Asian Science Citation Index is committed to provide an authoritative, trusted and significant information by the
coverage of the most important and influential journals
to meet the needs of the global scientific community.
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.
