基于MLP神经网络的中国南方地区多因子PWV预测模型
首发时间:2024-01-31
摘要:针对利用全球导航卫星系统(global navigation satellite system, GNSS)反演高精度大气可降水量(precipitable water vapor, PWV)时需要获取大气加权平均温度(Tm)从而影响结果精度的问题,在充分探究了PWV与对流层天顶湿延迟(zenith wet delay, ZWD)等诸多因子相关性的基础上,利用中国南方地区40个探空站在2015~2017年的探空数据,基于多层感知器(multi layer perceptron, MLP)神经网络以及多元回归拟合算法分别建立预测PWV的MLP模型、线性回归模型(linear regression model,LRM)与非线性回归模型(non-linear regression model,NLRM)(简称LR、NLR模型)。为充分探究两种建模方法对PWV精度的影响,利用2018年探空数据为参考值进行模型精度检验,并与传统PWV预测模型(PWV-SC2模型)进行精度对比分析。结果表明,MLP模型的年均均方根误差(RMSE)、偏差(bias)和相对误差(RE)分别为0.66 mm、0.06 mm和2.18%,相比LR模型和NLR模型年均RMSE分别降低了0.11 mm(14.6%)和0.17 mm(20.5%),年均bias分别降低了0.04 mm(43.7%)和0.28 mm(82.3%),年均RE分别降低50.7%和57.3%;相比PWV-SC2模型,年均RMSE和bias分别降低了0.17 mm(20.5%)和0.15 mm(71.4%),年均RE降低了47.7%。因此,MLP模型在中国南方地区有较好的精度及适应性,可应用于中国南方地区高精度PWV预测。
关键词: GNSS 大气可降水量 多层感知器 神经网络模型 回归模型 精度分析 中国南方地区
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A multi-factor PWV prediction model based on MLP neural network for southern China
Abstract:To address the problem of obtaining key parameters such as atmospheric weighted mean temperature (Tm) for the inversion of high-precision Precipitable Water Vapor (PWV) using Global Navigation Satellite System (GNSS), we investigate the correlation between PWV and Based on the correlation between PWV and the tropospheric Zenith Wet Delay (ZWD) and other factors, we use the 2015-2017 sounding data from 40 sounding stations in southern China to establish the MLP model, the linear regression model (LRM) and the multiple regression fitting algorithm to predict PWV based on the multilayer perceptron (MLP) neural network. MLP model, linear regression model (LRM) and nonlinear regression model (NLRM) were developed respectively based on the multilayer perceptron (MLP) neural network and multiple regression fitting algorithm; to fully investigate the influence of the two modeling methods on the accuracy of PWV, the model accuracy was examined using the 2018 sounding data as the reference value and compared with the traditional PWV prediction model (PWV-SC2 model). The results showed that the average annual RMSE, bias, and RE of the MLP model were 0.66 mm, 0.06 mm, and 2.18, respectively, which were 0.11 mm (14.6%) and 0.17 mm (20.5%) lower compared to the LR model and the NLR model in terms of average annual RMSE, 0.04 mm (43.7%) and 0.28 mm in terms of average annual bias ( 82.3%), and 50.7% and 57.3%lower annual mean RE, respectively; compared to the PWV-SC2 model, the annual mean RMSE and bias were reduced by 0.17 mm (20.5%) and 0.15 mm (71.4%), respectively, and the annual mean RE was reduced by 47.7%. Therefore, the MLP model has better accuracy and adaptability and can be applied to high-precision PWV prediction in southern China.
Keywords: GNSS atmospheric precipitable water multilayer perceptron neural network model regression model accuracy analysis southern China
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基于MLP神经网络的中国南方地区多因子PWV预测模型
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