Comparison of Input Data Compression Methods in Neural Network Solution of Inverse Problem in Laser Raman Spectroscopy of Natural Waters

In their previous papers, the authors of this study have suggested and realized a method of simultaneous determination of temperature and salinity of seawater using laser Raman spectroscopy, with the help of neural networks. Later, the method has been improved for determination of temperature and salinity of natural water using Raman spectra, in presence of fluorescence of dissolved organic matter as dispersant pedestal under Raman valence band. In this study, the method has been further improved by compression of input data. This paper presents comparison of various input data compression methods using feature selection and feature extraction and their effect on the error of determination of temperature and salinity.

[1]  J. Scherer,et al.  Raman spectra and structure of water from -10 to 90.deg. , 1974 .

[2]  Jacqueline Boutin,et al.  Surface Salinity Retrieved from SMOS Measurements over the Global Ocean: Imprecisions Due to Sea Surface Roughness and Temperature Uncertainties , 2004 .

[3]  Tatiana A. Dolenko,et al.  Valence band of liquid water Raman scattering: some peculiarities and applications in the diagnostics of water media , 2000 .

[4]  Donald F. Specht,et al.  A general regression neural network , 1991, IEEE Trans. Neural Networks.

[5]  S. Burikov,et al.  Remote determination of temperature and salinity in consideration of dissolved organic matter in natural waters using laser spectroscopy. , 2011 .

[6]  G. Walrafen,et al.  Raman Spectral Studies of the Effects of Temperature on Water Structure , 1967 .

[7]  M. Materazzi,et al.  Accuracy of remote sensing of water temperature by Raman spectroscopy. , 1999, Applied optics.

[8]  G. Walrafen,et al.  Raman Spectral Studies of the Effects of Temperature on Water and Electrolyte Solutions , 1966 .

[9]  D. Combes,et al.  Effect of salts on dynamics of water: A Raman spectroscopy study , 1990 .

[10]  D. A. Leonard,et al.  Remote sensing of subsurface water temperature by Raman scattering. , 1979, Applied optics.

[11]  Antonio Turiel,et al.  The multifractal structure of satellite sea surface temperature maps can be used to obtain global maps of streamlines , 2009 .

[12]  K. Furic,et al.  Raman spectroscopic study of sodium chloride water solutions , 2000 .

[13]  Javier Marcello,et al.  Methodology to obtain accurate sea surface temperature from locally received NOAA-14 data in the Canary-Azores-Gibraltar area , 2001 .

[14]  G. Walrafen,et al.  Raman Spectral Studies of Water Structure , 1964 .

[15]  I. G. Persiantsev,et al.  New opportunities in solution of inverse problems in laser spectroscopy due to application of artificial neural networks , 2002, International Conference on Coherent and Nonlinear Optics.

[16]  Chin H. Chang,et al.  Seawater Temperature Measurement from Raman Spectra , 1972 .

[17]  S. Burikov,et al.  NEW APPROACHES TO DETERMINATION OF TEMPERATURE AND SALINITY OF SEAWATER BY LASER RAMAN SPECTROSCOPY , 2004 .

[18]  Yann Kerr,et al.  SMOS: The Challenging Sea Surface Salinity Measurement From Space , 2010, Proceedings of the IEEE.