Urban Water Demand: Statistical Optimization Approach to Modeling Daily Demand-Reference-Cited by-同舟云学术

Urban Water Demand: Statistical Optimization Approach to Modeling Daily Demand

Published:2021-02 Issue:2 Volume:147 Page:04020105
ISSN:0733-9496
Container-title:Journal of Water Resources Planning and Management
language:en
Short-container-title:J. Water Resour. Plann. Manage.

Author:

Capt Tallen¹,Mirchi Ali²^ORCID,Kumar Saurav³,Walker W. Shane⁴^ORCID

Affiliation:

1. Dept. of Civil Engineering, Univ. of Texas at El Paso, 500 W University Ave., El Paso, TX 79968.

2. Assistant Professor, Dept. of Biosystems and Agricultural Engineering, Oklahoma State Univ., 111 Agriculture Hall, Stillwater, OK 74078. ORCID: .

3. Assistant Professor, Dept. of Biological and Agricultural Engineering, Texas A&M Univ., AgriLife Research, 1380 A&M Circle, El Paso, TX 79927.

4. Director of the Center for Inland Desalination Systems and Associate Professor, Dept. of Civil Engineering, Univ. of Texas at El Paso, 500 W University Ave., El Paso, TX 79968 (corresponding author). 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.1943-5452.0001315

Reference43 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. Development and testing of the WaterGAP 2 global model of water use and availability

3. Urban Residential Water Demand Prediction Based on Artificial Neural Networks and Time Series Models

4. Improving the Performance of Water Demand Forecasting Models by Using Weather Input

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