An advanced learning-based multiple model control supervisor for pumping stations in a smart water distribution system

Abstract

Water distribution is fundamental to modern society, and there are many associated challenges in the context of large metropolitan areas. A multi-domain approach is required for designing modern solutions for the existing infrastructure, including control and monitoring systems, data science and Machine Learning. Considering the large scale water distribution networks in metropolitan areas, machine and deep learning algorithms can provide improved adaptability for control applications. This paper presents a monitoring and control machine learning-based architecture for a smart water distribution system. Automated test scenarios and learning methods are proposed and designed to predict the network configuration for a modern implementation of a multiple model control supervisor with increased adaptability to changing operating conditions. The high-level processing and components for smart water distribution systems are supported by the smart meters, providing real-time data, push-based and decoupled software architectures and reactive programming.