KSCE Journal of Civil and Environmental Engineering Research (대한토목학회논문집)

Korean Society of Civil Engeneers (대한토목학회)
권호 표지
DOI QR코드

DOI QR Code

The Integrational Operation Method for the Modeling of the Pan Evaporation and the Alfalfa Reference Evapotranspiration

증발접시 증발량과 알팔파 기준증발산량의 모형화를 위한 통합운영방법

  • 김성원 (동양대학교 철도토목학과) ;
  • 김형수 (인하대학교 환경토목공학부)
  • Received : 2007.08.13
  • Accepted : 2008.02.15
  • Published : 2008.03.31
Download PDF
⟨ Previous Next ⟩

Abstract

The goal of this research is to develop and apply the integrational operation method (IOM) for the modeling of the monthly pan evaporation (PE) and the alfalfa reference evapotranspiration ($ET_r$). Since the observed data of the alfalfa $ET_r$ using lysimeter have not been measured for a long time in Republic of Korea, Penman-Monteith (PM) method is used to estimate the observed alfalfa $ET_r$. The IOM consists of the application of the stochastic and neural networks models, respectively. The stochastic model is applied to generate the training dataset for the monthly PE and the alfalfa $ET_r$, and the neural networks models are applied to calculate the observed test dataset reasonably. Among the considered six training patterns, 1,000/PARMA(1,1)/GRNNM-GA training pattern can evaluate the suggested climatic variables very well and also construct the reliable data for the monthly PE and the alfalfa $ET_r$. Uncertainty analysis is used to eliminate the climatic variables of input nodes from 1,000/PARMA(1,1)/GRNNM-GA training pattern. The sensitive and insensitive climatic variables are chosen from the uncertainty analysis of the input nodes. Finally, it can be to model the monthly PE and the alfalfa $ET_r$ simultaneously with the least cost and endeavor using the IOM.

본 연구의 목적은 월별 증발접시 증발량과 월별 알팔파 기준증발산량의 모형화를 위한 통합운영방법을 개발하고 적용하는데 있다. 우리나라에서는 장기간동안 증발산계를 이용하여 알팔파 기준증발산량의 관측이 시행되지 않고 있으므로, Penman-Monteith(PM) 공식을 이용하여 산정된 값을 계측된 알팔파 기준증발산량으로 가정하였다. 통합운영 방법은 각각 추계학적 모형과 신경망모형의 적용으로 구성되어 있다. 추계학적 모형은 월별 증발접시 증발량과 월별 알팔파 기준증발산량에 대한 훈련자료의 모의발생을 위하여 적용되었으며, 신경망모형은 관측된 테스트자료를 합리적으로 계산하기 위하여 적용되었다. 고려된 6가지의 훈련패턴 중에서 1,000/PARMA(1,1)/GRNNM-GA 훈련패턴은 제시된 기상인자를 가장 양호하게 평가하였으며, 또한 월별 증발접시 증발량과 월별 알팔파 기준증발산량의 신뢰성있는 자료를 구축할 수 있다. 불확실성 분석은 1,000/PARMA(1,1)/GRNNM-GA 훈련패턴 으로부터 입력층노드의 기상인자를 제거하기 위하여 이용되었으며, 민감하거나 민감하지 않는 기상인자들이 불확실성 분석을 통하여 선택되어 진다. 마지막으로 통합운영방법을 이용하여 최소비용과 노력으로 월별 증발접시 증발량과 월별 알팔파 기준증발산량을 동시에 모형화가 가능하게 되었다.

Keywords

References

  1. 건설교통부(2007) 수자원 관리 종합정보 시스템 홈페이지 http://www.wamis.go.kr
  2. 기상청(2007) 기상청 홈페이지 http://www.kma.go.kr
  3. 김성원(2003) 추계학적모형과 신경망모형을 연계한 병렬저수지군의 유입량산정. 한국수자원학회 논문집, 한국수자원학회, 제36권, 제2호, pp. 195-209
  4. 김성원(2005) 신경망모형에 의한 홍수위예측의 신뢰성분석 1. 모형의 개발 및 적용. 대한토목학회 논문집, 대한토목학회, 제25권, 제6B호, pp. 473-482
  5. 김성원, 이순탁, 조정석(2001) 중소하천유역에서 Hybrid Neural Networks에 의한 수문학적 예측. 한국수자원학회 논문집, 한국수자원학회, 제34권, 제4호, pp. 303-316
  6. Allen, R.G., Jensen, M.E., Wright, J.L., and Burman, R.D. (1989) Operational estimates of reference evapotranspiration. Agrono. J., Vol. 81, No. 4, pp. 650-662 https://doi.org/10.2134/agronj1989.00021962008100040019x
  7. Ayyub, B.M. and McCuen, R.H. (1997) Probability, Statistics, and Reliability for Engineers and Statistics, Chapman & Hall/CRC, New York, NY
  8. Bishop, C.M. (1994) Neural networks and their applications. Rev. Scien. Instru. Vol. 65, pp. 1803-1832 https://doi.org/10.1063/1.1144830
  9. Burman, R.D. (1976) Intercontinental comparison of evaporation estimates. J. of Irrig. and Drain. Engr., ASCE, Vol. 93, No. 1, pp. 61-79
  10. Christiansen, J.E. (1966) Estimating pan evaporation and evapotranspiration evapotranspiration from climatic data. In Irrigation and drainage Special Conference, ASCE, Las Vegas, NV, pp. 193-231
  11. Deb, K. (2001) Multi-objective optimization using evolutionary algorithms, John Wiley & Sons, Chichester
  12. Fahlman, S.E. and Lebiere, C. (1990) The cascade-correlation learning architecture. Rep. CMU-CS-90-100, Carnegie Mellon University, Pittsburgh, PA
  13. Food and Agriculture Organization (FAO) (1991) Report on the expert consultation on revision of FAO methodologies for crop water requirement, Land and Water Devel. Div., Rome, Italy
  14. Hargreaves, G.H. (1966) Consumptive use computations from evaporation pan data. In Irrigation and Drainage Special Conference, ASCE, Las Vegas, NV, pp. 35-62
  15. Haykin, S. (1999) Neural networks : A comprehensive foundation, Prentice Hall, NJ
  16. Hirsch, R.M. (1979) Synthetic hydrology and water supply reliability. Water Resour. Res., Vol. 15, No. 6, pp. 1603-1615 https://doi.org/10.1029/WR015i006p01603
  17. Holland, J.H. (1975) Adaptation in natural and artificial systems, University Michigan Press, Ann Arbor, MI
  18. Howell, T.A., Phene, C.J., and Meek, D.W. (1983) Evaporation from screened Class A pans in a semi-arid environment. Agric. Met., Vol. 29, No. 1, pp. 111-124 https://doi.org/10.1016/0002-1571(83)90044-4
  19. Jain, A. and Srinivasulu, S. (2004) Development of effective and efficient rainfall-runoff models using integration of deterministic, real-coded genetic algorithms and artificial neural network technique. Water Resour. Res., Vol. 40, No. 4, W04302 https://doi.org/10.1029/2003WR002355
  20. Jensen, M.E. (1974) Consumptive use of water and irrigation water requirement, Report Tech. Comm. on Irrigation Water Requirements, Irrigation and Drainage, ASCE
  21. Jensen, M.E., Burman, R.D., and Allen, R.G. (1990) Evapotranspiration and irrigation water requirements, ASCE Manual and Report on Engineering Practice No. 70, ASCE, NY
  22. Kim, S. and Kim, H.S. (2006) Estimation of the reference evapotranspiration using neural networks model and limited climatic variables. Proc. World Environmental & Water Resources Congress 2006, ASCE/EWRI, Omaha, NE. [ Printed in CD ]
  23. Kim, S. and Kim, H.S. (2008) Uncertainty reduction of the flood stage forecasting using neural networks model. J. of Amer. Water Resour. Associ., Vol. 44, No. 1, pp. 148-165 https://doi.org/10.1111/j.1752-1688.2007.00144.x
  24. Kim, S. and Kim, H.S. (In press) Neural networks and genetic algorithm approach for nonlinear evaporation and evapotranspiration modeling, J. of Hydro., Accepted
  25. Kim, S. and Jee, H. (2006) An expansion of the ungaged pan evaporation using neural networks model in rural regions, South Korea. Proc. World Environmental & Water Resources Congress 2006, ASCE/EWRI, Omaha, NE. [ Printed in CD ]
  26. Kohler, M.A., Nordenson, T.J. and Fox, W.E. (1955) Evaporation from pans on lakes, US Department of Commerce, Weather Bureau Research Paper 38, Washington, DC
  27. Kumar, M., Raghuwanshi, N.S., Singh, R., Wallender, W.W., and Pruitt, W.O. (2002) Estimating evapotranspiration using artificial neural network. J. of Irrig. and Drain. Engr., ASCE, Vol. 128, No. 4, pp. 224-233 https://doi.org/10.1061/(ASCE)0733-9437(2002)128:4(224)
  28. Liong, S.Y., Chan, W.T., and ShreeRam, J. (1995) Peak-flow forecasting with genetic algorithm and SWMM. J. of Hydrau. Engr., ASCE, Vol. 121, No. 8, pp. 613-617 https://doi.org/10.1061/(ASCE)0733-9429(1995)121:8(613)
  29. McCuen, R.H. (1993) Microcomputer applications in statistical hydrology, Prentice Hall, NJ
  30. Mishra, A.K., Desai, V.R., and Singh, V.P. (2007) Drought forecasting using a hybrid stochastic and neural network model J. of Hydro. Engr., ASCE, Vol. 12, No. 6, pp. 626-638 https://doi.org/10.1061/(ASCE)1084-0699(2007)12:6(626)
  31. Monteith, J.L. (1965) The state and movement of water in living organism. Proc., Evaporation and Environment, XIXth Symp., soc. For Exp. Biol., Swansea, Cambridge Univ. Press, NY, pp. 205-234
  32. Neuroshell 2 (1993) Ward systems group, Inc., MD
  33. Penman, H.L. (1948) Natural evaporation from open water, bare soil and grass. Proc. R. Soc. London, 193, pp. 120-146
  34. Powell, M.J.D. (1987) Radial basis functions for multivariable interpolation: A review. In Algorithms for the Approximation of Functions and Data, Mason, J.C., and Cox, M.G., eds., Oxford, England : Clarenden Press, pp. 143-167
  35. Salas, J.D. and Abdelmohsen, M. (1993) Initialization for generating single site and multisite low order PARMA processes. Water Resour. Res., Vol. 29, No. 6, pp. 1771-1776 https://doi.org/10.1029/93WR00371
  36. Salas, J.D., Delleur, J.R., Yevjevich, V., and Lane, W.L. (1980) Applied modeling of hydrologic time series, Water Resor. Pub., Littleton, CO
  37. Salas, J.D., Markus, M., and Tokar, A.S. (2000) Streamflow forecasting based on artificial neural networks. In Artificial neural networks in hydrology, Govindaraju, R.S., and Ramachandra Rao, A., eds., Water sci. and tec. lib. Vol. 36, Kluwer Academic Press, pp. 23-51
  38. Salas, J.D., Smith, R.A., Tabios III, G.Q., and Heo, J.H. (2001) Statistical computing techniques in water resources and environmental engineering, Unpublished book in CE622, Colorado State University, Fort Collins, CO
  39. Sivakumar, B., Jayawardena, A.W., and Fernando, T.M.K.G. (2002) River flow forecasting : use of phase-space reconstruction and artificial neural networks approaches. J. of Hydrol., Vol. 265, pp. 225-245 https://doi.org/10.1016/S0022-1694(02)00112-9
  40. Specht, D.F. (1991) A general regression neural network. IEEE Trans. on Neural Networks, Vol. 2, No. 6, pp. 568-576 https://doi.org/10.1109/72.97934
  41. Sudheer, K.P., Gosain, A.K., and Ramasastri, K.S. (2003) Estimating actual evapotranspiration from limited climatic data using neural computing technique. J. of Irrig. and Drain. Engr., ASCE, Vol. 129, No. 3, pp. 214-218 https://doi.org/10.1061/(ASCE)0733-9437(2003)129:3(214)
  42. Sudheer, K.P., Gosain, A.K., Rangan, D.M., and Saheb, S.M. (2002) Modeling evaporation using an artificial neural network algorithm. Hydro. Process., Vol. 16, pp. 3189-3202 https://doi.org/10.1002/hyp.1096
  43. Tao, P.C. and Delleur, J.W. (1976) Seasonal and nonseasonal ARMA models in hydrology J. of Hydraul. Div., ASCE, Vol. 102, No. HY10, pp. 1591-1599
  44. Tokar, A.S. and Johnson, P.A. (1999) Rainfall-runoff modeling using artificial neural networks. J. of Hydro. Engr., ASCE, Vol. 4, No. 3, pp. 232-239 https://doi.org/10.1061/(ASCE)1084-0699(1999)4:3(232)
  45. Tsoukalas, L.H. and Uhrig, R.E. (1997) Fuzzy and neural approaches in engineering, John Wiley & Sons Incorporated, New York, NY
  46. Veihmeyer, F.J. (1964) Evaporation: Handbook of applied hydrology, Chow, V.T. (ed.), McGraw-Hill Book Co., New York, NY
  47. Wasserman, P.D. (1993) Advanced methods in neural computing, Van Nostrand Reinhold, New York, NY
  48. Wright (1982) New evapotranspiration crop coefficients. J. of Irrig. and Drain. Engr., ASCE, Vol. 108, No. 2, pp. 57-74