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

Publikation: Bidrag til bog/antologi/rapport/konference proceeding › Konferenceabstrakt i proceeding › Forskning › peer review

1 Citationer (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%.

Originalsprog	Engelsk
Titel	2022 IEEE 18th International Conference on e-Science, eScience 2022
Antal sider	2
Forlag	IEEE
Publikationsdato	2022
Sider	401-402
ISBN (Elektronisk)	9781665461245
DOI	https://doi.org/10.1109/eScience55777.2022.00058
Status	Udgivet - 2022
Begivenhed	18th IEEE International Conference on e-Science, eScience 2022 - Salt Lake City, USA Varighed: 10 okt. 2022 → 14 okt. 2022

Konference

Konference	18th IEEE International Conference on e-Science, eScience 2022
Land/Område	USA
By	Salt Lake City
Periode	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)

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

Bibliografisk note

Publisher Copyright:
© 2022 IEEE.

Adgang til dokumentet

10.1109/eScience55777.2022.00058

AUB Link

Søg efter materialet i Aalborg Universitetsbiblioteks søgemaskine

Andre filer og links

Link to publication in Scopus

Citationsformater

@inbook{a0c23a2d0f4641c387bfc6363d6c15d3,

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%.",

keywords = "Artificial Intelligence, High-Performance Computing, Lagrangian model, Marine pollution forecast",

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

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: {\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",

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

pages = "401--402",

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

publisher = "IEEE",

address = "United States",

}

De Vita, CG, Mellone, G, Di Luccio, D, Kosta, S, Ciaramella, A & Montella, R 2022, AIQUAM: Artificial Intelligence-based water QUAlity Model. i 2022 IEEE 18th International Conference on e-Science, eScience 2022. IEEE, Proceedings - 2022 IEEE 18th International Conference on e-Science, eScience 2022, s. 401-402, 18th IEEE International Conference on e-Science, eScience 2022, Salt Lake City, USA, 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, 2022. s. 401-402 (Proceedings - 2022 IEEE 18th International Conference on e-Science, eScience 2022).

Publikation: Bidrag til bog/antologi/rapport/konference proceeding › Konferenceabstrakt i proceeding › Forskning › 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

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.

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 - http://www.scopus.com/inward/record.url?scp=85145436678&partnerID=8YFLogxK

U2 - 10.1109/eScience55777.2022.00058

DO - 10.1109/eScience55777.2022.00058

M3 - Conference abstract in proceeding

AN - SCOPUS:85145436678

T3 - Proceedings - 2022 IEEE 18th International Conference on e-Science, eScience 2022

SP - 401

EP - 402

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

PB - IEEE

Y2 - 10 October 2022 through 14 October 2022

ER -

AIQUAM: Artificial Intelligence-based water QUAlity Model

Abstract

Konference

Bibliografisk note

Adgang til dokumentet

AUB Link

Andre filer og links

Fingeraftryk

Citationsformater