Sequence-to-Sequence Deep Learning for Urban Water Demand Forecasting
Author:
Affiliation:
1. Department of Civil and Environmental Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada
Publisher
MDPI
Link
https://www.mdpi.com/2673-4591/69/1/41/pdf
Reference10 articles.
1. Hybrid Regression Model for Near Real-Time Urban Water Demand Forecasting;Brentan;J. Comput. Appl. Math.,2017
2. Deep Learning with Long Short-Term Memory Neural Networks Combining Wavelet Transform and Principal Component Analysis for Daily Urban Water Demand Forecasting;Du;Expert Syst. Appl.,2021
3. Long Short-Term Memory;Hochreiter;Neural Comput.,1997
4. Effective Improvement of Multi-Step-Ahead Flood Forecasting Accuracy through Encoder-Decoder with an Exogenous Input Structure;Cui;J. Hydrol.,2022
5. A Quantile-Based Encoder-Decoder Framework for Multi-Step Ahead Runoff Forecasting;Jahangir;J. Hydrol.,2023
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