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| Cluster Analysis of Rainfall-Runoff Training Patterns to Flow Modeling Using Hybrid RBF Networks |
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H. Abghari,
M. Mahdavi,
A. Fakherifard
and A. Salajegheh
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Abstract: The artificial intelligence modeling of nonstationary
rainfall-runoff has some restriction in accuracy of simulation base on
complexity and nonlinearity of training patterns. Statistical preprocessing
of trainings could determine homogeneity of rainfall-runoff patterns before
modeling in artificial intelligence. In this study, the new hybrid model
of artificial intelligence in conjunction with statistical clustering
is introduced. Statistical pre-processing effects of 360 rainfall-runoff
patterns considered before modeling using Radial Basis Function Neural
Networks (RBFNNs). In the first step all 360 monthly rainfall-runoff patterns
classify by cluster analysis in 4 groups and each class modeled by different
RBFNNs topology. Results of 4 cluster base-RBFNNs compare with no action
one and the optimized structure of Hybrid Cluster base-RBFNN models of
Nazloochaei river flow present. Results show that clustering of rainfall-runoff
patterns and modeling of each dataset by different RBFNNs has higher accuracy
than no preprocessing of patterns in prediction and modeling of river
flow. |
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23 November, 2020
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