Predicting Residential Water Demand with Machine-Based Statistical Learning-Reference-Cited by-同舟云学术

Predicting Residential Water Demand with Machine-Based Statistical Learning

Published:2020-01 Issue:1 Volume:146 Page:04019067
ISSN:0733-9496
Container-title:Journal of Water Resources Planning and Management
language:en
Short-container-title:J. Water Resour. Plann. Manage.

Author:

Lee Dongwoo¹,Derrible Sybil²

Affiliation:

1. Postdoctoral Researcher, Complex and Sustainable Urban Networks Laboratory and Civil and Materials Engineering, Univ. of Illinois at Chicago, Chicago, IL 60607 (corresponding author).

2. Professor and Director, Complex and Sustainable Urban Networks Laboratory, Civil and Materials Engineering, and Institute for Environmental Science and Policy, Univ. of Illinois at Chicago, Chicago, IL 60607.

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.1943-5452.0001119

Reference76 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. Evolution of Public Supply Water Withdrawal in the USA: A Network Approach

3. Using Fisher information to assess stability in the performance of public transportation systems

4. Using Fisher information to track stability in multivariate systems

5. The role of travel demand and network centrality on the connectivity and resilience of an urban street system

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