首页|Studies from Science University of Malaysia Provide New Data on Machine Learning (Improved Tropical Cyclone Wind Speed Estimation for Microwave Altimeter Using Machine Learning)
Studies from Science University of Malaysia Provide New Data on Machine Learning (Improved Tropical Cyclone Wind Speed Estimation for Microwave Altimeter Using Machine Learning)
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原文链接
NETL
NSTL
Elsevier
Current study results on Machine Learning have been published. According to news originating from George Town, Malaysia, by NewsRx correspondents, research stated, “Satellite altimeters can provide excellent global wind speed at 10 m above the sea surface (U10), however, the U10 becomes inaccurate and difficult to measure in tropical cyclone conditions. The violent wind, rough waves and torrential rain manifest an exceptionally complex ocean-atmospheric environment for wind estimation.” Financial supporters for this research include Universiti Sains Malaysia, Ministry of Education, Malaysia. Our news journalists obtained a quote from the research from the Science University of Malaysia, “Although the backscatter signal is measured equally well in normal condition, the interpretation is not straightforward in tropical cyclones that requires complex associations with ocean-atmospheric geophysical variables. Typical U10 regression model developed in normal atmospheric conditions would inevitably reduce the estimation quality and encounter high modelling uncertainties from high dimensional input data that provide ill-posed solutions in extreme U10 estimation. However, other secondary parameters simultaneously measured by the altimeter have properties that convey additional atmospheric information to enhance U10 estimation accuracy in storm condition. Therefore, the present study proposes machine learning (ML) approaches based on artificial neural network (ANN), support vector machine (SVM), and Gaussian process regression (GPR) to integrate the multi-dimensional parameters and provide accurate U10 estimates for different parameter combination. Results suggest that the GPR method, considering atmospheric and sea surface related parameters, can provide the highest accuracy of U10 up to 35 ms-1 with quality perseverance against rain contamination.”
George TownMalaysiaAsiaCyborgsEmerging TechnologiesMachine LearningScience University of Malaysia
NETL
NSTL
Elsevier
