Probabilistic Multi-Step-Ahead Short-Term Water Demand Forecasting with Lasso-Reference-Cited by-同舟云学术

Probabilistic Multi-Step-Ahead Short-Term Water Demand Forecasting with Lasso

Published:2020-10 Issue:10 Volume:146 Page:04020077
ISSN:0733-9496
Container-title:Journal of Water Resources Planning and Management
language:en
Short-container-title:J. Water Resour. Plann. Manage.

Author:

Kley-Holsteg Jens¹^ORCID,Ziel Florian²

Affiliation:

1. Ph.D. Student, Faculty of Economics, esp. Economics of Renewable Energies, Univ. Duisburg-Essen, Universitätsstr. 2, Essen 45141, Germany (corresponding author). ORCID: .

2. Professor, Faculty of Economics, esp. Economics of Renewable Energies, Univ. Duisburg-Essen, Universitätsstr. 2, Essen 45141, Germany. Email: .

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.1943-5452.0001268

Reference57 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. Comparison of Multivariate Regression and Artificial Neural Networks for Peak Urban Water-Demand Forecasting: Evaluation of Different ANN Learning Algorithms

3. Assessment of predictive uncertainty within the framework of water demand forecasting using the Model Conditional Processor (MCP)

4. A short-term, pattern-based model for water-demand forecasting

5. Committee Machines for Hourly Water Demand Forecasting in Water Supply Systems

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