Mozo, A. and Morón-López, J. and Vakaruk, S. and Pompa, Á.G. and González-Prieto, Á. and Pascual, J.A. and Gómez-Canaval, S. and Ortiz, J.M. (2022) Chlorophyll soft-sensor based on machine learning models for algal bloom predictions. Scientific Reports, 12 (1). ISSN 2045-2322
|
Text (Open access)
Soft-sensors.pdf - Published Version Available under License Creative Commons Attribution. Download (17Mb) | Preview |
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 μg/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.
| Item Type: | Article |
|---|---|
| Subjects: | T Technology > TD Environmental technology. Sanitary engineering |
| Depositing User: | Dr. Juan Antonio Pascual Aguilar |
| Date Deposited: | 05 Sep 2022 13:59 |
| Last Modified: | 05 Sep 2022 13:59 |
| URI: | http://eprints.imdea-agua.org:13000/id/eprint/1399 |
Actions (login required)
 |
View Item |
