Modeling Automatic Meter Reading Water Demands as Nonhomogeneous Point Processes-Reference-Cited by-同舟云学术

Modeling Automatic Meter Reading Water Demands as Nonhomogeneous Point Processes

Published:2014-01 Issue:1 Volume:140 Page:55-64
ISSN:0733-9496
Container-title:Journal of Water Resources Planning and Management
language:en
Short-container-title:J. Water Resour. Plann. Manage.

Author:

Arandia-Perez Ernesto¹,Uber James G.²,Boccelli Dominic L.³,Janke Robert⁴,Hartman David⁵,Lee Yeongho⁶

Affiliation:

1. Research Assistant, Environmental Engineering Program, SEEBME, Univ. of Cincinnati, Cincinnati, OH 45221 (corresponding author).

2. Professor, Environmental Engineering Program, SEEBME, Univ. of Cincinnati, Cincinnati, OH 45221.

3. Assistant Professor, Environmental Engineering Program, SEEBME, Univ. of Cincinnati, Cincinnati, OH 45221.

4. Research Scientist, U.S. Environmental Protection Agency, 26 Martin Luther King Dr., Cincinnati, OH 45268.

5. Greater Cincinnati Water Works, Cincinnati, OH 45228.

6. Supervising Engineer, Greater Cincinnati Water Works, Cincinnati, OH 45228.

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.1943-5452.0000318

Reference22 articles.

1. TIME SERIES ANALYSIS AND CONTROL THROUGH PARAMETRIC MODELS

2. Bettonville A. Cebula C. Finnie B. Hughes M. and Toshach A. (2009). “Badger Meter Inc.: CFA Institute Investment Research Challenge.” Rep. No. 1 Marquette Univ. Milwaukee WI.

3. Simulating Residential Water Demand with a Stochastic End-Use Model

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