Abstract: Presently various computational techniques are used
in modeling and analyzing environmental engineering data. In the
present study, an intra-comparison of polynomial and radial basis
kernel functions based on Support Vector Regression and, in turn, an
inter-comparison with Multi Linear Regression has been attempted in
modeling mass transfer capacity of vertical (θ = 90O) and inclined (θ
multiple plunging jets (varying from 1 to 16 numbers). The data set
used in this study consists of four input parameters with a total of
eighty eight cases, forty four each for vertical and inclined multiple
plunging jets. For testing, tenfold cross validation was used.
Correlation coefficient values of 0.971 and 0.981 along with
corresponding root mean square error values of 0.0025 and 0.0020
were achieved by using polynomial and radial basis kernel functions
based Support Vector Regression respectively. An intra-comparison
suggests improved performance by radial basis function in
comparison to polynomial kernel based Support Vector Regression.
Further, an inter-comparison with Multi Linear Regression
(correlation coefficient = 0.973 and root mean square error = 0.0024)
reveals that radial basis kernel functions based Support Vector
Regression performs better in modeling and estimating mass transfer
by multiple plunging jets.
Abstract: An approach was evaluated for the retrieval of soil
moisture of bare soil surface using bistatic scatterometer data in the
angular range of 200 to 700 at VV- and HH- polarization. The
microwave data was acquired by specially designed X-band (10
GHz) bistatic scatterometer. The linear regression analysis was done
between scattering coefficients and soil moisture content to select the
suitable incidence angle for retrieval of soil moisture content. The 250
incidence angle was found more suitable. The support vector
regression analysis was used to approximate the function described
by the input output relationship between the scattering coefficient and
corresponding measured values of the soil moisture content. The
performance of support vector regression algorithm was evaluated by
comparing the observed and the estimated soil moisture content by
statistical performance indices %Bias, root mean squared error
(RMSE) and Nash-Sutcliffe Efficiency (NSE). The values of %Bias,
root mean squared error (RMSE) and Nash-Sutcliffe Efficiency
(NSE) were found 2.9451, 1.0986 and 0.9214 respectively at HHpolarization.
At VV- polarization, the values of %Bias, root mean
squared error (RMSE) and Nash-Sutcliffe Efficiency (NSE) were
found 3.6186, 0.9373 and 0.9428 respectively.
Abstract: Among neural models the Support Vector Machine
(SVM) solutions are attracting increasing attention, mostly because
they eliminate certain crucial questions involved by neural network
construction. The main drawback of standard SVM is its high
computational complexity, therefore recently a new technique, the
Least Squares SVM (LS–SVM) has been introduced. In this paper we
present an extended view of the Least Squares Support Vector
Regression (LS–SVR), which enables us to develop new
formulations and algorithms to this regression technique. Based on
manipulating the linear equation set -which embodies all information
about the regression in the learning process- some new methods are
introduced to simplify the formulations, speed up the calculations
and/or provide better results.
Abstract: The drainage Estimating is an important factor in
dam management. In this paper, we use fuzzy support vector
regression (FSVR) to predict the drainage of the Sirikrit Dam at
Uttaradit province, Thailand. The results show that the FSVR is a
suitable method in drainage estimating.