{"type":"ScholarlyArticle","title":"Time-Evolving Data Science and Artificial Intelligence for Advanced Open Environmental Science (TAIAO) Programme","authors":[{"name":"Yun Sing Koh","slug":"yun-sing-koh","given":"Yun Sing","family":"Koh","affiliation":"University of Auckland , New Zealand"},{"name":"Albert Bifet","slug":"albert-bifet","given":"Albert","family":"Bifet","affiliation":"AI Institute , University of Waikato , New Zealand; LTCI , Télécom Paris , IP Paris , France"},{"name":"Karin Bryan","slug":"karin-bryan","given":"Karin","family":"Bryan","affiliation":"University of Auckland , New Zealand"},{"name":"Guilherme Cassales","slug":"guilherme-cassales","given":"Guilherme","family":"Cassales","affiliation":"AI Institute , University of Waikato , New Zealand"},{"name":"Olivier Graffeuille","slug":"olivier-graffeuille","given":"Olivier","family":"Graffeuille","affiliation":"University of Auckland , New Zealand"},{"name":"Nick Lim","slug":"nick-lim","given":"Nick","family":"Lim","affiliation":"AI Institute , University of Waikato , New Zealand"},{"name":"Phil Mourot","slug":"phil-mourot","given":"Phil","family":"Mourot","affiliation":"AI Institute , University of Waikato , New Zealand; Waikato Regional Council , New Zealand"},{"name":"Ding Ning","slug":"ding-ning","given":"Ding","family":"Ning","affiliation":"University of Canterbury , New Zealand"},{"name":"Bernhard Pfahringer","slug":"bernhard-pfahringer","given":"Bernhard","family":"Pfahringer","affiliation":"AI Institute , University of Waikato , New Zealand"},{"name":"Varvara Vetrova","slug":"varvara-vetrova","given":"Varvara","family":"Vetrova","affiliation":"University of Canterbury , New Zealand"},{"name":"Heitor Murilo Gomes","slug":"heitor-murilo-gomes","given":"Heitor Murilo","family":"Gomes","affiliation":"Victoria University of Wellington , New Zealand"}],"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.","doi":"10.24963/ijcai.2024/809","url":"https://ijcai.org/proceedings/2024/809","pdf":"https://ijcai.org/proceedings/2024/0809.pdf","markdown":"https://ijcai.org/proceedings/2024/809.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/809","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"AI for Good","track":"AI for Good","number":809,"pages":{"first":7314,"last":7322},"keywords":["Machine Learning: General","Multidisciplinary Topics and Applications: General"],"videos":[],"references":[{"position":1,"raw":"[Bar-On et al., 2018] Yinon M. Bar-On, Rob Phillips, and Ron Milo. The biomass distribution on earth. Proceedings of the National Academy of Sciences, 115(25):6506-6511, 2018.","title":"The biomass distribution on earth","authors":"M Yinon, Rob Bar-On, Ron Phillips, Milo","year":"2018","venue":"Proceedings of the National Academy of Sciences","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Bennett et al., 2009] Elena M. Bennett, Garry D. Peter- son, and Line J. Gordon. Understanding relationships among multiple ecosystem services. Ecology Letters, 12(12):1394-1404, 2009.","title":"Understanding relationships among multiple ecosystem services","authors":"Elena M Bennett, Garry D Peterson, Line J Gordon","year":"2009","venue":"Ecology Letters","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Bonan, 2016] Gordon B. Bonan. Forests, climate, and pub- lic policy: A 500-year interdisciplinary odyssey. Annual Review of Ecology, Evolution, and Systematics, 47(1):97- 121, 2016.","title":"Forests, climate, and public policy: A 500-year interdisciplinary odyssey","authors":"Gordon B Bonan","year":"2016","venue":"Annual Review of Ecology, Evolution, and Systematics","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Cachay et al., 2023] Salva Rühling Cachay, Bo Zhao, Hai- ley James, and Rose Yu. Dyffusion: A dynamics-informed diffusion model for spatiotemporal forecasting. arXiv preprint arXiv:2306.01984, 2023.","title":"Dyffusion: A dynamics-informed diffusion model for spatiotemporal forecasting","authors":"Bo Salva Rühling Cachay, Hailey Zhao, Rose James, Yu","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Cattoën et al., 2022] Céline Cattoën, Jono Conway, Nava Fedaeff, Daniel Lagrava, Paula Blackett, Kelsey Mont- gomery, Ude Shankar, Trevor Carey-Smith, Stuart Moore, Andrea Mari, et al. A national flood awareness system for ungauged catchments in complex topography: The case of development, communication and evaluation in new zealand. Journal of Flood Risk Management, page e12864, 2022.","title":"A national flood awareness system for ungauged catchments in complex topography: The case of development, communication and evaluation in new zealand","authors":"Céline Cattoën, Jono Conway, Nava Fedaeff, Daniel Lagrava, Paula Blackett, Kelsey Montgomery, Ude Shankar, Trevor Carey-Smith, Stuart Moore, Andrea Mari","year":"2022","venue":"Journal of Flood Risk Management","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Defferrard et al., 2019] Michaël Defferrard, Nathanaël Per- raudin, Tomasz Kacprzak, and Raphael Sgier. Deep- Sphere: towards an equivariant graph-based spherical CNN. arXiv preprint arXiv:1904.05146, 2019.","title":"Deep-Sphere: towards an equivariant graph-based spherical CNN","authors":"Michaël Defferrard, Nathanaël Perraudin, Tomasz Kacprzak, Raphael Sgier","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Douville et al., 2022] Hervé Douville, Saïd Qasmi, Aurélien Ribes, and Olivier Bock. Global warming at near-constant tropospheric relative humidity is supported by observations. Communications Earth & Environment, 3(1):237, 2022.","title":"Global warming at near-constant tropospheric relative humidity is supported by observations","authors":"Hervé Douville, Saïd Qasmi, Aurélien Ribes, Olivier Bock","year":"2022","venue":"Communications Earth & Environment","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Ferro and Stephenson, 2011] Christopher AT Ferro and David B Stephenson. Extremal dependence indices: Im- proved verification measures for deterministic forecasts of rare binary events. Weather and Forecasting, 26(5):699- 713, 2011.","title":"Extremal dependence indices: Improved verification measures for deterministic forecasts of rare binary events","authors":"A T Christopher, David B Ferro, Stephenson","year":"2011","venue":"Weather and Forecasting","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Graffeuille et al., 2022] Olivier Graffeuille, Yun Sing Koh, Jörg Wicker, and Moritz K Lehmann. Semi-supervised conditional density estimation with wasserstein laplacian regularisation. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 36, pages 6746-6754, 2022.","title":"Semi-supervised conditional density estimation with wasserstein laplacian regularisation","authors":"Olivier Graffeuille, Yun Sing Koh, Jörg Wicker, Moritz K Lehmann","year":"2022","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Hamilton et al., 2017] Will Hamilton, Zhitao Ying, and Jure Leskovec. Inductive representation learning on large graphs. In Proceedings of the 30th Advances in Neural Information Processing Systems, 2017.","title":"Inductive representation learning on large graphs","authors":"Hamilton","year":"2017","venue":"Proceedings of the 30th Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Hersbach et al., 2020] Hans Hersbach, Bill Bell, Paul Berrisford, Shoji Hirahara, András Horányi, Joaquín Muñoz-Sabater, Julien Nicolas, Carole Peubey, Raluca Radu, Dinand Schepers, et al. The ERA5 global reanal- ysis. Quarterly Journal of the Royal Meteorological Soci- ety, 146(730):1999-2049, 2020.","title":"The ERA5 global reanalysis","authors":"Hans Hersbach, Bill Bell, Paul Berrisford, Shoji Hirahara, András Horányi, Joaquín Muñoz-Sabater, Julien Nicolas, Carole Peubey, Raluca Radu, Dinand Schepers","year":"2020","venue":"Quarterly Journal of the Royal Meteorological Society","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Hobday et al., 2016] Alistair J Hobday, Lisa V Alexan- der, Sarah E Perkins, Dan A Smale, Sandra C Straub, Eric CJ Oliver, Jessica A Benthuysen, Michael T Bur- rows, Markus G Donat, Ming Feng, et al. A hierarchi- cal approach to defining marine heatwaves. Progress in Oceanography, 141:227-238, 2016.","title":"A hierarchical approach to defining marine heatwaves","authors":"J Alistair, Lisa V Hobday, Sarah E Alexander, Dan A Perkins, Sandra C Smale, Eric Cj Straub, Jessica A Oliver, Michael T Benthuysen, Markus G Burrows, Ming Donat, Feng","year":"2016","venue":"Progress in Oceanography","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Hobday et al., 2018] Alistair J Hobday, Claire M Spillman, J Paige Eveson, Jason R Hartog, Xuebin Zhang, and Stephanie Brodie. A framework for combining seasonal forecasts and climate projections to aid risk management for fisheries and aquaculture. Frontiers in Marine Science, page 137, 2018.","title":"A framework for combining seasonal forecasts and climate projections to aid risk management for fisheries and aquaculture","authors":"J Alistair, Claire M Hobday, J Spillman, Jason R Paige Eveson, Xuebin Hartog, Stephanie Zhang, Brodie","year":"2018","venue":"Frontiers in Marine Science","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Holbrook et al., 2020] Neil J Holbrook, Alex Sen Gupta, Eric CJ Oliver, Alistair J Hobday, Jessica A Benthuysen, Hillary A Scannell, Dan A Smale, and Thomas Wernberg. Keeping pace with marine heatwaves. Nature Reviews Earth & Environment, 1(9):482-493, 2020.","title":"Keeping pace with marine heatwaves","authors":"J Neil, Alex Sen Holbrook, Eric Cj Gupta, Alistair J Oliver, Jessica A Hobday, Hillary A Benthuysen, Dan A Scannell, Thomas Smale, Wernberg","year":"2020","venue":"Nature Reviews Earth & Environment","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[ICNZ, 2023] ICNZ. New Zealand Insurance Council: Cost of disaster events in New Zealand. https://www.icnz.org. nz/industry/cost-of-natural-disasters/, 2023. Accessed: 2024-06-06.","title":"ICNZ. New Zealand Insurance Council: Cost of disaster events in New Zealand","authors":"Icnz","year":"2023","venue":null,"doi":null,"url":"https://www.icnz.org.nz/industry/cost-of-natural-disasters/,2023","internal_url":null},{"position":16,"raw":"[Jacox et al., 2022] Michael G Jacox, Michael A Alexander, Dillon Amaya, Emily Becker, Steven J Bograd, Stephanie Brodie, Elliott L Hazen, Mercedes Pozo Buil, and Desiree Tommasi. Global seasonal forecasts of marine heatwaves. Nature, 604(7906):486-490, 2022.","title":"Global seasonal forecasts of marine heatwaves","authors":"Michael A Michael G Jacox, Dillon Alexander, Emily Amaya, Steven J Becker, Stephanie Bograd, Elliott L Brodie, Mercedes Pozo Hazen, Desiree Buil, Tommasi","year":"2022","venue":"Nature","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Jia et al., 2021] Yunzhe Jia, Eibe Frank, Bernhard Pfahringer, Albert Bifet, and Nick Lim. Studying and exploiting the relationship between model accuracy and explanation quality. In Machine Learning and Knowledge Discovery in Databases. Research Track: European Conference, ECML PKDD 2021, Bilbao, Spain, September 13-17, 2021, Proceedings, Part II 21, pages 699-714. Springer, 2021.","title":"Studying and exploiting the relationship between model accuracy and explanation quality","authors":"Jia","year":"2021","venue":"Machine Learning and Knowledge Discovery in Databases. Research Track: European Conference, ECML PKDD 2021","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Keenan and Williams, 2018] T.F. Keenan and C.A. Williams. The terrestrial carbon sink. Annual Re- view of Environment and Resources, 43(1):219-243, 2018.","title":"The terrestrial carbon sink","authors":"T F Keenan, C A Williams","year":"2018","venue":"Annual Review of Environment and Resources","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Kratzert et al., 2019] Frederik Kratzert, Daniel Klotz, Guy Shalev, Günter Klambauer, Sepp Hochreiter, and Grey Nearing. Towards learning universal, regional, and lo- cal hydrological behaviors via machine learning applied to large-sample datasets. Hydrology and Earth System Sci- ences, 23(12):5089-5110, 2019.","title":"Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets","authors":"Frederik Kratzert, Daniel Klotz, Guy Shalev, Günter Klambauer, Sepp Hochreiter, Grey Nearing","year":"2019","venue":"Hydrology and Earth System Sciences","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Lim et al., 2023] Nick Lim, Albert Bifet, Daniel Bull, Eibe Frank, Yunzhe Jia, Jacob Montiel, and Bernhard Pfahringer. Showcasing the taiao project: providing resources for machine learning from images of new zealand's natural environment. Journal of the Royal So- ciety of New Zealand, 53(1):69-81, 2023.","title":"Showcasing the taiao project: providing resources for machine learning from images of new zealand's natural environment","authors":"Nick Lim, Albert Bifet, Daniel Bull, Eibe Frank, Yunzhe Jia, Jacob Montiel, Bernhard Pfahringer","year":"2023","venue":"Journal of the Royal Society of New Zealand","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Luo et al., 2016] Wenjie Luo, Yujia Li, Raquel Urtasun, and Richard Zemel. Understanding the effective receptive field in deep convolutional neural networks. Advances in neural information processing systems, 29, 2016.","title":"Understanding the effective receptive field in deep convolutional neural networks","authors":"Wenjie Luo, Yujia Li, Raquel Urtasun, Richard Zemel","year":"2016","venue":"Advances in neural information processing systems","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Milz et al., 2023] Tobias Milz, Marte Hofsteenge, Marwan Katurji, and Varvara Vetrova. Foehn wind analysis using unsupervised deep anomaly detection. In EGU General Assembly Conference Abstracts, pages EGU-10256, 2023.","title":"Foehn wind analysis using unsupervised deep anomaly detection","authors":"Milz","year":"2023","venue":"EGU General Assembly Conference Abstracts","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Mosavi et al., 2018] Amir Mosavi, Pinar Ozturk, and Kwok-wing Chau. Flood prediction using machine learn- ing models: Literature review. Water, 10(11):1536, 2018.","title":"Flood prediction using machine learning models: Literature review","authors":"Amir Mosavi, Pinar Ozturk, Kwok-Wing Chau","year":"2018","venue":"Water","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Mourot et al., 2022] Phil Mourot, Nick Lim, Bernhard Pfahringer, and Albert Bifet. A regional flood impact prediction tool using machine learning to manage flood risk in real-time. a case study in new zealand. In EGU General Assembly Conference Abstracts, pages EGU22- 12455, 2022.","title":"A regional flood impact prediction tool using machine learning to manage flood risk in real-time. a case study in new zealand","authors":"Mourot","year":"2022","venue":"EGU General Assembly Conference Abstracts","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Ning et al., 2023] Ding Ning, Varvara Vetrova, and Karin R Bryan. Graph-based deep learning for sea surface temper- ature forecasts. arXiv preprint arXiv:2305.09468, 2023.","title":"Graph-based deep learning for sea surface temperature forecasts","authors":"Ding Ning, Varvara Vetrova, Karin R Bryan","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Ogden, 1995] John Ogden. The long-term conservation of forest diversity in new zealand. Pacific Conservation Biol- ogy, 2(1):77-90, 1995.","title":"The long-term conservation of forest diversity in new zealand","authors":"John Ogden, Ogden","year":"1995","venue":"Pacific Conservation Biology","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Oliver et al., 2019] Eric CJ Oliver, Michael T Burrows, Markus G Donat, Alex Sen Gupta, Lisa V Alexander, Sarah E Perkins-Kirkpatrick, Jessica A Benthuysen, Al- istair J Hobday, Neil J Holbrook, Pippa J Moore, et al. Projected marine heatwaves in the 21st century and the po- tential for ecological impact. Frontiers in Marine Science, 6:734, 2019.","title":"Projected marine heatwaves in the 21st century and the potential for ecological impact","authors":"Eric Cj Oliver, Michael T Burrows, Markus G Donat, Alex Sen Gupta, Lisa V Alexander, Sarah E Perkins-Kirkpatrick, Jessica A Benthuysen, J Alistair, Neil J Hobday, Pippa J Holbrook, Moore","year":"2019","venue":"Frontiers in Marine Science","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Oliver et al., 2021] Eric CJ Oliver, Jessica A Benthuysen, Sofia Darmaraki, Markus G Donat, Alistair J Hobday, Neil J Holbrook, Robert W Schlegel, and Alex Sen Gupta. Marine heatwaves. Annual Review of Marine Science, 13:313-342, 2021.","title":"Marine heatwaves","authors":"Eric Cj Oliver, Jessica A Benthuysen, Sofia Darmaraki, Markus G Donat, J Alistair, Neil J Hobday, Robert W Holbrook, Alex Sen Schlegel, Gupta","year":"2021","venue":"Annual Review of Marine Science","doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Pörtner et al., 2023] Hans Pörtner, Debra C Roberts, Camille Parmesan, Helen Adams, Ibidun Adelekan, Carolina Adler, Rita Adrian, Paulina Aldunce, Elham Ali, Rawshan Ara, et al. IPCC 2022: Technical Summary, Working Group II Impacts, Adaptation and Vulnerability. PhD thesis, Intergovernmental Panel on Climate Change, 2023.","title":"Technical Summary, Working Group II Impacts, Adaptation and Vulnerability","authors":"Hans Pörtner, Debra C Roberts, Camille Parmesan, Helen Adams, Ibidun Adelekan, Carolina Adler, Rita Adrian, Paulina Aldunce, Elham Ali, Rawshan Ara","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Potter et al., 2021] Sally Potter, Sara Harrison, and Peter Kreft. The benefits and challenges of implementing impact-based severe weather warning systems: Perspec- tives of weather, flood, and emergency management per- sonnel. Weather, climate, and society, 13(2):303-314, 2021.","title":"The benefits and challenges of implementing impact-based severe weather warning systems: Perspectives of weather, flood, and emergency management personnel","authors":"Sally Potter, Sara Harrison, Peter Kreft","year":"2021","venue":"Weather, climate, and society","doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Ren et al., 2022] Jiawei Ren, Mingyuan Zhang, Cunjun Yu, and Ziwei Liu. Balanced mse for imbalanced visual re- gression. arXiv preprint arXiv:2203.16427, 2022.","title":"Balanced mse for imbalanced visual regression","authors":"Mingyuan Jiawei Ren, Cunjun Zhang, Ziwei Yu, Liu","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Schaefer, 1990] Joseph T Schaefer. The critical success in- dex as an indicator of warning skill. Weather and forecast- ing, 5(4):570-575, 1990.","title":"The critical success index as an indicator of warning skill","authors":"T Joseph, Schaefer","year":"1990","venue":"Weather and forecasting","doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Seddon, 2022] Nathalie Seddon. Harnessing the potential of nature-based solutions for mitigating and adapting to climate change. Science, 376(6600):1410-1416, 2022.","title":"Harnessing the potential of nature-based solutions for mitigating and adapting to climate change","authors":"Nathalie Seddon","year":"2022","venue":"Science","doi":null,"url":null,"internal_url":null},{"position":34,"raw":"[United Nations, 2015] United Nations. Transforming our world: the 2030 agenda for sustainable development, 2015.","title":"United Nations. Transforming our world: the 2030 agenda for sustainable development","authors":null,"year":"2015","venue":null,"doi":null,"url":null,"internal_url":null},{"position":35,"raw":"[Yang et al., 2021] Yuzhe Yang, Kaiwen Zha, Yingcong Chen, Hao Wang, and Dina Katabi. Delving into deep imbalanced regression. In Proceedings of the 38th Inter- national Conference on Machine Learning, pages 11842- 11851. PMLR, 2021.","title":"Delving into deep imbalanced regression","authors":"Yuzhe Yang, Kaiwen Zha, Yingcong Chen, Hao Wang, Dina Katabi","year":"2021","venue":"Proceedings of the 38th International Conference on Machine Learning","doi":null,"url":null,"internal_url":null},{"position":36,"raw":"[Zhang et al., 2022] Yaqian Zhang, Bernhard Pfahringer, Eibe Frank, Albert Bifet, Nick Jin Sean Lim, and Yun- zhe Jia. A simple but strong baseline for online contin- ual learning: Repeated augmented rehearsal. Advances in Neural Information Processing Systems, 35:14771-14783, 2022.","title":"A simple but strong baseline for online continual learning: Repeated augmented rehearsal","authors":"Zhang","year":"2022","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}