Short-Term Water Demand Forecasting Using Nonlinear Autoregressive Artificial Neural Networks-Reference-Cited by-同舟云学术

Short-Term Water Demand Forecasting Using Nonlinear Autoregressive Artificial Neural Networks

Published:2020-03 Issue:3 Volume:146 Page:04020008
ISSN:0733-9496
Container-title:Journal of Water Resources Planning and Management
language:en
Short-container-title:J. Water Resour. Plann. Manage.

Author:

Bata Mo’tamad H.¹,Carriveau Rupp²^ORCID,Ting David S.-K.³^ORCID

Affiliation:

1. Master of Applied Science Student, Dept. of Civil and Environmental Engineering, Ed Lumley Centre for Engineering Innovation, Univ. of Windsor, Windsor, ON, Canada N9B 3P4.

2. Professor, Dept. of Civil and Environmental Engineering, Ed Lumley Centre for Engineering Innovation, Univ. of Windsor, Windsor, ON, Canada N9B 3P4 (corresponding author). ORCID: .

3. Professor, Mechanical, Dept. of Automotive and Materials Engineering, Ed Lumley Centre for Engineering Innovation, Univ. of Windsor, Windsor, ON, Canada N9B 3P4. ORCID: .

Publisher

American Society of Civil Engineers (ASCE)

Subject

Management, Monitoring, Policy and Law,Water Science and Technology,Geography, Planning and Development,Civil and Structural Engineering

Link

http://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29WR.1943-5452.0001165

Reference35 articles.

1. Comparison of Multivariate Regression and Artificial Neural Networks for Peak Urban Water-Demand Forecasting: Evaluation of Different ANN Learning Algorithms

2. A short-term, pattern-based model for water-demand forecasting

3. Short-Term Forecasting for Urban Water Consumption

4. A New Neural Network Approach to Short Term Load Forecasting of Electrical Power Systems

5. Tailoring Seasonal Time Series Models to Forecast Short-Term Water Demand

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