• editor.aipublications@gmail.com
  • Track Your Paper
  • Contact Us
  • ISSN: 2456-8635

International Journal Of Horticulture, Agriculture And Food Science(IJHAF)

Comparison and Evaluation of Support Vector Machine and Gene Programming in River Suspended Sediment Estimation (Case Study: Kashkan River)

Hamidreza Bababali , Reza Dehghani


International Journal of Horticulture, Agriculture and Food science(IJHAF), Vol-4,Issue-2, March - April 2020, Pages 53-61, 10.22161/ijhaf.4.2.5

Download | Downloads : | Total View : 470

Share

Simulation and evaluation of sediment are important issues in water resources management. Common methods for measuring sediment concentration are generally time consuming and costly and sometimes does not have enough accuracy. In this research, we have tried to evaluate sediment amounts, using Support Vector Machine (SVM), for Kashkanriver, Iran, and compare it with common Gene-Expression Programming. The parameter of flow discharge for input in different time lags and the parameter of sediment for output dhuring contour time (1998-2018) considered. Criteria of correlation coefficient, root mean square error, mean absolute error and Nash Sutcliff coefficient were used to evaluate and compare the performance of models. The results showed that two models estimate sediment discharge with acceptable accuracy, but in terms of accuracy, the support vector machine model had the highest correlation coefficient (0.994), minimum root mean square error (0.001ton/day) , mean absolute error(0.001 ton/day) and the Nash Sutcliff (0.988) hence was chosen the prior in the verification stage. Finally, the results showed that the support vector machine has great capability in estimating minimum and maximum sediment discharge values.

Suspended Sediment, Kashkan, Support Vector Machine, Gene Expression Programing.

[1] Chiang, J., Tsai, Y., Cheng, K., Lee, Y., Sun, M., Wei, J.,2014. Suspended Sediment Load Prediction Using Support Vector Machines in the Goodwin Creek Experimental Watershed. Geophysical Research Abstracts. 16(1):pp.234-247.
[2] Chiang, J., Tsai, Y., 2011. Suspended Sediment Load Estimate Using Support Vector Machines in Kaoping River Basin. International Conference on suspended sediment load
[3] Cohn, T.A.,Caulder, D.L., Gilroy, E.J., Zynjuk, L.D., 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.pp.2353–2363.
[4] Ferreira,C .,2001. Gene expression programming: a new adaptive algorithm for solving problems. Complex Systems,13(2):pp. 87–129.
[5] Forman, S.L., Pierson, J., 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,pp. 157– 176.
[6] Ghani, A.B., Azamathulla, H., 2011.Gene-Expression Programming for Sediment Transport in Sewer Pipe Systems. J. Pipeline Syst. Eng. Pract., 2(3):pp. 102-106.
[7] Ghorbani, M.A.,Khatibi, R., Goel, A., Azani, A., 2016.Modeling river discharge time series using support vector machine and artificial neural networks. Environmental Earth Sciences. 75(8):pp.675-685
[8] Ghorbani, M.A., Khatibi, R., Asadi, H., Yousefi,P ., 2012. Inter- Comparison of an Evolutionary Programming Model of Suspended Sediment Time-series whit other Local Model. INTECH. doi. org/10.5772/47801,pp. 255-282
[9] Ghorbani, M.A., Khatibi, R.,Goel, A., FazeliFard, M.H., Azani, A., 2016. Modeling river discharge time series using support vector machine and artificial neural networks. Environ Earth Sci. DOI: 10.1007/s12665-016-5435-6
[10] Jajarmizadeh, M., KakaeiLafdani, E., Harun, S., 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):pp.345-357
[11] Kecman, V., 2000. Learning and Soft Computing, Support Vector Machines, Neural Network and Fuzzy Logic Models.MIT Press,2000(ISBN 0- 262-11255-8. 608p).
[12] Khatibi, R., Naghipour, L., Ghorbani, M.A., 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.
[13] Misra, D., Oommen, T., Agarwal, A., Mishra, S.K., Thompson, A.M., 2009. Application and analysis of support vector machine based simulation for runoff and sediment yield, Biosystems engineering, 103(2): pp. 527 – 535,
[14] Shin, S., Kyung, D., Lee, S., Taik Kim, J., Hyun, J ., 2005. An application of support vector machines in bankruptcy prediction model. Expert Systems with Applications, 28:pp. 127-135.
[15] 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., pp.272–286.
[16] Vapnik,V.N .,1998. Statistical Learning Theory. Wiley, New York.
[17] White S. 2005. Sediment yield prediction and modeling. Hydrological Processes 19, pp.3053–3057.
[18] Xu, L., Wang, J., Guan, J., Huang, F ., 2007. A Support Vector Machine Model for Mapping of Lake Water Quality from Remote-Sensed Images. IC-MED. Vol. 1(1), pp. 57-66.
[19] Yang, C.T., 1996. Sediment Transport, Theory and Practice. McGraw-Hill, New York.