An evaluation of big data analytics in feature selection for long-lead extreme floods forecasting

Yong Zhuang; Kui Yu; Dawei Wang; Wei Ding

doi:10.1109/ICNSC.2016.7479007

An evaluation of big data analytics in feature selection for long-lead extreme floods forecasting

Yong Zhuang
, Kui Yu
, Dawei Wang
, Wei Ding

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Original language	English
Title of host publication	ICNSC 2016 - 13th IEEE International Conference on Networking, Sensing and Control
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781467399753
DOIs	https://doi.org/10.1109/ICNSC.2016.7479007
State	Published - May 25 2016
Event	13th IEEE International Conference on Networking, Sensing and Control, ICNSC 2016 - Mexico City, Mexico Duration: Apr 28 2016 → Apr 30 2016

Publication series

Name	ICNSC 2016 - 13th IEEE International Conference on Networking, Sensing and Control

Conference

Conference	13th IEEE International Conference on Networking, Sensing and Control, ICNSC 2016
Country/Territory	Mexico
City	Mexico City
Period	4/28/16 → 4/30/16

ASJC Scopus Subject Areas

Artificial Intelligence
Computer Networks and Communications
Control and Systems Engineering
Modeling and Simulation

Keywords

Online Group Feature Selection
Online Streaming Feature Selection
Precipitation Prediction

Access to Document

10.1109/ICNSC.2016.7479007

Cite this

Zhuang, Y., Yu, K., Wang, D., & Ding, W. (2016). An evaluation of big data analytics in feature selection for long-lead extreme floods forecasting. In ICNSC 2016 - 13th IEEE International Conference on Networking, Sensing and Control Article 7479007 (ICNSC 2016 - 13th IEEE International Conference on Networking, Sensing and Control). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICNSC.2016.7479007

An evaluation of big data analytics in feature selection for long-lead extreme floods forecasting. / Zhuang, Yong; Yu, Kui; Wang, Dawei et al.
ICNSC 2016 - 13th IEEE International Conference on Networking, Sensing and Control. Institute of Electrical and Electronics Engineers Inc., 2016. 7479007 (ICNSC 2016 - 13th IEEE International Conference on Networking, Sensing and Control).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Zhuang, Y, Yu, K, Wang, D & Ding, W 2016, An evaluation of big data analytics in feature selection for long-lead extreme floods forecasting. in ICNSC 2016 - 13th IEEE International Conference on Networking, Sensing and Control., 7479007, ICNSC 2016 - 13th IEEE International Conference on Networking, Sensing and Control, Institute of Electrical and Electronics Engineers Inc., 13th IEEE International Conference on Networking, Sensing and Control, ICNSC 2016, Mexico City, Mexico, 4/28/16. https://doi.org/10.1109/ICNSC.2016.7479007

Zhuang Y, Yu K, Wang D, Ding W. An evaluation of big data analytics in feature selection for long-lead extreme floods forecasting. In ICNSC 2016 - 13th IEEE International Conference on Networking, Sensing and Control. Institute of Electrical and Electronics Engineers Inc. 2016. 7479007. (ICNSC 2016 - 13th IEEE International Conference on Networking, Sensing and Control). doi: 10.1109/ICNSC.2016.7479007

Zhuang, Yong ; Yu, Kui ; Wang, Dawei et al. / An evaluation of big data analytics in feature selection for long-lead extreme floods forecasting. ICNSC 2016 - 13th IEEE International Conference on Networking, Sensing and Control. Institute of Electrical and Electronics Engineers Inc., 2016. (ICNSC 2016 - 13th IEEE International Conference on Networking, Sensing and Control).

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author = "Yong Zhuang and Kui Yu and Dawei Wang and Wei Ding",

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M3 - Conference contribution

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BT - ICNSC 2016 - 13th IEEE International Conference on Networking, Sensing and Control

PB - Institute of Electrical and Electronics Engineers Inc.

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An evaluation of big data analytics in feature selection for long-lead extreme floods forecasting

Publication series

Conference

ASJC Scopus Subject Areas

Keywords

Access to Document

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