Enhancing estuary salinity prediction: A Machine Learning and Deep Learning based approach Article Swipe
Leonardo Saccotelli
,
Giorgia Verri
,
Alessandro De Lorenzis
,
Carla Cherubini
,
Rocco Caccioppoli
,
Giovanni Coppini
,
Rosalia Maglietta
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.acags.2024.100173
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.acags.2024.100173
As critical transitional ecosystems, estuaries are facing the increasingly urgent threat of salt wedge intrusion, which impacts their ecological balance as well as human-dependent activities. Accurately predicting estuary salinity is essential for water resource management, ecosystem preservation, and for ensuring sustainable development along coastlines. In this study, we investigated the application of different machine learning and deep learning models to predict salinity levels within estuarine environments. Leveraging different techniques, including Random Forest, Least-Squares Boosting, Artificial Neural Network and Long Short-Term Memory networks, the aim was to enhance the predictive accuracy in order to better understand the complex interplay of factors influencing estuarine salinity dynamics. The Po River estuary (Po di Goro), which is one of the main hotspots of salt wedge intrusion, was selected as the study area. Comparative analyses of machine learning models with the state-of-the-art physics-based Estuary box model (EBM) and Hybrid-EBM models were conducted to assess model performances. The results highlighted an improvement in the machine learning performance, with a reduction in the RMSE (from 4.22 psu obtained by physics-based EBM to 2.80 psu obtained by LSBoost-Season) and an increase in the R2 score (from 0.67 obtained by physics-based EBM to 0.85 by LSBoost-Season), computed on the test set. We also explored the impact of different variables and their contributions to the predictive capabilities of the models. Overall, this study demonstrates the feasibility and effectiveness of ML-based approaches for estimating salinity levels due to salt wedge intrusion within estuaries. The insights obtained from this study could significantly support smart management strategies, not only in the Po River estuary, but also in other location.
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Metadata
- Type
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- Language
- en
- Landing Page
- https://doi.org/10.1016/j.acags.2024.100173
- OA Status
- gold
- Cited By
- 18
- References
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- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4399768588Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.acags.2024.100173Digital Object Identifier
- Title
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Enhancing estuary salinity prediction: A Machine Learning and Deep Learning based approachWork title
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articleOpenAlex work type
- Language
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enPrimary language
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2024Year of publication
- Publication date
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2024-06-18Full publication date if available
- Authors
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Leonardo Saccotelli, Giorgia Verri, Alessandro De Lorenzis, Carla Cherubini, Rocco Caccioppoli, Giovanni Coppini, Rosalia MagliettaList of authors in order
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https://doi.org/10.1016/j.acags.2024.100173Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.1016/j.acags.2024.100173Direct OA link when available
- Concepts
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Estuary, Deep learning, Artificial intelligence, Salinity, Computer science, Machine learning, Oceanography, GeologyTop concepts (fields/topics) attached by OpenAlex
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18Total citation count in OpenAlex
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2025: 15, 2024: 3Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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