scholarly journals A Two-Layer Water Demand Prediction System in Urban Areas Based on Micro-Services and LSTM Neural Networks

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 147647-147661
Author(s):  
Ahmed Abdel Nasser ◽  
Magdi Z. Rashad ◽  
Sherif E. Hussein
Keyword(s):  
Neural Networks ◽  
Water Demand ◽  
Urban Areas ◽  
Prediction System ◽  
Demand Prediction ◽  
Water Demand Prediction

Adaptable urban water demand prediction system

2015 ◽  
Vol 15 (5) ◽  
pp. 958-964 ◽  
Author(s):  
G. Banjac ◽  
M. Vašak ◽  
M. Baotić
Keyword(s):  
Mutual Information ◽  
Water Demand ◽  
Distribution Systems ◽  
Water Distribution ◽  
Meteorological Data ◽  
Model Parameters ◽  
Prediction System ◽  
Prediction Ability ◽  
Demand Prediction ◽  
Water Demand Prediction

In this work, identification of 24-hours-ahead water demand prediction model based on historical water demand data is considered. As part of the identification procedure, the input variable selection algorithm based on partial mutual information is implemented. It is shown that meteorological data on a daily basis are not relevant for the water demand prediction in the sense of partial mutual information for the analysed water distribution systems of the cities of Tavira, Algarve, Portugal and Evanton East, Scotland, UK. Water demand prediction system is modelled using artificial neural networks, which offer a great potential for the identification of complex dynamic systems. The adaptive tuning procedure of model parameters is also developed in order to enable the model to adapt to changes in the system. A significant improvement of the prediction ability of such a model in relation to the model with fixed parameters is shown when a certain trend is present in the water demand profile.

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