJSER(ISSN 2229-5518) Vol, 5, Issue 3, March-2014,PP. 158-167.
- Mishra S. , Gupta P. . , Pandey S. K. , and Shukla, J. P. , An Efficient Approach of Artificial Neural Network for Runoff Forecasting, IJCA(0975-8887), Vol. 93, No. 8, April 2014, Accepted on 25/03/2014.
- Suliman A. , Nazri N. , Othman M. , Malek M. A. and K. Ruhana, Artificial Neural network and Support Vector machine in Flood Forecasting : A Review, 2013, International Conference on Computing and Informatics, ICOCI 2013, pp 327-332.
- Terzi O. , Monthly River Flow Forecasting by Data Mining Process, www. intechopen. com.
- Wang W-C, Chau K-W, Cheng C-T and Qiu L. , 2009, A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series, Journal Of Hydrology, Vol. 374, No. 3-4, pp 294-306.
- Xu J. , Wei J. and LIU Y. , 2010, Modeling Daily Runoff in a Large-Scale Basin based on Support Vector Machines, International conference on computer and Communication Technologies in Agriculture Engineering, 978-4244-6947-5/10.
Index Terms
Keywords