Journal of Hydrology (2008) 351, 299– 317
available at
journal homepage: ate/jhydrol
works and ic algorithm approach for
nonlinear evaporation and evapotranspiration
modeling
Sungwon Kim a,*, Hung Soo Kim b
a Department of Railroad and Civil Engineering, Dongyang University, Yeongju 750-711, Republic of Korea
b School of Environmental and Civil Engineering, Inha University, Incheon 402-751, Republic of Korea
Received 21 June 2007; received in revised form 5 December 2007; accepted 6 December 2007
KEYWORDS Summary The purpose of this study is to develop and apply the generalized regression
GRNNM-GA; works model (GRNNM) embedding the ic algorithm (GA) in order to esti-
Pan evaporation; mate and calculate the pan evaporation (PE) and the alfalfa reference evapotranspiration
Alfalfa reference (ETr), Republic of Korea. Since the observed data of the alfalfa ETr using lysimeter have
evapotranspiration; not been measured for a long period, Penman–Monteith (PM) method was used to esti-
Penman–Monteith mate the observed alfalfa ETr. BINE-GRNNM-GA (Type-1) was developed to calcu-
method; late a reasonable PE and the alfalfa ETr. BINE-GRNNM-GA (Type-1) was evaluated
Uncertainty analysis; through the training, the testing, and the reproduction performances, respectively. An
Map construction uncertainty analysis was used to eliminate the climatic variables of the input layer nodes
and to construct the BINE-GRNNM-GA (Type-1). The climatic variable with the
lowest smoothing factor during the training performance was eliminated from the original
COMBINE-GRNNM-GA (Type-1). The climatic variable with the lowest smoothing factor
implies the most useless input layer node for the model output. Therefore, the optimal
COMBINE-GRNNM-GA (Type-1) can estimate and calculate the PE, which is missed or
ungaged, and the alfalfa ETr, which is not measured, with the least cost and endeavor.
Furthermore, it is possible to derive a linear regression statis
(2007) Neural networks and genetic algorithm approach for nonlinear evaporation and evapotranspiration modeling 来自淘豆网m.daumloan.com转载请标明出处.
