scholarly journals Improving the Performance of Water Demand Forecasting Models by Using Weather Input

2014 ◽  
Vol 70 ◽  
pp. 93-102 ◽  
Author(s):  
M. Bakker ◽  
H. van Duist ◽  
K. van Schagen ◽  
J. Vreeburg ◽  
L. Rietveld
Keyword(s):  
Water Demand ◽  
Demand Forecasting ◽  
Forecasting Models ◽  
Water Demand Forecasting

A Comparison of Short-Term Water Demand Forecasting Models

2019 ◽  
Vol 33 (4) ◽  
pp. 1481-1497 ◽  
Author(s):  
E. Pacchin ◽  
F. Gagliardi ◽  
S. Alvisi ◽  
M. Franchini
Keyword(s):  
Water Demand ◽  
Demand Forecasting ◽  
Short Term ◽  
Forecasting Models ◽  
Water Demand Forecasting

scholarly journals An Innovative Hourly Water Demand Forecasting Preprocessing Framework with Local Outlier Correction and Adaptive Decomposition Techniques

Water ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 582 ◽  
Author(s):  
Shiyuan Hu ◽  
Jinliang Gao ◽  
Dan Zhong ◽  
Liqun Deng ◽  
Chenhao Ou ◽  
...  
Keyword(s):  
Water Demand ◽  
Water Distribution ◽  
Demand Forecasting ◽  
Cost Effective ◽  
Distribution Networks ◽  
Correction Method ◽  
Support Vector ◽  
Forecasting Models ◽  
Water Demand Forecasting ◽  
Local Outlier

Accurate forecasting of hourly water demand is essential for effective and sustainable operation, and the cost-effective management of water distribution networks. Unlike monthly or yearly water demand, hourly water demand has more fluctuations and is easily affected by short-term abnormal events. An effective preprocessing method is needed to capture the hourly water demand patterns and eliminate the interference of abnormal data. In this study, an innovative preprocessing framework, including a novel local outlier detection and correction method Isolation Forest (IF), an adaptive signal decomposition technique Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), and basic forecasting models have been developed. In order to compare a promising deep learning method Gated Recurrent Unit (GRU) as a basic forecasting model with the conventional forecasting models, Support Vector Regression (SVR) and Artificial Neural Network (ANN) have been used. The results show that the proposed hybrid method can utilize the complementary advantages of the preprocessing methods to improve the accuracy of the forecasting models. The root-mean-square error of the SVR, ANN, and GRU models has been reduced by 57.5%, 27.8%, and 30.0%, respectively. Further, the GRU-based models developed in this study are superior to the other models, and the IF-CEEMDAN-GRU model has the highest accuracy. Hence, it is promising that this preprocessing framework can improve the performance of the water demand forecasting models.

Quantification of Water Savings due to Drought Restrictions in Water Demand Forecasting Models

2014 ◽  
Vol 140 (11) ◽  
pp. 04014035 ◽  
Author(s):  
Md. Mahmudul Haque ◽  
Dharma Hagare ◽  
Ataur Rahman ◽  
Golam Kibria
Keyword(s):  
Water Demand ◽  
Demand Forecasting ◽  
Water Savings ◽  
Forecasting Models ◽  
Water Demand Forecasting

scholarly journals Variable selection methods for water demand forecasting in Ethiopia: Case study Gondar town

2018 ◽  
Vol 4 (1) ◽  
pp. 1537067 ◽  
Author(s):  
Mohammed Gedefaw ◽  
Wang Hao ◽  
Yan Denghua ◽  
Abel Girma ◽  
Mustafa Ibrahim Khamis
Keyword(s):  
Variable Selection ◽  
Water Demand ◽  
Demand Forecasting ◽  
Selection Methods ◽  
Water Demand Forecasting ◽  
Gondar Town
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