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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/32398

Title: The Use of Neural Networks in Identifying Error Sources in Satellite-Derived Tropical SST Estimates
Authors: Yung-Hsiang Lee;Chun-Ru Ho;Feng-Chun Su;Nan-Jung Kuo;Yu-Hsin Cheng
Contributors: NTOU:Department of Marine Environmental Informatics
Keywords: infrared sensor, data mining, neural network, sea surface temperature, tropical pacific
Date: 2011-08
Issue Date: 2012-06-15T08:02:28Z
Publisher: Sensors
Abstract: An neural network model of data mining is used to identify error sources in satellite-derived tropical sea surface temperature (SST) estimates from thermal infrared sensors onboard the Geostationary Operational Environmental Satellite (GOES). By using the Back Propagation Network (BPN) algorithm, it is found that air temperature, relative humidity, and wind speed variation are the major factors causing the errors of GOES SST products in the tropical Pacific. The accuracy of SST estimates is also improved by the model. The root mean square error (RMSE) for the daily SST estimate is reduced from 0.58 K to 0.38 K and mean absolute percentage error (MAPE) is 1.03%. For the hourly mean SST estimate, its RMSE is also reduced from 0.66 K to 0.44 K and the MAPE is 1.3%.
Relation: 11(8), pp.7530-7544
URI: http://ntour.ntou.edu.tw/handle/987654321/32398
Appears in Collections:[海洋環境資訊系] 期刊論文

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