ABF: A data-driven approach for algal bloom forecasting using machine intelligence and remotely sensed data series[Formula presented]
dc.contributor.author | Ananias, Pedro Henrique M. [UNESP] | |
dc.contributor.author | Negri, Rogério G. [UNESP] | |
dc.contributor.author | Bressane, Adriano [UNESP] | |
dc.contributor.author | Dias, Maurício A. [UNESP] | |
dc.contributor.author | Silva, Erivaldo A. [UNESP] | |
dc.contributor.author | Casaca, Wallace [UNESP] | |
dc.contributor.institution | Universidade Estadual Paulista (UNESP) | |
dc.date.accessioned | 2025-04-29T19:15:09Z | |
dc.date.issued | 2023-09-01 | |
dc.description.abstract | This 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.affiliation | São Paulo State University (UNESP), São José dos Campos | |
dc.description.affiliation | Graduate Program in Natural Disasters (UNESP/CEMADEN), São José dos Campos | |
dc.description.affiliation | Civil and Environmental Engineering Graduate Program (UNESP), Bauru | |
dc.description.affiliation | São Paulo State University (UNESP), Presidente Prudente | |
dc.description.affiliation | São Paulo State University (UNESP), São José do Rio Preto | |
dc.description.affiliationUnesp | São Paulo State University (UNESP), São José dos Campos | |
dc.description.affiliationUnesp | Graduate Program in Natural Disasters (UNESP/CEMADEN), São José dos Campos | |
dc.description.affiliationUnesp | Civil and Environmental Engineering Graduate Program (UNESP), Bauru | |
dc.description.affiliationUnesp | São Paulo State University (UNESP), Presidente Prudente | |
dc.description.affiliationUnesp | São Paulo State University (UNESP), São José do Rio Preto | |
dc.description.sponsorship | Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) | |
dc.description.sponsorship | Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) | |
dc.description.sponsorshipId | FAPESP: 2016/24185-8 | |
dc.description.sponsorshipId | FAPESP: 2021/01305-6 | |
dc.description.sponsorshipId | FAPESP: 2021/03328-3 | |
dc.description.sponsorshipId | CNPq: 305220/2022-5 | |
dc.description.sponsorshipId | CNPq: 316228/2021-4 | |
dc.identifier | http://dx.doi.org/10.1016/j.simpa.2023.100518 | |
dc.identifier.citation | Software Impacts, v. 17. | |
dc.identifier.doi | 10.1016/j.simpa.2023.100518 | |
dc.identifier.issn | 2665-9638 | |
dc.identifier.scopus | 2-s2.0-85161664243 | |
dc.identifier.uri | https://hdl.handle.net/11449/302644 | |
dc.language.iso | eng | |
dc.relation.ispartof | Software Impacts | |
dc.source | Scopus | |
dc.subject | Algal bloom | |
dc.subject | Forecasting | |
dc.subject | Machine learning | |
dc.subject | Remote sensing | |
dc.title | ABF: A data-driven approach for algal bloom forecasting using machine intelligence and remotely sensed data series[Formula presented] | en |
dc.type | Artigo | pt |
dspace.entity.type | Publication | |
unesp.author.orcid | 0000-0002-4808-2362 0000-0002-4808-2362[2] | |
unesp.campus | Universidade Estadual Paulista (UNESP), Instituto de Ciência e Tecnologia, São José dos Campos | pt |
unesp.campus | Universidade Estadual Paulista (UNESP), Faculdade de Ciências e Tecnologia, Presidente Prudente | pt |
unesp.campus | Universidade Estadual Paulista (UNESP), Instituto de Biociências, Letras e Ciências Exatas, São José do Rio Preto | pt |