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

6 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
Article number	9973587
ISBN (Print)	978-1-6654-6125-2
ISBN (Electronic)	978-1-6654-6124-5
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)

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

Keywords

Artificial Intelligence
High-Performance Computing
Lagrangian model
Marine pollution forecast

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10.1109/eScience55777.2022.00058

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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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author = "\{De Vita\}, \{Ciro Giuseppe\} and Gennaro Mellone and \{Di Luccio\}, Diana and Sokol Kosta and Angelo Ciaramella and Raffaele Montella",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 18th IEEE International Conference on e-Science, eScience 2022 ; Conference date: 10-10-2022 Through 14-10-2022",

year = "2022",

doi = "10.1109/eScience55777.2022.00058",

language = "English",

isbn = "978-1-6654-6125-2",

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booktitle = "2022 IEEE 18th International Conference on e-Science, eScience 2022",

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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., 9973587, IEEE (Institute of Electrical and Electronics Engineers), 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 9973587.

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

TY - ABST

T1 - AIQUAM

T2 - 18th IEEE International Conference on e-Science, eScience 2022

AU - De Vita, Ciro Giuseppe

AU - Mellone, Gennaro

AU - Di Luccio, Diana

AU - Kosta, Sokol

AU - Ciaramella, Angelo

AU - Montella, Raffaele

PY - 2022

Y1 - 2022

N2 - 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%.

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%.

KW - Artificial Intelligence

KW - High-Performance Computing

KW - Lagrangian model

KW - Marine pollution forecast

UR - https://www.scopus.com/pages/publications/85145436678

U2 - 10.1109/eScience55777.2022.00058

DO - 10.1109/eScience55777.2022.00058

M3 - Conference abstract in proceeding

AN - SCOPUS:85145436678

SN - 978-1-6654-6125-2

SP - 401

EP - 402

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

PB - IEEE (Institute of Electrical and Electronics Engineers)

Y2 - 10 October 2022 through 14 October 2022

ER -

AIQUAM: Artificial Intelligence-based water QUAlity Model

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