One Data Mining Technique for Light Detection and Ranging Data Classification in Detection of Windshear

Jingyan Huang; Michael Kwok Po Ng; Pak Wai Chan

One Data Mining Technique for Light Detection and Ranging Data Classification in Detection of Windshear

Jingyan Huang
, Michael Kwok Po Ng
, Pak Wai Chan

Department of Mathematics

Research output: Contribution to conference › Conference paper › peer-review

Abstract

In this paper, we analyze runway-specific Light Detection and Ranging Data (LIDAR) from Hong Kong Observatory (HKO) to enhance the windshear detection performance. Considering the ways that windshear over runway threaten the airplanes for take-off and the uncertain range of windshear location, we develop one data mining technique which gets 100% prediction accuracy for windshear and null windshear examples. Supervised Principal Component Analysis (Supervised PCA) is compared with the data mining technique in terms of prediction accuracy. The results indicate that our data mining technique performs better since supervised PCA might not account for the windshear in irregular locations.

Original language	English
Publication status	Published - 18 Jun 2018
Event	2018 INFORMS International Meeting: A Better World Through O.R., Analytics, and AI - Taipei International Convention Center, Taipei, Taiwan, China Duration: 17 Jun 2018 → 20 Jun 2018 https://3449182.fs1.hubspotusercontent-na1.net/hubfs/3449182/Program_Archives/International/2018-International.pdf (Link to conference programme)

Conference

Conference	2018 INFORMS International Meeting
Country/Territory	Taiwan, China
City	Taipei
Period	17/06/18 → 20/06/18
Internet address	https://3449182.fs1.hubspotusercontent-na1.net/hubfs/3449182/Program_Archives/International/2018-International.pdf (Link to conference programme)

Cite this

@conference{5cdff1f89bd6414a82292202b4603b11,

title = "One Data Mining Technique for Light Detection and Ranging Data Classification in Detection of Windshear",

abstract = "In this paper, we analyze runway-specific Light Detection and Ranging Data (LIDAR) from Hong Kong Observatory (HKO) to enhance the windshear detection performance. Considering the ways that windshear over runway threaten the airplanes for take-off and the uncertain range of windshear location, we develop one data mining technique which gets 100\% prediction accuracy for windshear and null windshear examples. Supervised Principal Component Analysis (Supervised PCA) is compared with the data mining technique in terms of prediction accuracy. The results indicate that our data mining technique performs better since supervised PCA might not account for the windshear in irregular locations.",

author = "Jingyan Huang and Ng, \{Michael Kwok Po\} and Chan, \{Pak Wai\}",

year = "2018",

month = jun,

day = "18",

language = "English",

note = "2018 INFORMS International Meeting : A Better World Through O.R., Analytics, and AI ; Conference date: 17-06-2018 Through 20-06-2018",

url = "https://3449182.fs1.hubspotusercontent-na1.net/hubfs/3449182/Program\_Archives/International/2018-International.pdf",

}

TY - CONF

T1 - One Data Mining Technique for Light Detection and Ranging Data Classification in Detection of Windshear

AU - Huang, Jingyan

AU - Ng, Michael Kwok Po

AU - Chan, Pak Wai

PY - 2018/6/18

Y1 - 2018/6/18

N2 - In this paper, we analyze runway-specific Light Detection and Ranging Data (LIDAR) from Hong Kong Observatory (HKO) to enhance the windshear detection performance. Considering the ways that windshear over runway threaten the airplanes for take-off and the uncertain range of windshear location, we develop one data mining technique which gets 100% prediction accuracy for windshear and null windshear examples. Supervised Principal Component Analysis (Supervised PCA) is compared with the data mining technique in terms of prediction accuracy. The results indicate that our data mining technique performs better since supervised PCA might not account for the windshear in irregular locations.

AB - In this paper, we analyze runway-specific Light Detection and Ranging Data (LIDAR) from Hong Kong Observatory (HKO) to enhance the windshear detection performance. Considering the ways that windshear over runway threaten the airplanes for take-off and the uncertain range of windshear location, we develop one data mining technique which gets 100% prediction accuracy for windshear and null windshear examples. Supervised Principal Component Analysis (Supervised PCA) is compared with the data mining technique in terms of prediction accuracy. The results indicate that our data mining technique performs better since supervised PCA might not account for the windshear in irregular locations.

M3 - Conference paper

T2 - 2018 INFORMS International Meeting

Y2 - 17 June 2018 through 20 June 2018

ER -

One Data Mining Technique for Light Detection and Ranging Data Classification in Detection of Windshear

Abstract

Conference

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