research-article

Distance Metric Learning Approach for Weather Data Mining

Authors:

Linxiu JiangAuthors Info & Claims

BDIOT '18: Proceedings of the 2018 2nd International Conference on Big Data and Internet of Things

Pages 33 - 36

https://doi.org/10.1145/3289430.3289455

Published: 24 October 2018 Publication History

Abstract

High throughput weather data, which is acquired by remote sensing technology, collected by local weather stations, or gathered by autonomous sensors, is the foundation for modern weather forecast and climate change prediction. Such data set often contains multi-dimensional information on aspects such as temperature, humidity, wind speed/direction, atmospheric pressure, etc., which can be extremely large-scale and convoluted. Therefore, effective and efficient methods for weather data analysis is important and urgently needed. Data mining technologies, which is the computer-aided process that digs useful patterns out of large-scale data sets, is widely acknowledged as a very promising direction in weather data analysis. In this paper, a novel methodology is described, which applies a distance metric learning approach for weather data mining. Such a method is applied to weather data set collected at JFK, MCO and SFO airport in 2016, and shows a very promising advantage in classification accuracy compared with other conventional methods.

References

[1]

Moulds, J. Food Price Crisis Feared As Erratic Weather Wreaks Havoc On Crops. Available online: http://www.guardian.co.uk/environment/2012/jul/22/food-price-crisis-weather-crops

[2]

Adams, Richard M., et al. "Global climate change and US agriculture." Nature 345.6272 (1990): 219.

[3]

Rotter, R.P.; Carter, T.R.; Olesen, J.E.; Porter, J.R. Crop-climate models need an overhaul. Nat. Clim. Chang. 2011, 1, 175--177.

[4]

Shanmuganathan S, Sallis P. Data mining methods to generate severe wind gust models. Atmosphere. 2014 Jan 13;5(1):60--80.

[5]

Wang X, Mortazawi A. A self-sensing AM frequency electromagnetic energy scavenger. InMicrowave Symposium Digest (IMS), 2013 IEEE MTT-S International 2013 Jun 2 (pp. 1--3). IEEE.

[6]

Valavanis KP, editor. Advances in unmanned aerial vehicles: state of the art and the road to autonomy. Springer Science & Business Media; 2008 Feb 26.

Digital Library

[7]

Li, Chuan, Wenfeng Qian, Calum J. Maclean, and Jianzhi Zhang. "The fitness landscape of a tRNA gene." Science 352, no. 6287 (2016): 837--840.

[8]

Web information: An introduction to Data Mining. Available from http://www.thearling.com/text/dmwhite/dmwhite.htm

[9]

Xingjian, S. H. I., Zhourong Chen, Hao Wang, Dit-Yan Yeung, Wai-Kin Wong, and Wang-chun Woo. "Convolutional LSTM network: A machine learning approach for precipitation nowcasting." In Advances in Neural Information Processing Systems, pp. 802--810. 2015.

Digital Library

[10]

Subana Shanmuganathan and Philip Sallis, "Data Mining Methods to Generate Severe Wind Gust Models", 5, 60--80, Atmosphere 2014.

[11]

Kavita Pabreja, "Clustering technique to interpret Numerical Weather Prediction output products for forecast of Cloudburst", International Journal of Computer Science and Information Technologies (IJCSIT), Vol. 3 (1), 2996--2999, 2012.

[12]

Shi, Haibo, Yong Luo, Chao Xu, Yonggang Wen, and Cooperative Medianet Innovation Center. "Manifold Regularized Transfer Distance Metric Learning." In BMVC, pp. 158--1. 2015.

[13]

Guillaumin, Thomas Mensink, Jakob Verbeek, and Cordelia Schmid. Tag-prop: Discriminative metric learning in nearest neighbor models for image auto-annotation. In International Conference on Computer Vision, pages 309--316.

[14]

Xing, Eric P., Andrew Y. Ng, Michael I. Jordan, and Stuart Russell. "Distance metric learning with application to clustering with side-information." In NIPS, vol. 15, no. 505--512, p. 12. 2002.

Digital Library

[15]

Weinberger, Kilian Q., and Lawrence K. Saul. "Distance metric learning for large margin nearest neighbor classification." Journal of Machine Learning Research 10, no. Feb (2009): 207--244.

Digital Library

[16]

Cao, Song, Kan Chen, and Ram Nevatia. "Abstraction hierarchy and self annotation update for fine grained activity recognition." In Applications of Computer Vision (WACV), 2016 IEEE Winter Conference on, pp. 1--9. IEEE, 2016.

[17]

Cao, Song, and Ram Nevatia. "Forecasting human pose and motion with multibody dynamic model." In Applications of Computer Vision (WACV), 2015 IEEE Winter Conference on, pp. 191--198. IEEE, 2015.

Digital Library

Cited By

Castillo-Rojas WHernández C(2021)Bibliographic Review on Data Mining Techniques Used with Weather DataProgramming and Computing Software10.1134/S036176882108009047:8(817-829)Online publication date: 1-Dec-2021
https://dl.acm.org/doi/10.1134/S0361768821080090
Ashraf FKabir MShafi MRifat J(2020)Finding Homogeneous Climate Zones in Bangladesh From Statistical Analysis of Climate Data Using Machine Learning Technique2020 23rd International Conference on Computer and Information Technology (ICCIT)10.1109/ICCIT51783.2020.9392689(1-6)Online publication date: 19-Dec-2020
https://doi.org/10.1109/ICCIT51783.2020.9392689
Castillo-Rojas WHernandez Hernandez C(2020)Systematic Mapping Study on Techniques Used in Stages of a Data Mining Process Focused to Weather Data Analysis2020 8th International Conference in Software Engineering Research and Innovation (CONISOFT)10.1109/CONISOFT50191.2020.00042(245-253)Online publication date: Nov-2020
https://doi.org/10.1109/CONISOFT50191.2020.00042

Index Terms

Distance Metric Learning Approach for Weather Data Mining
1. Computing methodologies
  1. Machine learning

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BDIOT '18: Proceedings of the 2018 2nd International Conference on Big Data and Internet of Things

October 2018

217 pages

ISBN:9781450365192

DOI:10.1145/3289430

Copyright © 2018 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: 24 October 2018

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BDIOT 2018

BDIOT 2018: 2018 2nd International Conference on Big Data and Internet of Things

October 24 - 26, 2018

Beijing, China

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Cited By

Castillo-Rojas WHernández C(2021)Bibliographic Review on Data Mining Techniques Used with Weather DataProgramming and Computing Software10.1134/S036176882108009047:8(817-829)Online publication date: 1-Dec-2021
https://dl.acm.org/doi/10.1134/S0361768821080090
Ashraf FKabir MShafi MRifat J(2020)Finding Homogeneous Climate Zones in Bangladesh From Statistical Analysis of Climate Data Using Machine Learning Technique2020 23rd International Conference on Computer and Information Technology (ICCIT)10.1109/ICCIT51783.2020.9392689(1-6)Online publication date: 19-Dec-2020
https://doi.org/10.1109/ICCIT51783.2020.9392689
Castillo-Rojas WHernandez Hernandez C(2020)Systematic Mapping Study on Techniques Used in Stages of a Data Mining Process Focused to Weather Data Analysis2020 8th International Conference in Software Engineering Research and Innovation (CONISOFT)10.1109/CONISOFT50191.2020.00042(245-253)Online publication date: Nov-2020
https://doi.org/10.1109/CONISOFT50191.2020.00042

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