机器学习在供水网络中的挑战、方法与数据综述
机器学习
2024-10-17 v1
摘要
随着气候变化导致饮用水供应不足，针对供水网络规划与控制的方法研究日益相关。迄今为止，大多数方法基于水力学与工程专业知识。然而，随着传感器的日益增多，机器学习技术正成为具有前景的工具。本文呈现供水网络的主要任务，探讨其与机器学习的关系，分析该领域特性对机器学习方法的挑战与潜在优势。此外，本文提供技术工具箱，呈现评估基准并对漏检测与定位这一典型任务进行结构化综述。
引用
@article{arxiv.2410.12461,
title = {Challenges, Methods, Data -- a Survey of Machine Learning in Water Distribution Networks},
author = {Valerie Vaquet and Fabian Hinder and André Artelt and Inaam Ashraf and Janine Strotherm and Jonas Vaquet and Johannes Brinkrolf and Barbara Hammer},
journal= {arXiv preprint arXiv:2410.12461},
year = {2024}
}
备注
This preprint has not undergone any post-submission improvements or corrections. The Version of Record of this contribution is published in Artificial Neural Networks and Machine Learning -- ICANN 2024