Machine learning for cyanobacteria inversion via remote sensing and AlgaeTorch in the Třeboň fishponds, Czech Republic

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60460709%3A41330%2F24%3A98773" target="_blank" >RIV/60460709:41330/24:98773 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1016/j.scitotenv.2024.174504" target="_blank" >https://doi.org/10.1016/j.scitotenv.2024.174504</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.scitotenv.2024.174504" target="_blank" >10.1016/j.scitotenv.2024.174504</a>

Result language

angličtina

Original language name

Machine learning for cyanobacteria inversion via remote sensing and AlgaeTorch in the Třeboň fishponds, Czech Republic

Original language description

Cyanobacteria blooms in fishponds, driven by climate change and anthropogenic activities, have become a critical concern for aquatic ecosystems worldwide. The diversity in fishpond sizes and fish densities further complicates their monitoring. This study addresses the challenge of accurately predicting cyanobacteria concentrations in turbid waters via remote sensing, hindered by optical complexities and diminished light signals. A comprehensive dataset of 740 sampling points was compiled, encompassing water quality metrics (cyanobacteria levels, total chlorophyll, turbidity, total cell count) and spectral data obtained through AlgaeTorch, alongside Sentinel-2 reflectance data from three Třeboň fishponds (UNESCO Man and Biosphere Reserve) in the Czech Republic over 2022–2023. Partial Least Squares Regression (PLSR) and three machine learning algorithms, Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost), were developed based on seasonal and annual data volumes. The SVM algorithm demonstrated commendable performance on the one-year data validation dataset from the Svět fishpond for the prediction of cyanobacteria, reflected by the key performance indicators: R2 = 0.88, RMSE = 15.07 µg Chl-a/L, and RPD = 2.82. Meanwhile, SVM displayed steady results in the unified one-year validation dataset from Naděje, Svět, and Vizír fishponds, with metrics showing R2 = 0.56, RMSE = 39.03 µg Chl-a/L, RPD = 1.50. Thus, Sentinel data proved viable for seasonal cyanobacteria monitoring across different fishponds. Overall, this study presents a novel approach for enhancing the precision of cyanobacteria predictions and long-term ecological monitoring in fishponds, contributing significantly to the water quality management strategies in the Třeboň region.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10511 - Environmental sciences (social aspects to be 5.7)

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2024

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Science of the Total Environment

ISSN

0048-9697

e-ISSN

0048-9697

Volume of the periodical

947

Issue of the periodical within the volume

2024-10-15

Country of publishing house

NL - THE KINGDOM OF THE NETHERLANDS

Number of pages

16

Pages from-to

1-16

UT code for WoS article

001092798100001

EID of the result in the Scopus database

2-s2.0-85197809153