Short-Term Forecasting of Household Water Demand in the UK Using an Interpretable Machine Learning Approach-Reference-Cited by-同舟云学术

Short-Term Forecasting of Household Water Demand in the UK Using an Interpretable Machine Learning Approach

Published:2021-04 Issue:4 Volume:147 Page:04021004
ISSN:0733-9496
Container-title:Journal of Water Resources Planning and Management
language:en
Short-container-title:J. Water Resour. Plann. Manage.

Author:

Xenochristou Maria¹^ORCID,Hutton Chris²,Hofman Jan³^ORCID,Kapelan Zoran⁴

Affiliation:

1. Ph.D. Student, Centre for Water Systems, Univ. of Exeter, North Park Rd., Exeter EX4 4QF, UK; presently, Postdoctoral Research Fellow, Dept. of Anesthesiology, Perioperative and Pain Medicine, Dept. of Biomedical Data Science, Dept. of Pediatrics, Stanford Univ., Stanford, CA 94305 (corresponding author). ORCID: .

2. Water Resources Planning Manager, Wessex Water, Claverton Down Rd., Bath BA2 7WW, UK.

3. Professor, Water Innovation and Research Centre, Dept. of Chemical Engineering, Univ. of Bath, Claverton Down Rd., Bath BA2 7AY, UK; Principal Scientist, KWR Water Research Institute, P.O. Box 1072, Nieuwegein 3430BB, Netherlands. ORCID:

4. Professor, Dept. of Water Management, Delft Univ. of Technology, Stevinweg 1, Delft 2628CN, Netherlands; Professor, Centre for Water Systems, Univ. of Exeter, North Park Rd., Exeter EX4 4QF, UK.

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.1943-5452.0001325

Reference51 articles.

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5. Forecasting Domestic Violence: A Machine Learning Approach to Help Inform Arraignment Decisions

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