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dc.contributor.authorBlix, Katalin
dc.contributor.authorEltoft, Torbjørn
dc.date.accessioned2018-10-26T07:04:50Z
dc.date.available2018-10-26T07:04:50Z
dc.date.issued2018-05-17
dc.description.abstractOcean Color remote sensing has a great importance in monitoring of aquatic environments. The number of optical imaging sensors onboard satellites has been increasing in the past decades, allowing to retrieve information about various water quality parameters of the world’s oceans and inland waters. This is done by using various regression algorithms to retrieve water quality parameters from remotely sensed multi-spectral data for the given sensor and environment. There is a great number of such algorithms for estimating water quality parameters with different performances. Hence, choosing the most suitable model for a given purpose can be challenging. This is especially the fact for optically complex aquatic environments. In this paper, we present a concept to an Automatic Model Selection Algorithm (AMSA) aiming at determining the best model for a given matchup dataset. AMSA automatically chooses between regression models to estimate the parameter in interest. AMSA also determines the number and combination of features to use in order to obtain the best model. We show how AMSA can be built for a certain application. The example AMSA we present here is designed to estimate oceanic Chlorophyll-a for global and optically complex waters by using four Machine Learning (ML) feature ranking methods and three ML regression models. We use a synthetic and two real matchup datasets to find the best models. Finally, we use two images from optically complex waters to illustrate the predictive power of the best models. Our results indicate that AMSA has a great potential to be used for operational purposes. It can be a useful objective tool for finding the most suitable model for a given sensor, water quality parameter and environment.en_US
dc.description.sponsorshipCIRFA partnersen_US
dc.descriptionFirst published in <i>Remote Sensing</i>. Source at <a href=https://doi.org/10.3390/rs10050775> https://doi.org/10.3390/rs10050775</a>.en_US
dc.identifier.citationBlix, K. & Eltoft, T. (2018). Machine Learning Automatic Model Selection Algorithm for Oceanic Chlorophyll-a Content Retrieval. Remote Sensing, 10(5). https://doi.org/10.3390/rs10050775en_US
dc.identifier.cristinIDFRIDAID 1585510
dc.identifier.doi10.3390/rs10050775
dc.identifier.issn2072-4292
dc.identifier.urihttps://hdl.handle.net/10037/14038
dc.language.isoengen_US
dc.publisherMDPIen_US
dc.relation.ispartofBlix, K. (2019). Machine Learning Water Quality Monitoring. (Doctoral thesis). <a href=https://hdl.handle.net/10037/16502>https://hdl.handle.net/10037/16502</a>.
dc.relation.journalRemote Sensing
dc.relation.projectIDinfo:eu-repo/grantAgreement/RCN/SFI/237906/Norway/Centre for Integrated Remote Sensing and Forecasting for Arctic Operations/CIRFA/en_US
dc.rights.accessRightsopenAccessen_US
dc.subjectVDP::Mathematics and natural science: 400::Physics: 430en_US
dc.subjectVDP::Matematikk og Naturvitenskap: 400::Fysikk: 430en_US
dc.subjectocean coloren_US
dc.subjectremote sensingen_US
dc.subjectmodel selectionen_US
dc.subjectfeature rankingen_US
dc.subjectregressionen_US
dc.titleMachine Learning Automatic Model Selection Algorithm for Oceanic Chlorophyll-a Content Retrievalen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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