{"title":"利用ANFIS模糊C-Means模拟莫桑比克海峡中尺度反气旋涡旋的轨迹","authors":"Hanitra Elisa Rasoavololoniaina, Harimino Andriamalala Rajaonarisoa, Roselin Randrianantenaina, A. Ratiarison, Hanitra Elisa, Rasoavololoniaina, Harimino Andriamalala, Rajaonarisoa, Todihasina Roselin, Adolphe Randrianantenaina, Andriamanga","doi":"10.33969/ais.2023050105","DOIUrl":null,"url":null,"abstract":"The aim of this paper is to optimize the Fuzzy C-Means (FCM) model of the ANFIS neuro-fuzzy system to model the four types of mesoscale anticyclonic eddy trajectories in the Mozambique Channel as a function of the variables eddy speed average of contour, amplitude and diameter, horizontal wind, atmospheric pressure and bathymetry. The study area concerns the eastern part of the Mozambique Channel between longitudes 41°E-44°E and latitudes 16°S-25°S. We classified the eddy trajectories of interest in our study area into four types according to their formation and dissipation zones. The data used are from the mesoscale eddy track atlas product derived from the META3 altimetry version. 1exp DT allsat for trajectories and eddy properties (amplitude, eddy rotation speed and diameter), GEBCO_2022 grid data for bathymetry, ECMWF data at spatial resolution 1° x 1° for atmospheric pressure, and Copernicus Marine data at spatial resolution 0.25° x 0.25° for wind. The latitudes and longitudes of the daily eddy displacement points from their formation to their dissipation characterize the trajectories. We used two different approaches in our study. The first approach consist to put each endogenous variable as input for the FCM model, while the second approach utilized the endogenous variables multiplied by the multiple regression coefficients. The results conclude that the case where the input variables of the model are preprocessed by the multiple (linear or polynomial) regression operation before FCM modeling is the best approach.","PeriodicalId":273028,"journal":{"name":"Journal of Artificial Intelligence and Systems","volume":"8 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Trajectories modelling of mesoscale anticyclonic eddies in the Mozambique Channel using ANFIS Fuzzy C-Means\",\"authors\":\"Hanitra Elisa Rasoavololoniaina, Harimino Andriamalala Rajaonarisoa, Roselin Randrianantenaina, A. Ratiarison, Hanitra Elisa, Rasoavololoniaina, Harimino Andriamalala, Rajaonarisoa, Todihasina Roselin, Adolphe Randrianantenaina, Andriamanga\",\"doi\":\"10.33969/ais.2023050105\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The aim of this paper is to optimize the Fuzzy C-Means (FCM) model of the ANFIS neuro-fuzzy system to model the four types of mesoscale anticyclonic eddy trajectories in the Mozambique Channel as a function of the variables eddy speed average of contour, amplitude and diameter, horizontal wind, atmospheric pressure and bathymetry. The study area concerns the eastern part of the Mozambique Channel between longitudes 41°E-44°E and latitudes 16°S-25°S. We classified the eddy trajectories of interest in our study area into four types according to their formation and dissipation zones. The data used are from the mesoscale eddy track atlas product derived from the META3 altimetry version. 1exp DT allsat for trajectories and eddy properties (amplitude, eddy rotation speed and diameter), GEBCO_2022 grid data for bathymetry, ECMWF data at spatial resolution 1° x 1° for atmospheric pressure, and Copernicus Marine data at spatial resolution 0.25° x 0.25° for wind. The latitudes and longitudes of the daily eddy displacement points from their formation to their dissipation characterize the trajectories. We used two different approaches in our study. The first approach consist to put each endogenous variable as input for the FCM model, while the second approach utilized the endogenous variables multiplied by the multiple regression coefficients. The results conclude that the case where the input variables of the model are preprocessed by the multiple (linear or polynomial) regression operation before FCM modeling is the best approach.\",\"PeriodicalId\":273028,\"journal\":{\"name\":\"Journal of Artificial Intelligence and Systems\",\"volume\":\"8 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Artificial Intelligence and Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.33969/ais.2023050105\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Artificial Intelligence and Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.33969/ais.2023050105","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Trajectories modelling of mesoscale anticyclonic eddies in the Mozambique Channel using ANFIS Fuzzy C-Means
The aim of this paper is to optimize the Fuzzy C-Means (FCM) model of the ANFIS neuro-fuzzy system to model the four types of mesoscale anticyclonic eddy trajectories in the Mozambique Channel as a function of the variables eddy speed average of contour, amplitude and diameter, horizontal wind, atmospheric pressure and bathymetry. The study area concerns the eastern part of the Mozambique Channel between longitudes 41°E-44°E and latitudes 16°S-25°S. We classified the eddy trajectories of interest in our study area into four types according to their formation and dissipation zones. The data used are from the mesoscale eddy track atlas product derived from the META3 altimetry version. 1exp DT allsat for trajectories and eddy properties (amplitude, eddy rotation speed and diameter), GEBCO_2022 grid data for bathymetry, ECMWF data at spatial resolution 1° x 1° for atmospheric pressure, and Copernicus Marine data at spatial resolution 0.25° x 0.25° for wind. The latitudes and longitudes of the daily eddy displacement points from their formation to their dissipation characterize the trajectories. We used two different approaches in our study. The first approach consist to put each endogenous variable as input for the FCM model, while the second approach utilized the endogenous variables multiplied by the multiple regression coefficients. The results conclude that the case where the input variables of the model are preprocessed by the multiple (linear or polynomial) regression operation before FCM modeling is the best approach.