dc.contributor.author | Berget, Gunhild Elisabeth | |
dc.contributor.author | Eidsvik, Jo | |
dc.contributor.author | Alver, Morten Omholt | |
dc.contributor.author | Johansen, Tor Arne | |
dc.date.accessioned | 2023-05-08T10:12:52Z | |
dc.date.available | 2023-05-08T10:12:52Z | |
dc.date.created | 2023-05-04T10:34:08Z | |
dc.date.issued | 2023 | |
dc.identifier.issn | 0929-5593 | |
dc.identifier.uri | https://hdl.handle.net/11250/3066802 | |
dc.description.abstract | Discharge of mine tailings significantly impacts the ecological status of the sea. Methods to efficiently monitor the extent of dispersion is essential to protect sensitive areas. By combining underwater robotic sampling with ocean models, we can choose informative sampling sites and adaptively change the robot’s path based on in situ measurements to optimally map the tailings distribution near a seafill. This paper creates a stochastic spatio-temporal proxy model of dispersal dynamics using training data from complex numerical models. The proxy model consists of a spatio-temporal Gaussian process model based on an advection–diffusion stochastic partial differential equation. Informative sampling sites are chosen based on predictions from the proxy model using an objective function favoring areas with high uncertainty and high expected tailings concentrations. A simulation study and data from real-life experiments are presented. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | Springer | en_US |
dc.rights | CC BY 4.0 | * |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | * |
dc.subject | Adaptive sampling | en_US |
dc.subject | Gaussian process | en_US |
dc.subject | Stochastic modeling | en_US |
dc.subject | AUV | en_US |
dc.subject | Mine tailings | en_US |
dc.title | Dynamic stochasticmodeling for adaptive sampling of environmental variables using an AUV | en_US |
dc.title.alternative | Dynamic stochasticmodeling for adaptive sampling of environmental variables using an AUV | en_US |
dc.type | Peer reviewed | en_US |
dc.type | Journal article | en_US |
dc.description.version | publishedVersion | en_US |
dc.rights.holder | © 2023 The authors | en_US |
dc.subject.nsi | VDP::Teknologi: 500 | en_US |
dc.source.journal | Autonomous Robots | en_US |
dc.identifier.doi | 10.1007/s10514-023-10095-8 | |
dc.identifier.cristin | 2145363 | |
dc.relation.project | Norges forskningsråd: 267793 | en_US |
dc.relation.project | Norges forskningsråd: 223254 | en_US |
cristin.ispublished | true | |
cristin.fulltext | original | |
cristin.qualitycode | 1 | |