AIQUAM: Artificial Intelligence-based water QUAlity Model

Ciro Giuseppe De Vita; Gennaro Mellone; Diana Di Luccio; Sokol Kosta; Angelo Ciaramella; Raffaele Montella

doi:10.1109/eScience55777.2022.00058

AIQUAM: Artificial Intelligence-based water QUAlity Model

Ciro Giuseppe De Vita, Gennaro Mellone, Diana Di Luccio, Sokol Kosta, Angelo Ciaramella, Raffaele Montella

Research output: Contribution to book/anthology/report/conference proceeding › Conference abstract in proceeding › Research › peer-review

3 Citations (Scopus)

Abstract

Monitoring the impact of the pollutants on the sea is a crucial issue for coastal human activities, such as aquaculture. However, leveraging a continuous microbiological laboratory analysis is unfeasible for costs and practical reasons. Here we present a novel methodology finalized to predict water quality as categorized indexes leveraging an integrated approach between computational components and artificial intelligence techniques. As a paradigm demonstrator, we couple WaComM++ with AIQUAM. The use case presented is an application of AIQUAM in the Bay of Naples (Campania Region, Italy) for predicting bacteria contaminants in mussel farms. The results are encouraging as the model reached a correct prediction rate of 93%.

Original language	English
Title of host publication	2022 IEEE 18th International Conference on e-Science, eScience 2022
Number of pages	2
Publisher	IEEE (Institute of Electrical and Electronics Engineers)
Publication date	2022
Pages	401-402
ISBN (Electronic)	9781665461245
DOIs	https://doi.org/10.1109/eScience55777.2022.00058
Publication status	Published - 2022
Event	18th IEEE International Conference on e-Science, eScience 2022 - Salt Lake City, United States Duration: 10 Oct 2022 → 14 Oct 2022

Conference

Conference	18th IEEE International Conference on e-Science, eScience 2022
Country/Territory	United States
City	Salt Lake City
Period	10/10/2022 → 14/10/2022
Sponsor	IEEE, IEEE Computer Society, IEEE Technical Committee on Parallel Processing (TCPP), IEEE�s Technical Committee on High Performance Computing (TCHPC)

Series	Proceedings - 2022 IEEE 18th International Conference on e-Science, eScience 2022

Bibliographical note

Funding Information:
The activities are in the framework of the MytilAI (Modelling mytilus farming with Artificial Intelligence technologies) and the H2020 EuroHPC+ ADMIRE (Adaptive multi-tier intelligent data manager for Exascale) research projects.

Publisher Copyright:
© 2022 IEEE.

Keywords

Artificial Intelligence
High-Performance Computing
Lagrangian model
Marine pollution forecast

Access to Document

10.1109/eScience55777.2022.00058

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Cite this

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title = "AIQUAM: Artificial Intelligence-based water QUAlity Model",

abstract = "Monitoring the impact of the pollutants on the sea is a crucial issue for coastal human activities, such as aquaculture. However, leveraging a continuous microbiological laboratory analysis is unfeasible for costs and practical reasons. Here we present a novel methodology finalized to predict water quality as categorized indexes leveraging an integrated approach between computational components and artificial intelligence techniques. As a paradigm demonstrator, we couple WaComM++ with AIQUAM. The use case presented is an application of AIQUAM in the Bay of Naples (Campania Region, Italy) for predicting bacteria contaminants in mussel farms. The results are encouraging as the model reached a correct prediction rate of 93%.",

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De Vita, CG, Mellone, G, Di Luccio, D, Kosta, S, Ciaramella, A & Montella, R 2022, AIQUAM: Artificial Intelligence-based water QUAlity Model. in 2022 IEEE 18th International Conference on e-Science, eScience 2022. IEEE (Institute of Electrical and Electronics Engineers), Proceedings - 2022 IEEE 18th International Conference on e-Science, eScience 2022, pp. 401-402, 18th IEEE International Conference on e-Science, eScience 2022, Salt Lake City, United States, 10/10/2022. https://doi.org/10.1109/eScience55777.2022.00058

AIQUAM: Artificial Intelligence-based water QUAlity Model. / De Vita, Ciro Giuseppe; Mellone, Gennaro; Di Luccio, Diana et al.
2022 IEEE 18th International Conference on e-Science, eScience 2022. IEEE (Institute of Electrical and Electronics Engineers), 2022. p. 401-402 (Proceedings - 2022 IEEE 18th International Conference on e-Science, eScience 2022).

Research output: Contribution to book/anthology/report/conference proceeding › Conference abstract in proceeding › Research › peer-review

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AU - De Vita, Ciro Giuseppe

AU - Mellone, Gennaro

AU - Di Luccio, Diana

AU - Kosta, Sokol

AU - Ciaramella, Angelo

AU - Montella, Raffaele

N1 - Funding Information: The activities are in the framework of the MytilAI (Modelling mytilus farming with Artificial Intelligence technologies) and the H2020 EuroHPC+ ADMIRE (Adaptive multi-tier intelligent data manager for Exascale) research projects. Publisher Copyright: © 2022 IEEE.

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AB - Monitoring the impact of the pollutants on the sea is a crucial issue for coastal human activities, such as aquaculture. However, leveraging a continuous microbiological laboratory analysis is unfeasible for costs and practical reasons. Here we present a novel methodology finalized to predict water quality as categorized indexes leveraging an integrated approach between computational components and artificial intelligence techniques. As a paradigm demonstrator, we couple WaComM++ with AIQUAM. The use case presented is an application of AIQUAM in the Bay of Naples (Campania Region, Italy) for predicting bacteria contaminants in mussel farms. The results are encouraging as the model reached a correct prediction rate of 93%.

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KW - Lagrangian model

KW - Marine pollution forecast

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De Vita CG, Mellone G, Di Luccio D, Kosta S, Ciaramella A, Montella R. AIQUAM: Artificial Intelligence-based water QUAlity Model. In 2022 IEEE 18th International Conference on e-Science, eScience 2022. IEEE (Institute of Electrical and Electronics Engineers). 2022. p. 401-402. (Proceedings - 2022 IEEE 18th International Conference on e-Science, eScience 2022). doi: 10.1109/eScience55777.2022.00058

AIQUAM: Artificial Intelligence-based water QUAlity Model

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