Comparison of wavelet based hybrid models for daily evapotranspiration estimation using meteorological data

This paper investigates the comparative performance of wavelet based radial basis networks and multi linear regression in daily reference evapotranspiration estimation. The meteorological data (air temperature, solar radiation, wind speed, relative humidity) from two stations in the United States was evaluated for estimating models. The wavelet based radial basis network combines wavelet transformation and radial basis neural network, while the wavelet based regression model combines wavelet transformation and multi linear regression. The results show that the wavelet transformation has significantly positive effects on modeling performance. The wavelet based radial basis network provided the best performance evaluation criteria.

[1]  Gorka Landeras,et al.  Forecasting weekly evapotranspiration with ARIMA and artificial neural network models. , 2009 .

[2]  J. Adamowski,et al.  A wavelet neural network conjunction model for groundwater level forecasting , 2011 .

[3]  Stéphane Mallat,et al.  A Theory for Multiresolution Signal Decomposition: The Wavelet Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Narendra Singh Raghuwanshi,et al.  Estimating Evapotranspiration using Artificial Neural Network , 2002 .

[5]  O. Kisi.  Wavelet regression model for short-term streamflow forecasting. , 2010 .

[6]  D. Broomhead,et al.  Radial Basis Functions, Multi-Variable Functional Interpolation and Adaptive Networks , 1988 .

[7]  Hafzullah Aksoy,et al.  An explicit neural network formulation for evapotranspiration , 2008 .

[8]  Juan B. Valdés,et al.  NONLINEAR MODEL FOR DROUGHT FORECASTING BASED ON A CONJUNCTION OF WAVELET TRANSFORMS AND NEURAL NETWORKS , 2003 .

[9]  K. P. Sudheer,et al.  Models for estimating evapotranspiration using artificial neural networks, and their physical interpretation , 2008 .

[10]  S. Jain,et al.  Radial Basis Function Neural Network for Modeling Rating Curves , 2003 .

[11]  Slavisa Trajkovic,et al.  Estimating Reference Evapotranspiration Using Limited Weather Data , 2009 .

[12]  George H. Hargreaves,et al.  Reference Crop Evapotranspiration from Temperature , 1985 .

[13]  Hikmet Kerem Cigizoglu,et al.  Estimation, forecasting and extrapolation of river flows by artificial neural networks , 2003 .

[14]  Turgay Partal,et al.  Estimation and forecasting of daily suspended sediment data using wavelet–neural networks , 2008 .

[15]  M. Dettinger,et al.  Sources of Variability of Evapotranspiration in California , 2005 .

[16]  Turgay Partal,et al.  Modelling evapotranspiration using discrete wavelet transform and neural networks , 2009 .

[17]  Branimir Todorovic,et al.  Forecasting of Reference Evapotranspiration by Artificial Neural Networks , 2003 .

[18]  H. K. Cigizoglu,et al.  ESTIMATION AND FORECASTING OF DAILY SUSPENDED SEDIMENT DATA BY MULTI-LAYER PERCEPTRONS , 2004 .

[19]  A. W. Jayawardena,et al.  Use of Radial Basis Function Type Artificial Neural Networks for Runoff Simulation , 1998 .

[20]  Ingrid Daubechies,et al.  The wavelet transform, time-frequency localization and signal analysis , 1990, IEEE Trans. Inf. Theory.

[21]  Yashar Falamarzi,et al.  Estimating evapotranspiration from temperature and wind speed data using artificial and wavelet neural networks (WNNs) , 2014 .

[22]  K. P. Sudheer,et al.  Modelling evaporation using an artificial neural network algorithm , 2002 .

[23]  Ozgur Kisi,et al.  Evapotranspiration modelling from climatic data using a neural computing technique , 2007 .

[24]  Yves Meyer.  Wavelets - algorithms & applications , 1993 .