Comparison of Multi Linear Regression, Nonparametric Regression, and Times Series Models for Estimation and Prediction of Evaporation Values

Document Type : Technical Note (5 pages)

Authors

1 Young Researchers and Elites Club, Science and Research Branch, Islamic Azad University, Tehran, Iran

2 Assistant Professor, Department of Water Engineering, Imam Khomeini International University, Qazvin, Iran.

3 Assistant Professor (Respectively), Department of Irrigation and Reclamation, Faculty of Agricultural Engineering and Technology, University of Tehran, Karaj, Iran

4 Professor (Respectively), Department of Irrigation and Reclamation, Faculty of Agricultural Engineering and Technology, University of Tehran, Karaj, Iran.

Abstract

In order to simulate time series, various methods are presented such as times series models (AR, ARMA and ARMAX), multi-linear regression (MLR), and nonparametric regression (K-NN). In this research, performance of these models for estimation of missing values and prediction of future values of evaporation series (from open water) were assessed. ARMAX model with standardized input time series of Tmin, Tmax, Tav, Wind, RH, and sunshine hours, outperformed the other models and the K-NN and MLR were in the next ranks, respectively. Also after the principal component analysis, ARMAX model showed noticeable deviation for estimating missing values and MLR and K-NN in calibration and MLR in validation stage performed the best. For short-term predictions, ARMAX model has the best performance, but MLR performed better in long-term predictions, Time series models were not robust for long term predictions.

Keywords

References

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History

  • Receive Date: 29 February 2012
  • Accept Date: 09 October 2012

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