{"title":"The Research of the Trend between the Annual Maximum Sea Level and Southern Oscillation Index","authors":"Jing Guan, D. Shi, Y. He","doi":"10.1109/ICBBE.2009.5163657","DOIUrl":null,"url":null,"abstract":"El Nino-Southern Oscillation is the strongest sea-weather interaction phenomena, which causes global climate change, and makes significant impact on sea level variation. The linear trend of the annual maximum sea level at Fremantle Port, Western Australia, related with time and Southern Oscillation index during 1897-1989 is analyzed by linear conditional quantile regression model. And the result is compared with that of the classical least square regression. The result shows that, under different quantiles, the linear trend of the annual maximum sea level related with time and Southern Oscillation Index is different, and pantile regression can provide much more information than the classical least square regression. So it is of great significant for prediction and prevention.","PeriodicalId":6430,"journal":{"name":"2009 3rd International Conference on Bioinformatics and Biomedical Engineering","volume":"23 1","pages":"1-4"},"PeriodicalIF":0.0000,"publicationDate":"2009-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 3rd International Conference on Bioinformatics and Biomedical Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICBBE.2009.5163657","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
El Nino-Southern Oscillation is the strongest sea-weather interaction phenomena, which causes global climate change, and makes significant impact on sea level variation. The linear trend of the annual maximum sea level at Fremantle Port, Western Australia, related with time and Southern Oscillation index during 1897-1989 is analyzed by linear conditional quantile regression model. And the result is compared with that of the classical least square regression. The result shows that, under different quantiles, the linear trend of the annual maximum sea level related with time and Southern Oscillation Index is different, and pantile regression can provide much more information than the classical least square regression. So it is of great significant for prediction and prevention.
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