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
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.
BibTeX
@inproceedings{ijcai2024p809,
title = {Time-Evolving Data Science and Artificial Intelligence for Advanced Open Environmental Science (TAIAO) Programme},
author = {Koh, Yun Sing and Bifet, Albert and Bryan, Karin and Cassales, Guilherme and Graffeuille, Olivier and Lim, Nick and Mourot, Phil and Ning, Ding and Pfahringer, Bernhard and Vetrova, Varvara and Murilo Gomes, Heitor},
booktitle = {Proceedings of the Thirty-Third International Joint Conference on
Artificial Intelligence, {IJCAI-24}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
editor = {Kate Larson},
pages = {7314--7322},
year = {2024},
month = {8},
note = {AI for Good},
doi = {10.24963/ijcai.2024/809},
url = {https://doi.org/10.24963/ijcai.2024/809},
}