A Fully Unsupervised Machine Learning Framework for Algal Bloom Forecasting in Inland Waters Using MODIS Time Series and Climatic Products

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.creatorAnanias, Pedro Henrique M.-
Autor(es): dc.creatorNegri, Rogerio G.-
Autor(es): dc.creatorDias, Mauricio A.-
Autor(es): dc.creatorSilva, Erivaldo A.-
Autor(es): dc.creatorCasaca, Wallace-
Data de aceite: dc.date.accessioned2025-08-21T21:44:25Z-
Data de disponibilização: dc.date.available2025-08-21T21:44:25Z-
Data de envio: dc.date.issued2022-11-29-
Data de envio: dc.date.issued2022-11-29-
Data de envio: dc.date.issued2022-09-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.3390/rs14174283-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/237849-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/237849-
Descrição: dc.descriptionProgressively monitoring water quality is crucial, as aquatic contaminants can pose risks to human health and other organisms. Machine learning can support the development of new effective tools for water monitoring, including the detection of algal blooms from remotely sensed image series. Therefore, in this paper, we introduce the Algal Bloom Forecast (ABF) framework, a fully automated framework for algal bloom prediction in inland water bodies. Our approach combines machine learning, time series of remotely sensed products (i.e., Moderate-Resolution Imaging Spectroradiometer (MODIS) images), environmental data and spectral indices to build anomaly detection models that can predict the occurrence of algal bloom events in the posterior period. Our assessments focused on the application of the ABF framework equipped with the support vector machine (SVM), random forest (RF), and long short-term memory (LSTM) methods, the outcomes of which were compared through different evaluation metrics such as global accuracy, the kappa coefficient, F1-Score and R-2-Score. Case studies covering the Erie (USA), Chilika (India) and Taihu (China) lakes are presented to demonstrate the effectiveness and flexibility of our learning approach. Based on comprehensive experimental tests, we found that the best algal bloom predictions were achieved by bringing together the ABF design with the RF model.-
Descrição: dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)-
Descrição: dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)-
Descrição: dc.descriptionSao Paulo State University (UNESP)-
Descrição: dc.descriptionSao Paulo State Univ, UNESP, Natl Ctr Monitoring & Early Warning Nat Disasters, Grad Program Nat Disasters, BR-12245000 Sao Jose Dos Campos, Brazil-
Descrição: dc.descriptionSao Paulo State Univ, UNESP, Sci & Technol Inst ICT, BR-01049010 Sao Jose Dos Campos, Brazil-
Descrição: dc.descriptionSao Paulo State Univ, UNESP, Fac Sci & Technol FCT, BR-19060900 Presidente Prudente, Brazil-
Descrição: dc.descriptionSao Paulo State Univ, UNESP, Inst Biosci Letters & Exact Sci IBILCE, BR-15054000 Sao Jose Do Rio Preto, Brazil-
Descrição: dc.descriptionSao Paulo State Univ, UNESP, Natl Ctr Monitoring & Early Warning Nat Disasters, Grad Program Nat Disasters, BR-12245000 Sao Jose Dos Campos, Brazil-
Descrição: dc.descriptionSao Paulo State Univ, UNESP, Sci & Technol Inst ICT, BR-01049010 Sao Jose Dos Campos, Brazil-
Descrição: dc.descriptionSao Paulo State Univ, UNESP, Fac Sci & Technol FCT, BR-19060900 Presidente Prudente, Brazil-
Descrição: dc.descriptionSao Paulo State Univ, UNESP, Inst Biosci Letters & Exact Sci IBILCE, BR-15054000 Sao Jose Do Rio Preto, Brazil-
Descrição: dc.descriptionFAPESP: 2021/01305-6-
Descrição: dc.descriptionFAPESP: 2021/03328-3-
Descrição: dc.descriptionFAPESP: 2016/24185-8-
Descrição: dc.descriptionCNPq: 427915/2018-0-
Descrição: dc.descriptionCNPq: 304402/2019-2-
Descrição: dc.descriptionCNPq: 316228/2021-4-
Formato: dc.format22-
Idioma: dc.languageen-
Publicador: dc.publisherMdpi-
Relação: dc.relationRemote Sensing-
???dc.source???: dc.sourceWeb of Science-
Palavras-chave: dc.subjectAlgal bloom-
Palavras-chave: dc.subjectRemote sensing-
Palavras-chave: dc.subjectMODIS-
Palavras-chave: dc.subjectPrediction-
Palavras-chave: dc.subjectMachine learning-
Título: dc.titleA Fully Unsupervised Machine Learning Framework for Algal Bloom Forecasting in Inland Waters Using MODIS Time Series and Climatic Products-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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