A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2018; you can also visit the original URL.
The file type is application/pdf.
Spatial prediction of monthly wind speeds in complex terrain with adaptive general regression neural networks
2013
International Journal of Climatology
unpublished
Preserved Fulltext
The research paper deals with a step-by-step methodology for the automatic modeling of geospatial environmental data. The methodology proposed is based on general regression neural networks (GRNN) and probabilistic neural networks (PNN) as modeling tools. GRNN and PNN are nonparametric nonlinear models suitable for the automatic analysis, modeling, and spatial predictions of complex environmental data. The simulated and real data case studies illustrating the methodology are considered and discussed.
fatcat:cj2tnzyrjnep3cxjpkju7bgoae