Fractal - Wavelet Based Techniques For Improving The Artificial Neural Network Models
release_kcxtr5a72rbevg46uq3mi7vtsa
by
Reza Bazargan Lari, Mohammad H. Fattahi
2014
Abstract
Natural resources management including water resources requires reliable estimations of time variant environmental parameters. Small improvements in the estimation of environmental parameters would result in grate effects on managing decisions. Noise reduction using wavelet techniques is an effective approach for preprocessing of practical data sets. Predictability enhancement of the river flow time series are assessed using fractal approaches before and after applying wavelet based preprocessing. Time series correlation and persistency, the minimum sufficient length for training the predicting model and the maximum valid length of predictions were also investigated through a fractal assessment.
In text/plain
format
Archived Files and Locations
application/pdf 240.0 kB
file_wrsrpke3prb5rfgugsq2fmxkzy
|
zenodo.org (repository) web.archive.org (webarchive) |
access all versions, variants, and formats of this works (eg, pre-prints)
Datacite Metadata (via API)
Worldcat
wikidata.org
CORE.ac.uk
Semantic Scholar
Google Scholar