A framework of Mult! Linear Regression based on Fuzzy Theory and Situation Awareness and its application to Beach Risk Assessment
Beaches have many risk factors that cause various accidents, such as drifting and drowning, these accidents have many risk factors. To analyze them, in this paper, we identify beach risk factors, and define the criteria and correlation for each risk factor. Then, we generate new risk factors based o...
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Veröffentlicht in: | KSII transactions on Internet and information systems 2020-07, Vol.14 (7), p.3039 |
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container_title | KSII transactions on Internet and information systems |
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creator | Shin, Gun-Yoon Hong, Sung-Sam Kim, Dong-Wook Hwang, Cheol-Hun Han, Myung-Mook Kim, Hwayoung Kim, Young jae |
description | Beaches have many risk factors that cause various accidents, such as drifting and drowning, these accidents have many risk factors. To analyze them, in this paper, we identify beach risk factors, and define the criteria and correlation for each risk factor. Then, we generate new risk factors based on Fuzzy theory, and define Situation Awareness for each time. Finally, we propose a beach risk assessment and prediction model based on linear regression using the calculated risk result and pre-defined risk factors. We use national public data of the Korea Meteorological Administration (KMA), and the Korea Hydrographic and Oceanographic Agency (KHOA). The results of the experiment showed the prediction accuracy of beach risk to be 0.90%, and the prediction accuracy of drifting and drowning accidents to be 0.89% and 0.86%, respectively. Also, through factor correlation analysis and risk factor assessment, the influence of each of the factors on beach risk can be confirmed. In conclusion, we confirmed that our proposed model can assess and predict beach risks. Keywords: Beach Risk Assessment, Beach Risk Factor, Situation Awareness, Fuzzy Theory, Multi Linear Regression. |
doi_str_mv | 10.3837/tiis.2020.07.017 |
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Linear Regression based on Fuzzy Theory and Situation Awareness and its application to Beach Risk Assessment</title><source>EZB-FREE-00999 freely available EZB journals</source><creator>Shin, Gun-Yoon ; Hong, Sung-Sam ; Kim, Dong-Wook ; Hwang, Cheol-Hun ; Han, Myung-Mook ; Kim, Hwayoung ; Kim, Young jae</creator><creatorcontrib>Shin, Gun-Yoon ; Hong, Sung-Sam ; Kim, Dong-Wook ; Hwang, Cheol-Hun ; Han, Myung-Mook ; Kim, Hwayoung ; Kim, Young jae</creatorcontrib><description>Beaches have many risk factors that cause various accidents, such as drifting and drowning, these accidents have many risk factors. To analyze them, in this paper, we identify beach risk factors, and define the criteria and correlation for each risk factor. Then, we generate new risk factors based on Fuzzy theory, and define Situation Awareness for each time. Finally, we propose a beach risk assessment and prediction model based on linear regression using the calculated risk result and pre-defined risk factors. We use national public data of the Korea Meteorological Administration (KMA), and the Korea Hydrographic and Oceanographic Agency (KHOA). The results of the experiment showed the prediction accuracy of beach risk to be 0.90%, and the prediction accuracy of drifting and drowning accidents to be 0.89% and 0.86%, respectively. Also, through factor correlation analysis and risk factor assessment, the influence of each of the factors on beach risk can be confirmed. In conclusion, we confirmed that our proposed model can assess and predict beach risks. Keywords: Beach Risk Assessment, Beach Risk Factor, Situation Awareness, Fuzzy Theory, Multi Linear Regression.</description><identifier>ISSN: 1976-7277</identifier><identifier>EISSN: 1976-7277</identifier><identifier>DOI: 10.3837/tiis.2020.07.017</identifier><language>eng</language><publisher>KSII, the Korean Society for Internet Information</publisher><subject>Accidents ; Regression analysis ; Risk assessment</subject><ispartof>KSII transactions on Internet and information systems, 2020-07, Vol.14 (7), p.3039</ispartof><rights>COPYRIGHT 2020 KSII, the Korean Society for Internet Information</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,27901,27902</link.rule.ids></links><search><creatorcontrib>Shin, Gun-Yoon</creatorcontrib><creatorcontrib>Hong, Sung-Sam</creatorcontrib><creatorcontrib>Kim, Dong-Wook</creatorcontrib><creatorcontrib>Hwang, Cheol-Hun</creatorcontrib><creatorcontrib>Han, Myung-Mook</creatorcontrib><creatorcontrib>Kim, Hwayoung</creatorcontrib><creatorcontrib>Kim, Young jae</creatorcontrib><title>A framework of Mult! Linear Regression based on Fuzzy Theory and Situation Awareness and its application to Beach Risk Assessment</title><title>KSII transactions on Internet and information systems</title><description>Beaches have many risk factors that cause various accidents, such as drifting and drowning, these accidents have many risk factors. To analyze them, in this paper, we identify beach risk factors, and define the criteria and correlation for each risk factor. Then, we generate new risk factors based on Fuzzy theory, and define Situation Awareness for each time. Finally, we propose a beach risk assessment and prediction model based on linear regression using the calculated risk result and pre-defined risk factors. We use national public data of the Korea Meteorological Administration (KMA), and the Korea Hydrographic and Oceanographic Agency (KHOA). The results of the experiment showed the prediction accuracy of beach risk to be 0.90%, and the prediction accuracy of drifting and drowning accidents to be 0.89% and 0.86%, respectively. Also, through factor correlation analysis and risk factor assessment, the influence of each of the factors on beach risk can be confirmed. In conclusion, we confirmed that our proposed model can assess and predict beach risks. 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Linear Regression based on Fuzzy Theory and Situation Awareness and its application to Beach Risk Assessment</atitle><jtitle>KSII transactions on Internet and information systems</jtitle><date>2020-07-01</date><risdate>2020</risdate><volume>14</volume><issue>7</issue><spage>3039</spage><pages>3039-</pages><issn>1976-7277</issn><eissn>1976-7277</eissn><abstract>Beaches have many risk factors that cause various accidents, such as drifting and drowning, these accidents have many risk factors. To analyze them, in this paper, we identify beach risk factors, and define the criteria and correlation for each risk factor. Then, we generate new risk factors based on Fuzzy theory, and define Situation Awareness for each time. Finally, we propose a beach risk assessment and prediction model based on linear regression using the calculated risk result and pre-defined risk factors. We use national public data of the Korea Meteorological Administration (KMA), and the Korea Hydrographic and Oceanographic Agency (KHOA). The results of the experiment showed the prediction accuracy of beach risk to be 0.90%, and the prediction accuracy of drifting and drowning accidents to be 0.89% and 0.86%, respectively. Also, through factor correlation analysis and risk factor assessment, the influence of each of the factors on beach risk can be confirmed. In conclusion, we confirmed that our proposed model can assess and predict beach risks. Keywords: Beach Risk Assessment, Beach Risk Factor, Situation Awareness, Fuzzy Theory, Multi Linear Regression.</abstract><pub>KSII, the Korean Society for Internet Information</pub><doi>10.3837/tiis.2020.07.017</doi></addata></record> |
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source | EZB-FREE-00999 freely available EZB journals |
subjects | Accidents Regression analysis Risk assessment |
title | A framework of Mult! Linear Regression based on Fuzzy Theory and Situation Awareness and its application to Beach Risk Assessment |
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