Comparison of multiple linear and nonlinear regression, autoregressive integrated moving average, artificial neural network, and wavelet artificial neural network methods for urban water demand forecasting in Montreal, Canada-Reference-Cited by-同舟云学术

Comparison of multiple linear and nonlinear regression, autoregressive integrated moving average, artificial neural network, and wavelet artificial neural network methods for urban water demand forecasting in Montreal, Canada

Published:2012-01 Issue:1 Volume:48 Page:
ISSN:0043-1397
Container-title:Water Resources Research
language:en
Short-container-title:Water Resour. Res.

Author:

Adamowski Jan,Fung Chan Hiu,Prasher Shiv O.,Ozga-Zielinski Bogdan,Sliusarieva Anna

Publisher

American Geophysical Union (AGU)

Subject

Water Science and Technology

Link

http://onlinelibrary.wiley.com/wol1/doi/10.1029/2010WR009945/fullpdf

Reference71 articles.

1. Development of a short-term river flood forecasting method based on wavelet analysis;Adamowski;J. Hydrol.,2007

2. Development of a short-term river flood forecasting method for snowmelt driven floods based on wavelet and cross-wavelet analysis;Adamowski;J. Hydrol.,2008

3. Peak daily water demand forecast modeling using artificial neural networks;Adamowski;J. Water Resour. Plan. Manage.,2008

4. River flow forecasting using wavelet and cross-wavelet transform models;Adamowski;J. Hydrol. Processes,2008

5. A wavelet neural network conjunction model for groundwater level forecasting;Adamowski;J. Hydrol.,2011

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