Dynamic Forecast of Daily Urban Water Consumption Using a Variable-Structure Support Vector Regression Model-Reference-Cited by-同舟云学术

Dynamic Forecast of Daily Urban Water Consumption Using a Variable-Structure Support Vector Regression Model

Published:2015-03 Issue:3 Volume:141 Page:04014058
ISSN:0733-9496
Container-title:Journal of Water Resources Planning and Management
language:en
Short-container-title:J. Water Resour. Plann. Manage.

Author:

Bai Yun¹,Wang Pu²,Li Chuan³,Xie Jingjing⁴,Wang Yin⁵

Affiliation:

1. Ph.D. Candidate, School of Urban Construction and Environmental Engineering, Chongqing Univ., Chongqing 400044, China.

2. Professor, School of Urban Construction and Environmental Engineering, Chongqing Univ., Chongqing 400044, China.

3. Professor, Engineering Laboratory for Detection, Control and Integrated Systems, Chongqing Technology and Business Univ., Chongqing 400067, China (corresponding author).

4. Engineer, Testing Center for Science and Technology, Chongqing Academy of Science and Technology, Chongqing 401123, China.

5. Research Student, School of Urban Construction and Environmental Engineering, Chongqing Univ., Chongqing 400044, China.

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.1943-5452.0000457

Reference53 articles.

1. 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

2. Time series outlier and intervention analysis: Irrigation management influences on soil water content in silty loam soil

3. Hybrid Fuzzy Regression–Artificial Neural Network for Improvement of Short-Term Water Consumption Estimation and Forecasting in Uncertain and Complex Environments: Case of a Large Metropolitan City

4. Identifying Prominent Explanatory Variables for Water Demand Prediction Using Artificial Neural Networks: A Case Study of Bangkok

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