| Full text | |
| Author(s): |
Ananias, Pedro Henrique M.
;
Negri, Rogerio G.
;
Dias, Mauricio A.
;
Silva, Erivaldo A.
;
Casaca, Wallace
Total Authors: 5
|
| Document type: | Journal article |
| Source: | REMOTE SENSING; v. 14, n. 17, p. 22-pg., 2022-09-01. |
| Abstract | |
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) | |
| FAPESP's process: | 21/03328-3 - Development of new methodologies and machine intelligence-based technological solutions for digital image segmentation and COVID-19 pandemic response |
| Grantee: | Wallace Correa de Oliveira Casaca |
| Support Opportunities: | Regular Research Grants |
| FAPESP's process: | 21/01305-6 - Theoretical advances on anomaly detection and environmental monitoring systems building |
| Grantee: | Rogério Galante Negri |
| Support Opportunities: | Regular Research Grants |
| FAPESP's process: | 16/24185-8 - Anomaly detection, analysis and localization: a case study on digital static images from remote sensing applied to Cartography |
| Grantee: | Maurício Araújo Dias |
| Support Opportunities: | Scholarships abroad - Research |