Estimating pollution spread in water networks as a Schrödinger bridge problem with partial information
2023 (English)In: European Journal of Control, ISSN 0947-3580, E-ISSN 1435-5671, article id 100846Article in journal (Refereed) Published
Abstract [en]
Incidents where water networks are contaminated with microorganisms or pollutants can result in a large number of infected or ill persons, and it is therefore important to quickly detect, localize and estimate the spread and source of the contamination. In many of today's water networks only limited measurements are available, but with the current internet of things trend the number of sensors is increasing and there is a need for methods that can utilize this information. Motivated by this fact, we address the problem of estimating the spread of pollution in a water network given measurements from a set of sensors. We model the water flow as a Markov chain, representing the system as a set of states where each state represents the amount of water in a specific part of the network, e.g., a pipe or a part of a pipe. Then we seek the most likely flow of the pollution given the expected water flow and the sensors observations. This is a large-scale optimization problem that can be formulated as a Schrödinger bridge problem with partial information, and we address this by exploiting the connection with the entropy regularized multimarginal optimal transport problem. The software EPANET is used to simulate the spread of pollution in the water network and will be used for testing the performance of the methodology. © 2023 The Author(s)
Place, publisher, year, edition, pages
Elsevier Ltd , 2023. article id 100846
Keywords [en]
Markov processes, Optimization algorithms, Schrödinger bridge, Sensor and signal fusion, Flow of water, Hydraulics, Optimization, Software testing, Water pollution, 'current, Bridge problems, Most likely, Partial information, Schrödinge bridge, Sensor fusion, Signal fusions, Water flows, Water networks
National Category
Natural Sciences
Identifiers
URN: urn:nbn:se:ri:diva-65675DOI: 10.1016/j.ejcon.2023.100846Scopus ID: 2-s2.0-85163881960OAI: oai:DiVA.org:ri-65675DiVA, id: diva2:1786119
Note
This work was supported by KTH Digital Futures, the Knut and Alice Wallenberg foundation under grant KAW 2021.0274, and the Swedish Research Council (VR) under grant 2020-03454.
2023-08-072023-08-072023-08-07Bibliographically approved