Hybrid Water Demand Forecasting Model Associating Artificial Neural Network with Fourier Series-Reference-Cited by-同舟云学术

Hybrid Water Demand Forecasting Model Associating Artificial Neural Network with Fourier Series

Published:2012-05 Issue:3 Volume:138 Page:245-256
ISSN:0733-9496
Container-title:Journal of Water Resources Planning and Management
language:en
Short-container-title:J. Water Resour. Plann. Manage.

Author:

Odan Frederico Keizo¹,Reis Luisa Fernanda Ribeiro²

Affiliation:

1. Doctoral student, Dept. of Hydraulics and Sanitary Engineering, Univ. of São Paulo, Av. Trabalhador Sãocarlense, 400. CEP 13566-590, São Carlos, São Paulo, Brazil (corresponding author).

2. Professor, Dept. of Hydraulics and Sanitary Engineering, Univ. of São Paulo, Av. Trabalhador Sãocarlense, 400. CEP 13566-590, São Carlos, São Paulo, Brazil.

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.1943-5452.0000177

Reference31 articles.

1. Crommelynck V. Duquesne C. Mercier M. and Miniussi C. (1992). “Daily and hourly water consumption forecasting tools using neural networks.” Proc. of the AWWA’s Annual Computer Specialty Conference Nashville TN 665–676.

Cited by 52 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Water Demand Forecast Using Generalized Autoregressive Moving Average Models;The 3rd International Joint Conference on Water Distribution Systems Analysis & Computing and Control for the Water Industry (WDSA/CCWI 2024);2024-09-12

2. Short-term water demand forecasting: a review;Australasian Journal of Water Resources;2024-05-14

3. Determinants for Supplier Selection Based on Hybrid Grey Theory: Case Study of the Vietnamese Coffee Industry;Processes;2024-04-28

4. Contrasting the Water Consumption Estimation Methods: Case of USA and South Africa;Earth and Environmental Sciences Library;2024

5. Comparison of deep learning approaches for forecasting urban short-term water demand a Greater Sydney Region case study;Knowledge-Based Systems;2023-09