research-article

Tornado Forecasting with Multiple Markov Boundaries

Authors:

David L. Small,

Shafiqul Islam,

Xindong WuAuthors Info & Claims

KDD '15: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Pages 2237 - 2246

https://doi.org/10.1145/2783258.2788612

Published: 10 August 2015 Publication History

Abstract

Reliable tornado forecasting with a long-lead time can greatly support emergency response and is of vital importance for the economy and society. The large number of meteorological variables in spatiotemporal domains and the complex relationships among variables remain the top difficulties for a long-lead tornado forecasting.

Standard data mining approaches to tackle high dimensionality are usually designed to discover a single set of features without alternating options for domain scientists to select more reliable and physical interpretable variables.

In this work, we provide a new solution to use the concept of multiple Markov boundaries in local causal discovery to identify multiple sets of the precursors for tornado forecasting. Specifically, our algorithm first confines the extremely large feature spaces to a small core feature space, then it mines multiple sets of the precursors from the core feature space that may equally contribute to tornado forecasting. With the multiple sets of the precursors, we are able to report to domain scientists the predictive but practical set of precursors.

An extensive empirical study is conducted on eight benchmark data sets and the historical tornado data near Oklahoma City, OK in the United States. Experimental results show that the tornado precursors we identified can help to improve the reliability of long-lead time catastrophic tornado forecasting.

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Cited By

Wu XJiang BZhong YChen H(2023)Multi-Target Markov Boundary Discovery: Theory, Algorithm, and ApplicationIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2022.319978445:4(4964-4980)Online publication date: 1-Apr-2023
https://doi.org/10.1109/TPAMI.2022.3199784
Cheng QOberhänsli RZhao M(2020)A new international initiative for facilitating data-driven Earth science transformationGeological Society, London, Special Publications10.1144/SP499-2019-158499:1(225-240)Online publication date: 14-May-2020
https://doi.org/10.1144/SP499-2019-158
Karpatne AEbert-Uphoff IRavela SBabaie HKumar V(2019)Machine Learning for the GeosciencesIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2018.286100631:8(1544-1554)Online publication date: 1-Aug-2019
https://dl.acm.org/doi/10.1109/TKDE.2018.2861006
Show More Cited By

Index Terms

Tornado Forecasting with Multiple Markov Boundaries
1. Computing methodologies
  1. Machine learning

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Published In

cover image ACM Conferences

KDD '15: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 2015

2378 pages

ISBN:9781450336642

DOI:10.1145/2783258

General Chairs:
Longbing Cao
University of Technology, Sydney
,
Chengqi Zhang
University of Technology, Sydney
,
Program Chairs:
Thorsten Joachims
Cornell University
,
Geoff Webb
Monash University
,
Dragos D. Margineantu
Boeing Research
,
Graham Williams
Australian Taxation Office

Copyright © 2015 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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New York, NY, United States

Publication History

Published: 10 August 2015

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Funding Sources

National 973 Program of China
The Pacific Institute for the Mathematical Sciences Canada
National Natural Science Foundation of China
NSERC
TBCIC NRAS
PCSIRT, Ministry of Education, China

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KDD '15

Sponsor:

KDD '15: The 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 10 - 13, 2015

NSW, Sydney, Australia

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KDD '15 Paper Acceptance Rate 160 of 819 submissions, 20%;

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

Wu XJiang BZhong YChen H(2023)Multi-Target Markov Boundary Discovery: Theory, Algorithm, and ApplicationIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2022.319978445:4(4964-4980)Online publication date: 1-Apr-2023
https://doi.org/10.1109/TPAMI.2022.3199784
Cheng QOberhänsli RZhao M(2020)A new international initiative for facilitating data-driven Earth science transformationGeological Society, London, Special Publications10.1144/SP499-2019-158499:1(225-240)Online publication date: 14-May-2020
https://doi.org/10.1144/SP499-2019-158
Karpatne AEbert-Uphoff IRavela SBabaie HKumar V(2019)Machine Learning for the GeosciencesIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2018.286100631:8(1544-1554)Online publication date: 1-Aug-2019
https://dl.acm.org/doi/10.1109/TKDE.2018.2861006
Javidi MEskandari S(2019)Online streaming feature selectionPattern Analysis & Applications10.1007/s10044-018-0690-722:3(949-963)Online publication date: 1-Aug-2019
https://dl.acm.org/doi/10.1007/s10044-018-0690-7
Wen XLin YPelechrinis K(2018)Event Analytics via Discriminant Tensor FactorizationACM Transactions on Knowledge Discovery from Data10.1145/318445512:6(1-38)Online publication date: 10-Oct-2018
https://dl.acm.org/doi/10.1145/3184455
Yu KWu XDing WPei J(2016)Scalable and Accurate Online Feature Selection for Big DataACM Transactions on Knowledge Discovery from Data10.1145/297674411:2(1-39)Online publication date: 3-Dec-2016
https://dl.acm.org/doi/10.1145/2976744

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