Improving Short-Term Urban Water Demand Forecasts with Reforecast Analog Ensembles-Reference-Cited by-同舟云学术

Improving Short-Term Urban Water Demand Forecasts with Reforecast Analog Ensembles

Published:2016-06 Issue:6 Volume:142 Page:04016008
ISSN:0733-9496
Container-title:Journal of Water Resources Planning and Management
language:en
Short-container-title:J. Water Resour. Plann. Manage.

Author:

Tian Di¹,Martinez Christopher J.²,Asefa Tirusew³

Affiliation:

1. Postdoctoral Research Associate, Dept. of Civil and Environmental Engineering, Princeton Univ., 59 Olden St., Princeton, NJ 08544 (corresponding author).

2. Associate Professor, Dept. of Agricultural and Biological Engineering, Univ. of Florida, 279 Frazier Rogers Hall, Gainesville, FL 32611.

3. Manager, Modeling and Decision Support, Tampa Bay Water, 2575 Enterprise Rd., Clearwater, FL 33763.

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.1943-5452.0000632

Reference30 articles.

1. A comparison of statistical downscaling methods suited for wildfire applications

2. A wavelet neural network conjunction model for groundwater level forecasting

3. 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

4. Assessment of the Predictive Uncertainty within the Framework of Water Demand Forecasting by Using the Model Conditional Processor

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

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