ArticleScientific reports2022
Chlorophyll soft-sensor based on machine learning models for algal bloom predictions.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed, 46 citations in OpenAlex.
- Microalgae-mediated removal and biodegradation of indoor volatile organic compounds: mechanisms, performance metrics, and engineering constraints.Biodegradation · 2026Review
- Portable Multispectral Fluorometer with Embedded Machine Learning for Chlorophyll-a Estimation in Acetone-Extracted Samples.Sensors (Basel, Switzerland) · 2026Article
- Review
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Authors and funding
8 authors at 4 institutions in 2 countries.
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No grant is acknowledged in the PubMed record.
Abstract
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 learning (ML) techniques to build a data-driven chlorophyll-a (Chl-a) soft-sensor. Massive data for water temperature, pH, electrical conductivity (EC) and system battery were taken for three years at intervals of 15 min from two different areas of As Conchas freshwater reservoir (NW Spain). We designed a set of soft-sensors based on compact and energy efficient ML algorithms to infer Chl-a fluorescence by using low-cost input variables and to be deployed on buoys with limited battery and hardware resources. Input and output aggregations were applied in ML models to increase their inference performance. A component capable of triggering a 10 [Formula: see text]g/L Chl-a alert was also developed. The results showed that Chl-a soft-sensors could be a rapid and inexpensive tool to support manual sampling in water bodies at risk.
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