Consumption Change Detection for Urban Planning: Monitoring and Segmenting Water Customers During Drought-Reference-Cited by-同舟云学术

Consumption Change Detection for Urban Planning: Monitoring and Segmenting Water Customers During Drought

Published:2020-03 Issue:3 Volume:56 Page:
ISSN:0043-1397
Container-title:Water Resources Research
language:en
Short-container-title:Water Resour. Res.

Author:

Bolorinos Jose¹^ORCID,Ajami Newsha K.²³^ORCID,Rajagopal Ram¹⁴

Affiliation:

1. Department of Civil and Environmental Engineering Stanford University Stanford CA USA

2. Woods Institute for the Environment Stanford University Stanford CA USA

3. ReNUWIt Engineering Research Center Stanford University Stanford CA USA

4. Precourt Institute for Energy Stanford University Stanford CA USA

Publisher

American Geophysical Union (AGU)

Subject

Water Science and Technology

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1029/2019WR025812

Reference49 articles.

1. Nonprice incentives and energy conservation

2. The Residential Water Demand Effect of Increasing Block Rate Water Budgets

3. Beattie G.(2017).The social cost of ‘fake news’: How media coverage of climate change affects behavior: Working Paper.

4. Mining water billing data to inform policy and communication strategies

5. Boyle C. &Tiger M.(2012).Shifting baselines in water utility management: Using customer‐level analysis to understand the interplay between utility policy pricing and household demand(Tech. Rep. 409).Chappel Hill:Water Resources Research Institute of the University of North Carolina.

Cited by 34 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. Generating zonal hourly water consumption patterns in water distribution networks using end-use data;Urban Water Journal;2024-06-27

2. Detection and pre-localization of anomalous consumption events in water distribution networks through automated, pressure-based methodology;Water Resources and Industry;2024-06

3. Water consumption time series forecasting in urban centers using deep neural networks;Applied Water Science;2024-01-12

4. Segmentation-based VHR SAR images built-up area change detection: a coarse-to-fine approach;Journal of Applied Remote Sensing;2024-01-11

5. Predicting and understanding residential water use with interpretable machine learning;Environmental Research Letters;2024-01-01