Battle of Water Demand Forecasting: An Optimized Deep Learning Model
Author:
Affiliation:
1. Department of Civil and Structural Engineering, University of Sheffield, Sheffield S1 3JD, UK
2. Department of Engineering and Mathematics, Sheffield Hallam University, Sheffield S1 1WB, UK
Publisher
MDPI
Link
https://www.mdpi.com/2673-4591/69/1/56/pdf
Reference9 articles.
1. A Comparison of Short-Term Water Demand Forecasting Models;Pacchin;Water Resour. Manag.,2019
2. Short-Term Water Demand Forecast Based on Deep Learning Method;Guo;J. Water Resour. Plan. Manag.,2018
3. Recurrent Neural Networks for Time Series Forecasting: Current status and future directions;Hewamalage;Int. J. Forecast.,2021
4. Staudemeyer, R.C., and Morris, E.R. (2019). Understanding LSTM—A tutorial into Long Short-Term Memory Recurrent Neural Networks. arXiv.
5. Wang, K., Ye, Z., Wang, Z., Liu, B., and Feng, T. (2023). MACLA-LSTM: A Novel Approach for Forecasting Water Demand. Sustainability, 15.
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