Time-Evolving Data Science and Artificial Intelligence for Advanced Open Environmental Science (TAIAO) Programme

Yun Sing Koh; Albert Bifet; Karin Bryan; Guilherme Cassales; Olivier Graffeuille; Nick Lim; Phil Mourot; Ding Ning; Bernhard Pfahringer; Varvara Vetrova; Heitor Gomes

Time-Evolving Data Science and Artificial Intelligence for Advanced Open Environmental Science (TAIAO) Programme

Yun Sing Koh
, Albert Bifet
, Karin Bryan
, Guilherme Cassales
, Olivier Graffeuille
, Nick Lim
, Phil Mourot
, Ding Ning
, Bernhard Pfahringer
, Varvara Vetrova
, Heitor Gomes

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

New Zealand's unique ecosystems face increasing threats from climate change, impacting biodiversity and posing challenges to safety, livelihoods, and well-being. To tackle these complex issues, advanced data science and artificial intelligence techniques can provide unique solutions. Currently in its fourth year of a seven-year program, TAIAO focuses on methods for analyzing environmental datasets. Recognizing this urgency, the open-source TAIAO platform was developed. This platform enables new artificial intelligence research for environmental data and offers an open-access repository to enhance reproducibility in the field. This paper will showcase four environmental case studies, artificial intelligence research, platform implementation details, and future development plans.

Original language	English
Title of host publication	Proceedings of the 33rd International Joint Conference on Artificial Intelligence, IJCAI 2024
Editors	Kate Larson
Publisher	International Joint Conferences on Artificial Intelligence
Pages	7314-7322
Number of pages	9
ISBN (Electronic)	9781956792041
Publication status	Published - 1 Jan 2024
Event	33rd International Joint Conference on Artificial Intelligence, IJCAI 2024 - Jeju, Korea, Republic of Duration: 3 Aug 2024 → 9 Aug 2024

Publication series

Name	IJCAI International Joint Conference on Artificial Intelligence
ISSN (Print)	1045-0823

Conference

Conference	33rd International Joint Conference on Artificial Intelligence, IJCAI 2024
Country/Territory	Korea, Republic of
City	Jeju
Period	3/08/24 → 9/08/24

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 13 Climate Action

Cite this

Koh, Y. S., Bifet, A., Bryan, K., Cassales, G., Graffeuille, O., Lim, N., Mourot, P., Ning, D., Pfahringer, B., Vetrova, V., & Gomes, H. (2024). Time-Evolving Data Science and Artificial Intelligence for Advanced Open Environmental Science (TAIAO) Programme. In K. Larson (Ed.), Proceedings of the 33rd International Joint Conference on Artificial Intelligence, IJCAI 2024 (pp. 7314-7322). (IJCAI International Joint Conference on Artificial Intelligence). International Joint Conferences on Artificial Intelligence.

Koh, Yun Sing ; Bifet, Albert ; Bryan, Karin et al. / Time-Evolving Data Science and Artificial Intelligence for Advanced Open Environmental Science (TAIAO) Programme. Proceedings of the 33rd International Joint Conference on Artificial Intelligence, IJCAI 2024. editor / Kate Larson. International Joint Conferences on Artificial Intelligence, 2024. pp. 7314-7322 (IJCAI International Joint Conference on Artificial Intelligence).

@inproceedings{1fe9a21f52b84eb4986eb85eeb5a7e55,

title = "Time-Evolving Data Science and Artificial Intelligence for Advanced Open Environmental Science (TAIAO) Programme",

abstract = "New Zealand's unique ecosystems face increasing threats from climate change, impacting biodiversity and posing challenges to safety, livelihoods, and well-being. To tackle these complex issues, advanced data science and artificial intelligence techniques can provide unique solutions. Currently in its fourth year of a seven-year program, TAIAO focuses on methods for analyzing environmental datasets. Recognizing this urgency, the open-source TAIAO platform was developed. This platform enables new artificial intelligence research for environmental data and offers an open-access repository to enhance reproducibility in the field. This paper will showcase four environmental case studies, artificial intelligence research, platform implementation details, and future development plans.",

author = "Koh, \{Yun Sing\} and Albert Bifet and Karin Bryan and Guilherme Cassales and Olivier Graffeuille and Nick Lim and Phil Mourot and Ding Ning and Bernhard Pfahringer and Varvara Vetrova and Heitor Gomes",

note = "Publisher Copyright: {\textcopyright} 2024 International Joint Conferences on Artificial Intelligence. All rights reserved.; 33rd International Joint Conference on Artificial Intelligence, IJCAI 2024 ; Conference date: 03-08-2024 Through 09-08-2024",

year = "2024",

month = jan,

day = "1",

language = "English",

series = "IJCAI International Joint Conference on Artificial Intelligence",

publisher = "International Joint Conferences on Artificial Intelligence",

pages = "7314--7322",

editor = "Kate Larson",

booktitle = "Proceedings of the 33rd International Joint Conference on Artificial Intelligence, IJCAI 2024",

}

Koh, YS, Bifet, A, Bryan, K, Cassales, G, Graffeuille, O, Lim, N, Mourot, P, Ning, D, Pfahringer, B, Vetrova, V & Gomes, H 2024, Time-Evolving Data Science and Artificial Intelligence for Advanced Open Environmental Science (TAIAO) Programme. in K Larson (ed.), Proceedings of the 33rd International Joint Conference on Artificial Intelligence, IJCAI 2024. IJCAI International Joint Conference on Artificial Intelligence, International Joint Conferences on Artificial Intelligence, pp. 7314-7322, 33rd International Joint Conference on Artificial Intelligence, IJCAI 2024, Jeju, Korea, Republic of, 3/08/24.

Time-Evolving Data Science and Artificial Intelligence for Advanced Open Environmental Science (TAIAO) Programme. / Koh, Yun Sing; Bifet, Albert; Bryan, Karin et al.
Proceedings of the 33rd International Joint Conference on Artificial Intelligence, IJCAI 2024. ed. / Kate Larson. International Joint Conferences on Artificial Intelligence, 2024. p. 7314-7322 (IJCAI International Joint Conference on Artificial Intelligence).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Time-Evolving Data Science and Artificial Intelligence for Advanced Open Environmental Science (TAIAO) Programme

AU - Koh, Yun Sing

AU - Bifet, Albert

AU - Bryan, Karin

AU - Cassales, Guilherme

AU - Graffeuille, Olivier

AU - Lim, Nick

AU - Mourot, Phil

AU - Ning, Ding

AU - Pfahringer, Bernhard

AU - Vetrova, Varvara

AU - Gomes, Heitor

PY - 2024/1/1

Y1 - 2024/1/1

N2 - New Zealand's unique ecosystems face increasing threats from climate change, impacting biodiversity and posing challenges to safety, livelihoods, and well-being. To tackle these complex issues, advanced data science and artificial intelligence techniques can provide unique solutions. Currently in its fourth year of a seven-year program, TAIAO focuses on methods for analyzing environmental datasets. Recognizing this urgency, the open-source TAIAO platform was developed. This platform enables new artificial intelligence research for environmental data and offers an open-access repository to enhance reproducibility in the field. This paper will showcase four environmental case studies, artificial intelligence research, platform implementation details, and future development plans.

AB - New Zealand's unique ecosystems face increasing threats from climate change, impacting biodiversity and posing challenges to safety, livelihoods, and well-being. To tackle these complex issues, advanced data science and artificial intelligence techniques can provide unique solutions. Currently in its fourth year of a seven-year program, TAIAO focuses on methods for analyzing environmental datasets. Recognizing this urgency, the open-source TAIAO platform was developed. This platform enables new artificial intelligence research for environmental data and offers an open-access repository to enhance reproducibility in the field. This paper will showcase four environmental case studies, artificial intelligence research, platform implementation details, and future development plans.

M3 - Conference contribution

AN - SCOPUS:85204303270

T3 - IJCAI International Joint Conference on Artificial Intelligence

SP - 7314

EP - 7322

BT - Proceedings of the 33rd International Joint Conference on Artificial Intelligence, IJCAI 2024

A2 - Larson, Kate

PB - International Joint Conferences on Artificial Intelligence

T2 - 33rd International Joint Conference on Artificial Intelligence, IJCAI 2024

Y2 - 3 August 2024 through 9 August 2024

ER -

Koh YS, Bifet A, Bryan K, Cassales G, Graffeuille O, Lim N et al. Time-Evolving Data Science and Artificial Intelligence for Advanced Open Environmental Science (TAIAO) Programme. In Larson K, editor, Proceedings of the 33rd International Joint Conference on Artificial Intelligence, IJCAI 2024. International Joint Conferences on Artificial Intelligence. 2024. p. 7314-7322. (IJCAI International Joint Conference on Artificial Intelligence).

Time-Evolving Data Science and Artificial Intelligence for Advanced Open Environmental Science (TAIAO) Programme

Abstract

Publication series

Conference

UN SDGs

Fingerprint

Cite this