Browse > Article
http://dx.doi.org/10.3741/JKWRA.2007.40.1.073

Neural Networks-Genetic Algorithm Model for Modeling of Nonlinear Evaporation and Evapotranspiration Time Series 1. Theory and Application of the Model  

Kim, Sung-Won (Dept. of Rail. and Civil Engr., Dongyang University)
Kim, Hung-Soo (School of Civil and Environ. Engr. Inha University)
Publication Information
Journal of Korea Water Resources Association / v.40, no.1, 2007 , pp. 73-88 More about this Journal
Abstract
The goal of this research is to develop and apply the generalized regression neural networks model(GRNNM) embedding genetic algorithm(GA) for the estimation and calculation of the pan evaporation(PE), which is missed or ungaged and of the alfalfa reference evapotranspiration ($ET_r$), which is not measured in South Korea. Since the observed data of the alfalfa 37. using Iysimeter have not been measured for a long time in South Korea, the Penman-Monteith(PM) method is used to estimate the observed alfalfa $ET_r$. In this research, we develop the COMBINE-GRNNM-GA(Type-1) model for the calculation of the optimal PE and the alfalfa $ET_r$. The suggested COMBINE-GRNNM-GA(Type-1) model is evaluated through training, testing, and reproduction processes. The COMBINE-GRNNM-GA(Type-1) model can evaluate the suggested climatic variables and also construct the reliable data for the PE and the alfalfa $ET_r$. We think that the constructive data could be used as the reference data for irrigation and drainage networks system in South Korea.
Keywords
GRNNM-GA; Pan evaporation; Alfalfa reference evapotranspiration; Penman-Monteith method;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 Holland, J.H. (1975). Adaptation in natural and artificial systems. University Michigan Press, Ann Arbor, MI
2 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 ]   DOI
3 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
4 Penman, H.L. (1948). 'Natural evaporation from open water, bare soil and grass.' Proc. R. Soc. London, 193, pp. 120-146   DOI
5 Food and Agriculture Organization(FAO). (1990). Report on the expert consultation on revision of FAO methodologies for crop water requirement. Land and Water Devel. Div., Rome, Italy
6 Neuroshell 2 (1993). Ward systems group. Inc., MD
7 Haykin, S. (1994). Neural networks : A comprehensive foundation. Macmillan College Pub. Comp., Inc., MA
8 Linacre, E.T. (1977). 'A simple formula for estimating evaporation rates in various climates, using temperature data alone.' Agric. Met., Vol. 23, No.6, pp. 409-424   DOI   ScienceOn
9 Liong, S.Y., Chan, W.T., and ShreeRam, J. (1995). 'Peak-flow forecasting with genetic algorithm and SWMM.' J. of Hvdrau. Engr., ASCE, Vol. 121, No.8, pp. 613-617   DOI   ScienceOn
10 Wasserman, P.D. (1993). Advanced methods in neural computing. Van Nostrand Reinhold, New York
11 Wright (1982). 'New evapotranspiration crop coefficients.' J. of Irrig. and Drain Engr., ASCE, Vol. 108, No.2, pp. 57-74
12 김성원, 이순탁, 조정석 (2001). '중소하천유역에서 Hybrid Neural Networks에 의한 수문학적 예측.' 한국수자원학회 논문집, 한국수자원학회, 제34권, 제4호, pp. 303-316   과학기술학회마을
13 건교부 (2006). 수자원 관리 종합정보 시스템 홈페이지 http://www.wamis.go.kr
14 기상청 (2006). 기상청 홈페이지 http://www.kma.go.kr
15 김성원 (2005). '신경망모형에 의한 홍수위예측의 신뢰성분석 1. 모형의 개발 및 적용.' 대한토목학회 논문집, 대한토목학회, 제25권, 제6B호, pp. 473-482
16 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   DOI
17 Bishop, C.M. (1994). ' Neural networks and their applications.' Rev. Scien. Instru. Vol. 65, pp. 1803-1832   DOI   ScienceOn
18 Bruton, J.M., McClendon, R.W., and Hoogenboom, G. (2000). 'Estimating daily pan evaporation with artificial neural networks.' Trans. of the ASAE, ASAE, Vol. 43, No.2, pp. 491-496   DOI
19 Burman, R.D. (1976). 'Intercontinental comparison of evaporation estimates.' J. of Irrig and Drain Engr., ASCE, Vol. 93, No.1, pp. 61-79
20 Christiansen, J.E. (1966). 'Estimating pan evaporation and evapotranspiration from climatic data.' In Irrigation and drainage Special Conference, ASCE, Las Vegas, NV, pp. 193-231
21 Gallant, S.I. (1993). Neural network learning and expert systems. MIT Press, Cambridge, MA
22 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   DOI   ScienceOn
23 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   DOI   ScienceOn
24 Jensen, M.E. (1974). Consumptive use cf water and irrigation water requirement. Report Tech. Comm. on Irrigation Water Requirements, Irrigation and Drainage, ASCE
25 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
26 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 ]   DOI
27 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
28 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   DOI   ScienceOn
29 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   DOI   ScienceOn
30 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
31 Specht, D.F. (1991), 'A general regression neural network' IEEE Trans. on Neural Networks, Vol. 2, No.6, pp. 568-576   DOI   ScienceOn
32 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   DOI   ScienceOn
33 Tsoukalas, L.H. and Uhrig, R.E. (1997). Fuzzy and neural approaches in engineering. John Wiley & Sons Incorporated, New York
34 Hargreaves, G.H. (1966). 'Consumptive use computations from evaporation pan data.' In Irrigation and Drainage Special Conference, ASCE, Las Vegas, NV, pp, 35-62
35 Veihmeyer, F.J.(1964). Evaporation : Handbook of applied hydrology. Chow, V.T.(ed), McGraw-Hill Book Co., New York