A Stochastic Data‐Driven Ensemble Forecasting Framework for Water Resources: A Case Study Using Ensemble Members Derived From a Database of Deterministic Wavelet‐Based Models-Reference-Cited by-同舟云学术

A Stochastic Data‐Driven Ensemble Forecasting Framework for Water Resources: A Case Study Using Ensemble Members Derived From a Database of Deterministic Wavelet‐Based Models

Published:2019-01 Issue:1 Volume:55 Page:175-202
ISSN:0043-1397
Container-title:Water Resources Research
language:en
Short-container-title:Water Resour. Res.

Author:

Quilty John¹^ORCID,Adamowski Jan¹^ORCID,Boucher Marie‐Amélie²^ORCID

Affiliation:

1. Department of Bioresource EngineeringMcGill University Montreal Quebec Canada

2. Department of Civil and Building EngineeringUniversité de Sherbrooke Sherbrooke Quebec Canada

Funder

Natural Sciences and Engineering Research Council of Canada

Publisher

American Geophysical Union (AGU)

Subject

Water Science and Technology

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1029/2018WR023205

Reference105 articles.

1. A Spectral Analysis Based Methodology to Detect Climatological Influences on Daily Urban Water Demand

2. 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

3. A Model Ranking Based Selective Ensemble Approach for Time Series Forecasting

4. Toward multi-day-ahead forecasting of suspended sediment concentration using ensemble models

5. Estimation of residential water demand: a state-of-the-art review

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