Big data in climate: Opportunities and challenges for machine learning

Anuj Karpatne, Vipin Kumar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

The climate and Earth sciences have recently undergone a rapid transformation from a data-poor to a data-rich environment. In particular, massive amount of data about Earth and its environment is now continuously being generated by a large number of Earth observing satellites as well as physics-based earth system models running on large-scale computational platforms. These massive and information-rich datasets offer huge potential for understanding how the Earth's climate and ecosystem have been changing and how they are being impacted by humans actions. We discuss the challenges involved in analyzing these massive data sets as well as opportunities they present for both advancing machine learning as well as the science of climate change.

Original languageEnglish (US)
Title of host publicationKDD 2017 - Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
PublisherAssociation for Computing Machinery
Pages21-22
Number of pages2
ISBN (Electronic)9781450348874
DOIs
StatePublished - Aug 13 2017
Event23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2017 - Halifax, Canada
Duration: Aug 13 2017Aug 17 2017

Publication series

NameProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
VolumePart F129685

Other

Other23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2017
CountryCanada
CityHalifax
Period8/13/178/17/17

Keywords

  • Climate science
  • Earth observation data
  • Machine learning

Fingerprint Dive into the research topics of 'Big data in climate: Opportunities and challenges for machine learning'. Together they form a unique fingerprint.

  • Cite this

    Karpatne, A., & Kumar, V. (2017). Big data in climate: Opportunities and challenges for machine learning. In KDD 2017 - Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 21-22). (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; Vol. Part F129685). Association for Computing Machinery. https://doi.org/10.1145/3097983.3105810