ABF: A data-driven approach for algal bloom forecasting using machine intelligence and remotely sensed data series

Author(s)

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

• Document type: Journal article
• Source: SOFTWARE IMPACTS; v. 17, p. 3-pg., 2023-06-13.

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. (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: 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
• 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