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ABF: A data-driven approach for algal bloom forecasting using machine intelligence and remotely sensed data series[Formula presented]

dc.contributor.authorAnanias, Pedro Henrique M. [UNESP]
dc.contributor.authorNegri, Rogério G. [UNESP]
dc.contributor.authorBressane, Adriano [UNESP]
dc.contributor.authorDias, Maurício A. [UNESP]
dc.contributor.authorSilva, Erivaldo A. [UNESP]
dc.contributor.authorCasaca, Wallace [UNESP]
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)
dc.date.accessioned2025-04-29T19:15:09Z
dc.date.issued2023-09-01
dc.description.abstractThis paper presents a fully automated framework for algal bloom forecasting in inland water by combining remote sensing data series and unsupervised machine learning concepts. In contrast to other methods in the specialized literature that usually employ pre-labeled data, the proposed approach was designed to be fully autonomous concerning pre-requisites, assuming as input only a time series of remotely sensed products to forecast algal proliferation. In more technical terms, the designed machine-intelligent methodology comprises the steps of pre-processing, feature extraction and modeling, and it learns unsupervised from past events to predict future scenarios of algal blooms, outputting algal insurgence maps.en
dc.description.affiliationSão Paulo State University (UNESP), São José dos Campos
dc.description.affiliationGraduate Program in Natural Disasters (UNESP/CEMADEN), São José dos Campos
dc.description.affiliationCivil and Environmental Engineering Graduate Program (UNESP), Bauru
dc.description.affiliationSão Paulo State University (UNESP), Presidente Prudente
dc.description.affiliationSão Paulo State University (UNESP), São José do Rio Preto
dc.description.affiliationUnespSão Paulo State University (UNESP), São José dos Campos
dc.description.affiliationUnespGraduate Program in Natural Disasters (UNESP/CEMADEN), São José dos Campos
dc.description.affiliationUnespCivil and Environmental Engineering Graduate Program (UNESP), Bauru
dc.description.affiliationUnespSão Paulo State University (UNESP), Presidente Prudente
dc.description.affiliationUnespSão Paulo State University (UNESP), São José do Rio Preto
dc.description.sponsorshipFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.description.sponsorshipConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.description.sponsorshipIdFAPESP: 2016/24185-8
dc.description.sponsorshipIdFAPESP: 2021/01305-6
dc.description.sponsorshipIdFAPESP: 2021/03328-3
dc.description.sponsorshipIdCNPq: 305220/2022-5
dc.description.sponsorshipIdCNPq: 316228/2021-4
dc.identifierhttp://dx.doi.org/10.1016/j.simpa.2023.100518
dc.identifier.citationSoftware Impacts, v. 17.
dc.identifier.doi10.1016/j.simpa.2023.100518
dc.identifier.issn2665-9638
dc.identifier.scopus2-s2.0-85161664243
dc.identifier.urihttps://hdl.handle.net/11449/302644
dc.language.isoeng
dc.relation.ispartofSoftware Impacts
dc.sourceScopus
dc.subjectAlgal bloom
dc.subjectForecasting
dc.subjectMachine learning
dc.subjectRemote sensing
dc.titleABF: A data-driven approach for algal bloom forecasting using machine intelligence and remotely sensed data series[Formula presented]en
dc.typeArtigopt
dspace.entity.typePublication
unesp.author.orcid0000-0002-4808-2362 0000-0002-4808-2362[2]
unesp.campusUniversidade Estadual Paulista (UNESP), Instituto de Ciência e Tecnologia, São José dos Campospt
unesp.campusUniversidade Estadual Paulista (UNESP), Faculdade de Ciências e Tecnologia, Presidente Prudentept
unesp.campusUniversidade Estadual Paulista (UNESP), Instituto de Biociências, Letras e Ciências Exatas, São José do Rio Pretopt

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