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001 on1164146181;

003 OCoLC;

005 20210119103134.0;

006 m o d ;

007 cr unu||||||||;

008 200709s2020 ncuab obm 000 0 eng d;

035 a| (Sirsi) o1164146181;

035 a| (OCoLC)1164146181;

049 a| EREE;

090 a| QC903;

100 1 a| Matta, Rekesh,e| author.?| UNAUTHORIZED;

245 10 a| Environmental model accuracy improvement framework using statistical techniques and a novel training approach /c| by Rakesh Matta.;

264 1 a| [Greenville, N.C.] :b| [East Carolina University],c| 2020.;

300 a| 51 pages :b| color illustrations, maps;

336 a| textb| txt2| rdacontent;

337 a| computerb| c2| rdamedia;

338 a| online resourceb| cr2| rdacarrier;

347 a| text fileb| PDFc| 6.669 MB2| rda;

538 a| System requirements: Adobe Reader.;

538 a| Mode of access: World Wide Web.;

502 b| Computer Sciencec| East Carolina Universityd| 2020.;

500 a| Presented to the faculty of the Department of Computer Science.;

500 a| Advisor: Rui Wu;

500 a| Title from PDF t.p. (viewed December 23, 2020).;

520 3 a| It is challenging to predict environmental behaviors because of extreme events, such as heatwaves, typhoons, droughts, tsunamis, torrential downpour, wind ramps, or hurricanes. In this thesis, we proposed a novel framework to improve environmental model accuracy with a novel training approach. Extreme event detection algorithms are surveyed, selected, and applied in our proposed framework. The application of statistics in extreme events detection is quite diverse and leads to diverse formulations, which need to be designed for a specific problem. Each formula needs to be tailored specially to work with the available data in the given situation. This diversity is one of the driving forces of this research towards identifying the most common mixture of components utilized in the analysis of extreme events detection. Besides the extreme event detection algorithm, we also integrated the sliding window approach to see how well our models predict future events. To test the proposed framework, we collected coastal data from various sources and obtained the results; we improved the predictive accuracy of various machine learning models by 20% to 25% increase in R2 value using our approach. Apart from that, we organized the discussion along with different extreme event detection types, presented a few outlier definitions, and briefly introduced their techniques. We also summarized the statistical methods involved in the detection of environmental extremes, such as wind ramps and climatic events.;

504 a| Includes bibliographical references.;

650 0 a| Climatic changesx| Risk management.=| ^A786977;

650 0 a| Weatherx| Computer simulation.=| ^A11382;

700 1 a| Wu, Rui,e| degree supervisor.=| ^A1412862;

710 2 a| East Carolina University.b| Department of Computer Science.?| UNAUTHORIZED;

856 40 z| Access via ScholarShipu| http://hdl.handle.net/10342/8591;

949 o| wjh;

994 a| C0b| ERE;

596 a| 1 4;

998 a| 5336205;

999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 5336205-1001l| JNETm| JOYNERr| Ys| Yt| JNE3ETDu| 7/9/2020x| ETDz| JERESOURCE;

999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 5336205-2001l| HSLELECm| HSLr| Ys| Yt| HEETDu| 7/9/2020x| ETDz| HERESOURCE;

Environmental model accuracy improvement framework using statistical techniques and a novel training approach / by Rakesh Matta.

Author/creator	Matta, Rekesh author.
Other author	Wu, Rui, degree supervisor.
Other author	East Carolina University. Department of Computer Science.
Format	Theses and dissertations
Publication	[Greenville, N.C.] : [East Carolina University], 2020.
Description	51 pages : color illustrations, maps
Supplemental Content	Access via ScholarShip
Subjects	Climatic changes--Risk management. Weather--Computer simulation.

Summary	It is challenging to predict environmental behaviors because of extreme events, such as heatwaves, typhoons, droughts, tsunamis, torrential downpour, wind ramps, or hurricanes. In this thesis, we proposed a novel framework to improve environmental model accuracy with a novel training approach. Extreme event detection algorithms are surveyed, selected, and applied in our proposed framework. The application of statistics in extreme events detection is quite diverse and leads to diverse formulations, which need to be designed for a specific problem. Each formula needs to be tailored specially to work with the available data in the given situation. This diversity is one of the driving forces of this research towards identifying the most common mixture of components utilized in the analysis of extreme events detection. Besides the extreme event detection algorithm, we also integrated the sliding window approach to see how well our models predict future events. To test the proposed framework, we collected coastal data from various sources and obtained the results; we improved the predictive accuracy of various machine learning models by 20% to 25% increase in R2 value using our approach. Apart from that, we organized the discussion along with different extreme event detection types, presented a few outlier definitions, and briefly introduced their techniques. We also summarized the statistical methods involved in the detection of environmental extremes, such as wind ramps and climatic events.
General note	Presented to the faculty of the Department of Computer Science.
General note	Advisor: Rui Wu
General note	Title from PDF t.p. (viewed December 23, 2020).
Dissertation note	Computer Science East Carolina University 2020.
Bibliography note	Includes bibliographical references.
Technical details	System requirements: Adobe Reader.
Technical details	Mode of access: World Wide Web.

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Environmental model accuracy improvement framework using statistical techniques and a novel training approach / by Rakesh Matta.

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