Chlorophyll soft-sensor based on machine learning models for algal bloom predictions

• Published in: Scientific reports Vol. 12; no. 1; p. 13529
• Main Authors: Mozo, Alberto, Morón-López, Jesús, Vakaruk, Stanislav, Pompa-Pernía, Ángel G., González-Prieto, Ángel, Aguilar, Juan Antonio Pascual, Gómez-Canaval, Sandra, Ortiz, Juan Manuel
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 08.08.2022; Nature Publishing Group; Nature Portfolio
• Subjects: 631/114/1305; 704/172; 704/172/4081; Algal blooms; Aquatic ecosystems; Chlorophyll; Electrical conductivity; Energy efficiency; Eutrophication; Humanities and Social Sciences; Learning algorithms; Machine learning; multidisciplinary; Public health; Science; Science (multidisciplinary); Sensors; Water monitoring; Water quality; Water temperature

Harmful algal blooms (HABs) are a growing concern to public health and aquatic ecosystems. Long-term water monitoring conducted by hand poses several limitations to the proper implementation of water safety plans. This work combines automatic high-frequency monitoring (AFHM) systems with machine lea...