Urban Water Demand Modeling Using Machine Learning Techniques: Case Study of Fortaleza, Brazil-Reference-Cited by-同舟云学术

Urban Water Demand Modeling Using Machine Learning Techniques: Case Study of Fortaleza, Brazil

Published:2021-01 Issue:1 Volume:147 Page:05020026
ISSN:0733-9496
Container-title:Journal of Water Resources Planning and Management
language:en
Short-container-title:J. Water Resour. Plann. Manage.

Author:

Nunes Carvalho Taís Maria¹^ORCID,de Souza Filho Francisco de Assis²,Porto Victor Costa³

Affiliation:

1. Ph.D. Student, Dept. of Hydraulic and Environmental Engineering, Universidade Federal do Ceará, Campus do Pici, Bloco 713, CEP 60455-760, Fortaleza, Brazil (corresponding author). ORCID: .

2. Professor, Dept. of Hydraulic and Environmental Engineering, Universidade Federal do Ceará, Campus do Pici, Bloco 713, CEP 60455-760, Fortaleza, Brazil.

3. Ph.D. Student, Dept. of Hydraulic and Environmental Engineering, Universidade Federal do Ceará, Campus do Pici, Bloco 713, CEP 60455-760, Fortaleza, Brazil.

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.1943-5452.0001310

Reference85 articles.

1. Comparison of Multivariate Regression and Artificial Neural Networks for Peak Urban Water-Demand Forecasting: Evaluation of Different ANN Learning Algorithms

2. Monthly Water Consumption Prediction Using Season Algorithm and Wavelet Transform–Based Models

3. Apley D. W. and J. Zhu. 2016. “Visualizing the effects of predictor variables in black box supervised learning models.” Preprint submitted December 27 2016. https://arxiv.org/abs/1612.08468.

4. Short-Term Water Demand Forecasting Using Nonlinear Autoregressive Artificial Neural Networks

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