Comparison and evaluation of intelligent models for river suspended sediment estimation (case study: Kakareza River, Iran)

Document Type : Research Paper

Authors

Lorestan University, Khorramabad, Iran

Abstract

Sediment transport constantly influences river and civil structures and the lack of
information about its exact amount makes management efforts less effective. Hence,
achieving a proper procedure to estimate the sediment load in rivers is important. We used
support vector machine model to estimate the sediments of the Kakareza River in Lorestan
Province and the results were compared with those obtained by gene expression
programming. The parameter of flow discharge for input in different time lags and the
parameter of sediment for output during 1992-2012 were considered. Criteria including
correlation coefficient, root mean square error and mean absolute error were used to
evaluate and also compare the performance of models. With regards to accuracy, the
support vector machine model showed the highest correlation coefficient (0.994), minimum
root mean square error (0.001 ton/day) and the mean absolute error (0.001 ton/day) which
was initiated at verification stage. The results also showed that the support vector machine
has great capability to estimate the minimum and maximum values for sediment discharge.

Keywords

Main Subjects

References

Chiang, J., Tsai, Y., Cheng, K., Lee, Y., Sun, M., and Wei, J. 2014. Suspended Sediment Load Prediction Using Support Vector Machines in the Goodwin Creek Experimental Watershed. Geophysical Research Abstracts, 16(1), 234-247.
Chiang, J., Tsai, Y., 2011. Suspended Sediment Load Estimate Using Support Vector Machines in Kaoping River Basin. International Conference onsuspended sediment load.
Cohn, T.A., Caulder, D.L., Gilroy, E.J., Zynjuk, L.D., and Summers, R.M. 1992. The validity of a simple statistical model for estimating fluvial constituent loads: an empirical study involving nutrient loads entering Chesapeake Bay. Water Resources Research, 28, 2353–2363.
Ferreira, C. 2001. Gene expression programming: a new adaptive algorithm for solving problems. Complex Systems, 13(2), 87–129.
Forman, S.L., Pierson, J., and Lepper, K. 2000. Luminescence geochronology. In: Sowers, J.M., Noller, J.S., Lettis, W.R. (Eds.), Quaternary Geochronology: Methods and Applications. American Geophysical Union Reference Shelf 4, Washington DC, 157–176.
Ghani, A.B., and Azamathulla, H. 2011.Gene-Expression Programming for Sediment Transport in Sewer Pipe Systems. J. Pipeline Syst. Eng. Pract., 2(3), 102-106.
Ghorbani, M.A., Khatibi,  R., Goel, A., and Azani, A. 2016. Modeling river discharge time series using support vector machine and artificial neural networks. Environmental Earth Sciences, 75(8), 675-685.
Ghorbani, M.A., Khatibi, R., Asadi, H., and Yousefi, P. 2012. Inter- Comparison of an Evolutionary Programming Model of Suspended Sediment Time-series whit other Local Model. INTECH. Pp, 255-282.
Ghorbani, M.A., Khatibi, R., Goel, A., FazeliFard, M.H., and Azani, A. 2016. Modeling river discharge time series using support vector machine and artificial neural networks. Environ Earth Sci.

Jajarmizadeh, M., Kakaei Lafdani, E., Harun, S., and Ahmadi, A. 2015. Application of SVM and SWAT models for monthly streamflow prediction, a case study in South of Iran. KSCE Journal of Civil Engineering, 19(1), 345-357.

Kecman, V. 2000. Learning and Soft Computing, Support Vector Machines, Neural Network and Fuzzy Logic Models. MIT Press, 2000 608p).
Khatibi, R., Naghipour, L., Ghorbani, M.A., and Aalami, M.T. 2012. Predictability of relative humidity by two artificial intelligence techniques using noisy data from two Californian gauging stations. Neural computing and application, pp. 643-941.
Misra, D., Oommen, T., Agarwal, A., Mishra, S.K., and Thompson, A.M. 2009. Application and analysis of support vector machine based simulation for runoff and sediment yield, Biosystems engineering, 103(2), 527–535.
Shin, S., Kyung, D., Lee, S., Taik Kim, J., and Hyun, J. 2005. An application of support vector machines in bankruptcy prediction model. Expert Systems with Applications, 28, 127-135.
Singh, V.P., Krstanovic, P.F., Lane, L.J., 1998. Stochastic models of sediment yield. In: Anderson, M.G. (Ed.), Modeling Geomorphological Systems, Vol. 2. John Wiley and Sons Ltd., 272–286.
Vapnik, V.N. 1998.  Statistical Learning Theory. Wiley, New York.
White S. 2005. Sediment yield prediction and modeling. Hydrological Processes 19, pp.3053–3057.
Xu, L., Wang, J., Guan, J., and Huang, F. 2007. A Support Vector Machine Model for Mapping of Lake Water Quality from Remote-Sensed Images. IC-MED. 1(1), 57-66.
Yang, C.T. 1996. Sediment Transport, Theory and Practice. McGraw-Hill, New York.
Environmental Resources Research
Volume 6, Issue 2
July 2018
Pages 139-148

Files

Share

How to cite

Statistics