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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 R2-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.<\/jats:p>","DOI":"10.3390\/rs14174283","type":"journal-article","created":{"date-parts":[[2022,8,31]],"date-time":"2022-08-31T00:13:56Z","timestamp":1661904836000},"page":"4283","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["A Fully Unsupervised Machine Learning Framework for Algal Bloom Forecasting in Inland Waters Using MODIS Time Series and Climatic Products"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0924-1236","authenticated-orcid":false,"given":"Pedro Henrique M.","family":"Ananias","sequence":"first","affiliation":[{"name":"Graduate Program in Natural Disasters, S\u00e3o Paulo State University (UNESP), National Center for Monitoring and Early Warning of Natural Disasters (CEMADEN), S\u00e3o Jos\u00e9 dos Campos 12245-000, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4808-2362","authenticated-orcid":false,"given":"Rog\u00e9rio G.","family":"Negri","sequence":"additional","affiliation":[{"name":"Graduate Program in Natural Disasters, S\u00e3o Paulo State University (UNESP), National Center for Monitoring and Early Warning of Natural Disasters (CEMADEN), S\u00e3o Jos\u00e9 dos Campos 12245-000, Brazil"},{"name":"Science and Technology Institute (ICT), S\u00e3o Paulo State University (UNESP), S\u00e3o Jos\u00e9 dos Campos 01049-010, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1361-6184","authenticated-orcid":false,"given":"Maur\u00edcio A.","family":"Dias","sequence":"additional","affiliation":[{"name":"Faculty of Science and Technology (FCT), S\u00e3o Paulo State University (UNESP), Presidente Prudente 19060-900, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7069-0479","authenticated-orcid":false,"given":"Erivaldo A.","family":"Silva","sequence":"additional","affiliation":[{"name":"Faculty of Science and Technology (FCT), S\u00e3o Paulo State University (UNESP), Presidente Prudente 19060-900, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1073-9939","authenticated-orcid":false,"given":"Wallace","family":"Casaca","sequence":"additional","affiliation":[{"name":"Institute of Biosciences, Letters and Exact Sciences (IBILCE), S\u00e3o Paulo State University (UNESP), S\u00e3o Jos\u00e9 do Rio Preto 15054-000, Brazil"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1392","DOI":"10.1002\/ldr.3543","article-title":"Spatial\u2013temporal variations in urbanization in Kunming and their impact on urban lake water quality","volume":"31","author":"Yang","year":"2020","journal-title":"Land Degrad. 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