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Machine Learning, Urban Water Resources Management and Operating Policy

Author

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  • Evangelos Rozos

    (National Observatory of Athens, Institute for Environmental Research & Sustainable Development, GR-15236 Athens, Greece)

Abstract

Meticulously analyzing all contemporaneous conditions and available options before taking operations decisions regarding the management of the urban water resources is a necessary step owing to water scarcity. More often than not, this analysis is challenging because of the uncertainty regarding inflows to the system. The most common approach to account for this uncertainty is to combine the Bayesian decision theory with the dynamic programming optimization method. However, dynamic programming is plagued by the curse of dimensionality, that is, the complexity of the method is proportional to the number of discretized possible system states raised to the power of the number of reservoirs. Furthermore, classical statistics does not consistently represent the stochastic structure of the inflows (see persistence). To avoid these problems, this study will employ an appropriate stochastic model to produce synthetic time-series with long-term persistence, optimize the system employing a network flow programming modelling, and use the optimization results for training a feedforward neural network (FFN). This trained FFN alone can serve as a decision support tool that describes not only reservoir releases but also how to operate the entire water supply system. This methodology is applied in a simplified representation of the Athens water supply system, and the results suggest that the FFN is capable of successfully operating the system according to a predefined operating policy.

Suggested Citation

  • Evangelos Rozos, 2019. "Machine Learning, Urban Water Resources Management and Operating Policy," Resources, MDPI, vol. 8(4), pages 1-13, November.
    • Handle: RePEc:gam:jresou:v:8:y:2019:i:4:p:173-:d:286880
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    References listed on IDEAS

    as
    1. A. Cancelliere & G. Giuliano & A. Ancarani & G. Rossi, 2002. "A Neural Networks Approach for Deriving Irrigation Reservoir Operating Rules," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 16(1), pages 71-88, February.
    2. Deepti Rani & Maria Moreira, 2010. "Simulation–Optimization Modeling: A Survey and Potential Application in Reservoir Systems Operation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(6), pages 1107-1138, April.
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