Project Details

Description

This project aims to identify and analyse the potential for value creation in the intersection between machine learning and the utility sector, with focus on the water supply sector. The specific areas targeted are: Sewers, drains, climate and streams, wastewater treatment plant, clean water, energy, and software and economy within the water sector.
Short titleMachine Learning in The Water Sector
StatusActive
Effective start/end date01/01/201931/12/2021

Fingerprint

Explore the research topics touched on by this project. These labels are generated based on the underlying awards/grants. Together they form a unique fingerprint.
  • General Sewer Deterioration Model Using Random Forest

    Hansen, B. D., Jensen, D. G., Rasmussen, S. H., Tamouk, J., Uggerby, M. & Moeslund, T. B., 2019, IEEE Symposium on Computational Intelligence for Engineering Solutions (IEEE CIES). IEEE, 8 p.

    Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

    2 Citations (Scopus)