Geology Reference
In-Depth Information
rming the success of GT in identifying the input combination [W, T,
Ed] and training data length 2,327, the chapter has performed modeling using
several state-of-the-art data-based models such as LLR, ANN, ANFIS, and SVMs.
This case study also made a novel attempt to combine DWT with the above-
mentioned major arti
After con
cial intelligence techniques in context of evaporation mod-
eling. The hybrid models such as NW model, W-ANFIS, and W-SVMs, and their
applications, are new in the
field of evaporation modeling. The ANN modeling
using different algorithms suggested that the Levenberg
Marquardt algorithm is
better for modeling compared to Conjugate Gradient and the Broyden
-
Fletcher
-
-
-
Goldfarb
Shanno (BFGS) algorithm. The SVM modeling was performed for dif-
ferent classi
-
-SVR with
linear kernel function was observed as the best model for evaporation modelling. It
was found that the models such as ANN-LM, NNARX, ANFIS, and LLR had better
performances than the SVM model in terms of statistical parameters. The incor-
poration of DWT with models such as ANFIS, ANN, and SVM improved the
prediction performance considerably. The W-SVM model took a considerably
longer time for modeling compared to other wavelet-based hybrid structure. It was
interesting to note that the good performance of the LLR model was consistent and
comparable to that of the relatively complex NW model.
ers and regressors with different kernel functions. The
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