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.




中文翻译：

---

先进的基于学习的智能水分配系统中泵站的多模型控制主管 物联网; 深度学习 机器学习 多模型控制主管

水资源分配是现代社会的基础，
在大都市地区背景下存在许多相关的挑战。
为现有基础架构（包括控制和监视系统，
数据科学和机器学习）设计现代解决方案时，需要采用多领域方法。考虑到
大都市地区的大规模供水网络，机器和深度学习算法可以
为控制应用程序提供更好的适应性。本文提出了一种
用于智能配水系统的基于监控机器学习的架构。
提出并设计了自动化测试场景和学习方法，以预测网络配置的现代化实现。
多模型控制主管，对变化的工作条件具有更大的适应性。
智能水表支持智能水分配系统的高级处理和组件
，可提供实时数据，基于推式和解耦的软件架构
以及反应式编程。