Hi everyone.
As I promised I am uploading the results of EWRA 2015 conference (i.e. Scatter plot of models) which I used to model the water elevation in Lake Urmia that have GEV distribution as I mentioned before. The full paper of the conference will be soon uploaded in Research gate and inrested people can refer to it. In this paper I used 11 month lagged data due to high persistence of the procedure (i.e. almost infinite). Then I used to model the water elevation by 9 different method categorized generally in parametric and non-parametric approaches. For the case of parametric approach I used multi linear regression (MLR), nonlinear regression (NLR) and decision tree (DT) while, ANN models namely radial basis function (RBF), feed forward back propagation (FFBP) and generalized regression neural network (GRN) with Gaussian transfer function were used in favor of non-parametric approach. Figure 1, shows the early results of the models indicating that parametric approaches are more favorable but FFBP still is a good competitor to the parametric results.
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| Fig 1. Scatter plot of all parametric and non-parametric models |
The most fitted model was NLR with multiplicative nature and this means that the procedure is extremely nonlinear. This results are in favor of some related research like Khatami Mashhadi (2013) whom tracked Chaos in the procedure.
GRN, DT and RBF shares the poorest results and generally speaking could not tracked down the real trend of the procedure.
Sincerely
Babak
Refferences:
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