Fractal - Wavelet Based Techniques For Improving The Artificial Neural Network Models release_kcxtr5a72rbevg46uq3mi7vtsa

by Reza Bazargan Lari, Mohammad H. Fattahi

Published by Zenodo.

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)
Read Archived PDF
Preserved and Accessible
Type  article-journal
Stage   published
Date   2014-08-01
Language   en ?
Work Entity
access all versions, variants, and formats of this works (eg, pre-prints)
Catalog Record
Revision: 0c27af18-d33a-4d8c-82ae-b2eba115782c
API URL: JSON