scholarly journals Identifying Typical Urban Water Demand Patterns for a Reliable Short-term Forecasting – The Icewater Project Approach

2014 ◽  
Vol 89 ◽  
pp. 1004-1012 ◽  
Author(s):  
A. Candelieri ◽  
F. Archetti
Keyword(s):  
Water Demand ◽  
Urban Water ◽  
Short Term ◽  
Project Approach ◽  
Urban Water Demand ◽  
Short Term Forecasting ◽  
Demand Patterns ◽  
Water Demand Patterns

scholarly journals Hourly and Daily Urban Water Demand Predictions Using a Long Short-Term Memory Based Model

2020 ◽  
Vol 146 (9) ◽  
pp. 05020017 ◽  
Author(s):  
Li Mu ◽  
Feifei Zheng ◽  
Ruoling Tao ◽  
Qingzhou Zhang ◽  
Zoran Kapelan
Keyword(s):  
Water Demand ◽  
Short Term Memory ◽  
Urban Water ◽  
Short Term ◽  
Term Memory ◽  
Urban Water Demand ◽  
Long Short Term Memory

scholarly journals Short-Term Urban Water Demand Prediction Considering Weather Factors

2018 ◽  
Vol 32 (14) ◽  
pp. 4527-4542 ◽  
Author(s):  
Salah L. Zubaidi ◽  
Sadik K. Gharghan ◽  
Jayne Dooley ◽  
Rafid M. Alkhaddar ◽  
Mawada Abdellatif
Keyword(s):  
Water Demand ◽  
Urban Water ◽  
Short Term ◽  
Weather Factors ◽  
Demand Prediction ◽  
Urban Water Demand ◽  
Water Demand Prediction

scholarly journals Short-term water demand predictions coupling an Artificial Neural Network model and a Genetic Algorithm

2021 ◽  
Author(s):  
Majid Gholami Shirkoohi ◽  
Mouna Doghri ◽  
Sophie Duchesne
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Water Demand ◽  
Urban Water ◽  
Ann Model ◽  
Short Term ◽  
Urban Water Demand ◽  
Artificial Neural ◽  
Mean Square Errors

Abstract The application of artificial neural network (ANN) models for short-term (15 min) urban water demand predictions is evaluated. Optimization of the ANN model's hyperparameters with a Genetic Algorithm (GA) and use of a growing window approach for training the model are also evaluated. The results are compared to those of commonly used time series models, namely the Autoregressive Integrated Moving Average (ARIMA) model and a pattern-based model. The evaluations are based on data sets from two Canadian cities, providing 15 minute water consumption records over respectively 5 years and 23 months, with a respective mean water demand of 14,560 and 887 m3/d. The GA optimized ANN model performed better than the other models, with Nash-Sutcliffe Efficiencies of 0.91 and 0.83, and Relative Root Mean Square Errors of 6 and 16% for City 1 and City 2, respectively. The results of this study indicate that the optimization of the hyperparameters of an ANN model can lead to better 15 min urban water demand predictions, which are useful for many real time control applications, such as dynamic pressure control.

scholarly journals Simulating a Stochastic Signal of Urban Water Demand by a Novel Combination of Data Analytic and Machine Learning Techniques

2021 ◽  
Vol 1058 (1) ◽  
pp. 012066
Author(s):  
Salah L. Zubaidi ◽  
Hussein Al-Bugharbee ◽  
Yousif Raad Muhsin ◽  
Sadik Kamel Gharghan ◽  
Khalid Hashim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Water Demand ◽  
Machine Learning Techniques ◽  
Urban Water ◽  
Stochastic Signal ◽  
Urban Water Demand ◽  
Learning Techniques ◽  
Data Analytic

A Fine Grained Forecasting Method for Minute-Level Urban Water Demand

2021 ◽  
Author(s):  
Shunyu Wu ◽  
Pingwei Zhao ◽  
Miaoshun Bai ◽  
Jingcheng Wang ◽  
Yang Lan
Keyword(s):  
Water Demand ◽  
Urban Water ◽  
Fine Grained ◽  
Urban Water Demand ◽  
Forecasting Method
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