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Modeling of daily pan evaporation using partial least squares regression

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Abstract

This study presented the application of partial least squares regression (PLSR) in estimating daily pan evaporation by utilizing the unique feature of PLSR in eliminating collinearity issues in predictor variables. The climate variables and daily pan evaporation data measured at two weather stations located near Elephant Butte Reservoir, New Mexico, USA and a weather station located in Shanshan County, Xinjiang, China were used in the study. The nonlinear relationship between climate variables and daily pan evaporation was successfully modeled using PLSR approach by solving collinearity that exists in the climate variables. The modeling results were compared to artificial neural networks (ANN) models with the same input variables. The results showed that the nonlinear equations developed using PLSR has similar performance with complex ANN approach for the study sites. The modeling process was straightforward and the equations were simpler and more explicit than the ANN black-box models.

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Authors and Affiliations

  1. Xinjiang Water Resources Research Institute, Urumqi, 830049, China

    Shalamu Abudu & ChunLiang Cui

  2. Civil Engineering Department, New Mexico State University, Las Cruces, New Mexico, 88001, USA

    Shalamu Abudu, J. Phillip King, Jimmy Moreno & A. Salim Bawazir

Authors
  1. Shalamu Abudu
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  2. ChunLiang Cui
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  3. J. Phillip King
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  4. Jimmy Moreno
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  5. A. Salim Bawazir
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Correspondence to Shalamu Abudu.

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Abudu, S., Cui, C., King, J.P. et al. Modeling of daily pan evaporation using partial least squares regression. Sci. China Technol. Sci. 54, 163–174 (2011). https://doi.org/10.1007/s11431-010-4205-z

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  • DOI: https://doi.org/10.1007/s11431-010-4205-z

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