Semantic Scholar Open Access 2024 4 sitasi

Challenges, Methods, Data-A Survey of Machine Learning in Water Distribution Networks

Valerie Vaquet Fabian Hinder André Artelt Inaam Ashraf Janine Strotherm +3 lainnya

Abstrak

Research on methods for planning and controlling water distribution networks gains increasing relevance as the availability of drinking water will decrease as a consequence of climate change. So far, the majority of approaches is based on hydraulics and engineering expertise. However, with the increasing availability of sensors, machine learning techniques constitute a promising tool. This work presents the main tasks in water distribution networks, discusses how they relate to machine learning and analyses how the particularities of the domain pose challenges to and can be leveraged by machine learning approaches. Besides, it provides a technical toolkit by presenting evaluation benchmarks and a structured survey of the exemplary task of leakage detection and localization.

Topik & Kata Kunci

Computer Science

Penulis (8)

Valerie Vaquet

Fabian Hinder

André Artelt

Inaam Ashraf

Janine Strotherm

Jonas Vaquet

Johannes Brinkrolf

Barbara Hammer

Format Sitasi

APA MLA BibTeX

Vaquet, V., Hinder, F., Artelt, A., Ashraf, I., Strotherm, J., Vaquet, J. et al. (2024). Challenges, Methods, Data-A Survey of Machine Learning in Water Distribution Networks. https://doi.org/10.1007/978-3-031-72356-8_11

Akses Cepat

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Lihat di Sumber doi.org/10.1007/978-3-031-72356-8_11

Informasi Jurnal

Tahun Terbit: 2024
Bahasa: en
Total Sitasi: 4×
Sumber Database: Semantic Scholar
DOI: 10.1007/978-3-031-72356-8_11
Akses: Open Access ✓