Intelligent Decision Support System for Real-Time Water Demand Management

DOI

10.1080/18756891.2016.1146533How to use a DOI?

Keywords

Water Demand Management; Decision Support System; Multi-agent Systems; Neural Networks

Abstract

Environmental and demographic pressures have led to the current importance of Water Demand Management (WDM), where the concepts of efficiency and sustainability now play a key role. Water must be conveyed to where it is needed, in the right quantity, at the required pressure, and at the right time using the fewest resources. This paper shows how modern Artificial Intelligence (AI) techniques can be applied on this issue from a holistic perspective. More specifically, the multi-agent methodology has been used in order to design an Intelligent Decision Support System (IDSS) for real-time WDM. It determines the optimal pumping quantity from the storage reservoirs to the points-of-consumption in an hourly basis. This application integrates advanced forecasting techniques, such as Artificial Neural Networks (ANNs), and other components within the overall aim of minimizing WDM costs. In the tests we have performed, the system achieves a large reduction in these costs. Moreover, the multi-agent environment has demonstrated to propose an appropriate framework to tackle this issue.

Copyright

© 2016. the authors. Co-published by Atlantis Press and Taylor & Francis

Open Access

This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

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TY  - JOUR
AU  - Borja Ponte
AU  - David de la Fuente
AU  - José Parreño
AU  - Raúl Pino
PY  - 2016
DA  - 2016/01/18
TI  - Intelligent Decision Support System for Real-Time Water Demand Management
JO  - International Journal of Computational Intelligence Systems
SP  - 168
EP  - 183
VL  - 9
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.1080/18756891.2016.1146533
DO  - 10.1080/18756891.2016.1146533
ID  - Ponte2016
ER  -