海洋测绘2024，Vol.44Issue(4)：64-68,5.DOI:10.3969/j.issn.1671-3044.2024.04.014
基于机器学习的海面风速和有效波高联合反演
Joint inversion of sea surface wind speed and significant wave height based on machine learning
摘要
Abstract
Sea surface wind speed and significant wave height(SWH)are key parameters in the marine environment,with a closely interrelated relationship.The global navigation satellite system reflectometry(GNSS-R)can effectively invert sea surface wind speed and SWH,yet existing studies have been limited to the inversion of single parameter.Thus,this paper presents a method for the joint inversion of sea surface wind speed and SWH based on machine learning algorithms.Initially,valid observational data from the cyclone global navigation satellite system(CYGNSS)were acquired through quality control.Subsequently,joint inversion models were constructed using random forest,extreme gradient boosting,light gradient boosting machine,decision tree,and adaptive boosting algorithms,and their inversion performances were comparatively analyzed.It is experimentally shown that the extreme gradient boosting is more suitable for the joint inversion of sea surface wind speed and SWH,and the root mean square errors are 0.91 m/s and 0.20 m,and the correlation coefficients reach 0.90 and 0.96,respectively.Compared with the traditional single parameter inversion,the method in this paper can realize efficient and accurate inversion of sea surface wind speed and SWH.关键词
全球导航卫星系统反射测量/海面风速/有效波高/机器学习/联合反演Key words
GNSS-R/sea surface wind speed/significant wave height/machine learning/joint inversion分类
天文与地球科学引用本文复制引用
梁月吉,蒋雪玉,党毓茜,罗启迪,朱丙林..基于机器学习的海面风速和有效波高联合反演[J].海洋测绘,2024,44(4):64-68,5.基金项目
国家自然科学基金(42064003) （42064003）
广西省自然科学基金(2021GXNSFBA220046,2022GXNSFBA035639). （2021GXNSFBA220046,2022GXNSFBA035639）
