Probabilistic Prediction of Satellite-Derived Water Quality for a Drinking Water Reservoir

A Bayesian network-based modelling framework was proposed to predict the probability of exceeding critical thresholds for chlorophyll-a and turbidity in an Australian subtropical drinking water reservoir, based on Sentinel-2 data and prior knowledge. The model was trained with quasi-synchronous hist...

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Bibliographic Details
Published inSustainability Vol. 15; no. 14; p. 11302
Main Authors Bertone, Edoardo, Peters Hughes, Sara
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.07.2023
Subjects
Accuracy
Algae
Analysis
Chlorophyll
Data collection
Datasets
Drinking water
Geospatial data
Probability
Reservoirs
Satellites
Sensors
Sustainability
Water quality
Water treatment
Australia
Queensland Australia
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Summary:A Bayesian network-based modelling framework was proposed to predict the probability of exceeding critical thresholds for chlorophyll-a and turbidity in an Australian subtropical drinking water reservoir, based on Sentinel-2 data and prior knowledge. The model was trained with quasi-synchronous historical in situ and satellite data for 2018–2023 and achieved satisfactory accuracy (Brier score < 0.27 for all models) despite limited poor water quality events in the final dataset. The graphical output of the model (posterior probability maps of high turbidity or chlorophyll-a) provides an effective means for the user to evaluate both the prediction, and the uncertainty behind the predictions in a single map. This avoids loss of trust in the model and can trigger spatially targeted data collection in order to reduce uncertainty. Future work will focus on refining the modelling methodology and its automation, as well as including other data such as in situ high-frequency sensors.
ISSN:2071-1050
2071-1050
DOI:10.3390/su151411302