首页|New Machine Learning Findings Reported from Southwest Jiaotong University (Global Zenith Wet Delay Modeling With Surface Mete- orological Data and Machine Learning)
New Machine Learning Findings Reported from Southwest Jiaotong University (Global Zenith Wet Delay Modeling With Surface Mete- orological Data and Machine Learning)
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Springer Nature
2024 FEB 20 (NewsRx) – By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Current study results on Machine Learning have been published. According to news originating from Chengdu, People’s Republic of China, by NewsRx correspondents, research stated, “The tropospheric delay is a major error source for space geodetic techniques, and the performance of its modeling is significantly limited due to the high spatiotemporal variability of the moisture in the lower atmosphere. In this study, global modeling of the tropospheric zenith wet delay (ZWD) was realized based on surface meteorological data obtained from radiosondes and Global Positioning System (GPS) radio occultation (RO) measurements through the random forest (RF) and backpropagation neural network (BPNN) regression analysis.” Funders for this research include China Scholarship Council, TU Wien, National Natural Science Foun- dation of China (NSFC), China Scholarship Council, National Program for Support of Top-notch Young Professions.
ChengduPeople’s Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine LearningSouthwest Jiaotong University
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Springer Nature
