한국환경농학회지 (Korean Journal of Environmental Agriculture)

  • 제33권1호
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  • Pages.37-43
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  • 2014
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  • 1225-3537(pISSN)
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  • 2233-4173(eISSN)
한국환경농학회 (The Korean Society of Environmental Agriculture)
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유전자 알고리즘과 회귀식을 이용한 오염부하량의 예측

Estimation of Pollutant Load Using Genetic-algorithm and Regression Model

  • Park, Youn Shik (Department of Agricultural and Biological Engineering, Purdue University)
  • 투고 : 2014.01.22
  • 심사 : 2014.02.07
  • 발행 : 2014.03.31
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초록

BACKGROUND: Water quality data are collected less frequently than flow data because of the cost to collect and analyze, while water quality data corresponding to flow data are required to compute pollutant loads or to calibrate other hydrology models. Regression models are applicable to interpolate water quality data corresponding to flow data. METHODS AND RESULTS: A regression model was suggested which is capable to consider flow and time variance, and the regression model coefficients were calibrated using various measured water quality data with genetic-algorithm. Both LOADEST and the regression using genetic-algorithm were evaluated by 19 water quality data sets through calibration and validation. The regression model using genetic-algorithm displayed the similar model behaviors to LOADEST. The load estimates by both LOADEST and the regression model using genetic-algorithm indicated that use of a large proportion of water quality data does not necessarily lead to the load estimates with smaller error to measured load. CONCLUSION: Regression models need to be calibrated and validated before they are used to interpolate pollutant loads, as separating water quality data into two data sets for calibration and validation.

키워드

참고문헌

  1. Carey, R.O., Migliaccio, K.W., Brown, M.T., 2011. Nutrient discharges to Biscayne Bay, Florida: trends, loads, and a pollutant index, Sci. Total Environ. 409, 530-539. https://doi.org/10.1016/j.scitotenv.2010.10.029
  2. Coynel, A., Schafer, J., Hurtrez, J., Dumas, J., Etcheber, H., Blanc, G., 2004. Sampling frequency and accuracy of SPM flux estimates in two contrasted drainage basins, Sci. Total Environ. 330, 233-247. https://doi.org/10.1016/j.scitotenv.2004.04.003
  3. Das, S.K., Ng, A.W.M., Perera, B.J.C., Assessment of nutrient and sediment loads in the Yarra river catchment, 19th International Congress on Modelling and Simulation, Perth, Australia, 12-16 December 2011, http://mssanz.org.au/modsim, pp. 3490-3496.
  4. Dornblaser, M.M., Striegl, R.G., 2009. Suspended sediment and carbonate transport in the Yukon river basin, Alska: Flouxes and potential future responses to climate change, Water Resour. Res. 45, W06411, doi:10.1029/ 2008WR007546
  5. Henjum, M.B., Hozalski, R.M., Wennen, C.R., Novak, P.J., Arnold, W.A., 2010. A comparison of total maximum daily load (TMDL) calculations in urban streams using near real-time and periodic sampling data, J. Environ. Monit. 12, 234-241. https://doi.org/10.1039/b912990a
  6. Horowitz, A.J., 2003. An evaluation of sediment rating curves for estimating suspended sediment concentrations for subsequent flux calculations, Hydrolog. Proc. 17, 3387-3409. https://doi.org/10.1002/hyp.1299
  7. Johnes, P.J., 2007. Uncertainties in annual riverine phosphorus load estimation: Impact of load estimation methodology, sampling frequency, baseflow index and catchment population density, J. Hydrol. 332, 241-258. https://doi.org/10.1016/j.jhydrol.2006.07.006
  8. King, K.W., Harmel, R.D., 2003. Considerations in selecting a water quality sampling strategy, Trans. ASAE. 46, 63-73.
  9. Kronvang, B., Bruhn, A.J., 1996. Choice of sampling strategy and estimation method for calculating nitrogen and phosphorus transport in small lowland streams, Hydrolog. Proc. 10, 1483-1501.
  10. Oh, J., Sankarasubramanian, A., 2011. Interannual hydroclimatic variability and its influence on winter nutrients variability over the southeast United States, Hydrol. Earth Sys. Sci. Discuss. 8, 10935-10971. https://doi.org/10.5194/hessd-8-10935-2011
  11. Park, Y., Kim, J., Park, J., Hong, J.H., Choi, D.H., Kim, T., Choi, J., Ahn, J., Kim, K.S., Lim, K.J., 2007. Evaluation of SWAT applicability to simulation of sediment behaviors at the Imha-Dam watershed, J. Korean Soc. Water Qual. 23, 467-473.
  12. Robertson, D.M., Roerish, E.D., 1999. Influence of various water quality sampling strategies on load estimates for small streams, Water Resour. Res. 35, 3747-3759. https://doi.org/10.1029/1999WR900277
  13. Robertson, D.M., 2003. Influence of different temporal sampling strategies on estimating total phosphorus and suspended sediment concentration and transport in small streams, J. Amer. Water Resour. Assoc. 39, 1281-1308. https://doi.org/10.1111/j.1752-1688.2003.tb03709.x
  14. Santhi, C., Arnold, J.G., Williams, J.R., Dugas, W.A., Srinivasan, R., Hauck, L.M., 2001. Validation of the SWAT model on a large river basin with point and nonpoint sources, J. Amer. Water Resour. Assoc. 37, 1169-1188. https://doi.org/10.1111/j.1752-1688.2001.tb03630.x
  15. Togan, V., Daloglu, A.T., 2008. An improved genetic algorithm with initial population strategy and self-adaptive member grouping, Comput. & Struct. 86, 1204-1218. https://doi.org/10.1016/j.compstruc.2007.11.006

피인용 문헌

  1. Evaluation of Regression Models in LOADEST to Estimate Suspended Solid Load in Hangang Waterbody vol.57, pp.2, 2015, https://doi.org/10.5389/KSAE.2015.57.2.037