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

Autor(es)

Ananias, Pedro Henrique M. ; Negri, Rogerio G. ; Dias, Mauricio A. ; Silva, Erivaldo A. ; Casaca, Wallace

• Tipo de documento: Artigo Científico
• Fonte: REMOTE SENSING; v. 14, n. 17, p. 22-pg., 2022-09-01.

Resumo

Progressively 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. (AU)

• Processo FAPESP: 21/03328-3 - Desenvolvimento de novas metodologias e soluções tecnológicas inteligentes em segmentação de imagens digitais e enfrentamento da COVID-19
• Beneficiário: Wallace Correa de Oliveira Casaca
• Modalidade de apoio: Auxílio à Pesquisa - Regular
• Processo FAPESP: 21/01305-6 - Avanços teóricos em detecção de anomalias e construção de sistemas de monitoramento ambiental
• Beneficiário: Rogério Galante Negri
• Modalidade de apoio: Auxílio à Pesquisa - Regular
• Processo FAPESP: 16/24185-8 - Detecção, análise e localização de anomalias: um estudo de caso sobre imagens digitais estáticas de sensoriamento remoto aplicado à Cartografia
• Beneficiário: Maurício Araújo Dias
• Modalidade de apoio: Bolsas no Exterior - Pesquisa