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摘要： 定量模拟太阳辐射对认识黄土高原区气候变化至关重要，现有研究表明机器学习可以很好地模拟太阳辐射，但不同的机器学习法在不同区域模拟精度不同，为了实现黄土高原区太阳辐射数据的最优模拟，从而为农作物模型、水文模型以及气候变化模型提供精度更高的太阳辐射数据。基于随机森林（RF，Random Forest）、人工神经网络（ANN,Artificial Neural Network）和支持向量机（SVM,Support Vector Machine）3种机器学习法来模拟黄土高原地区的太阳辐射并对这3种算法进行比较研究，选取了2003~2009年14个辐射站点和2010~2016年10个辐射站点的实测数据和对应参数气压、云量、云光学厚度、臭氧、可降水水汽以及DEM、坡度、坡向作为模型的训练数据，随机选取2010~2016年4个辐射站点的太阳辐射实测数据对模拟结果进行验证。验证结果表明：RF模型在黄土高原及周边地区的模拟效果最优，平均偏差(MBE)为-0.17 MJ·m-2，均方根误差(RMSE)为1.48 MJ·m-2，拟合优度达到0.96。研究结果表明:RF模型与气象数据及遥感数据结合能够有效解决黄土高原无辐射观测区的太阳辐射模拟问题，对区域太阳辐射的研究具有重要意义。 关键词： 太阳辐射;  随机森林(RF);  人工神经网络（ANN);  支持向量机（SVM);  遥感     Abstract: Quantitative simulation of solar radiation is essential for understanding climate change 0n the Loess Plateau, Many machine learning methods were developed to estimate solar radiation well, but different machine learning methods have different simulation accuracy in different regions, In order to achieve optimal simulation of solar radiation on the Loess Plateau, this provides more higher precision solar radiation data for crop models, hydrological models, and climate change models. In this study, three machine learning methods, including Random Forest (RF), Artificial Neural Network (ANN) and Support Vector Machine (SVM), were applied to estimate solar radiation on the Loess Plateau, three machine learning methods were trained using ground measurements at fourteen radiation sites from 2003 to 2009 and ten radiation sites from 2010 to 2016 and corresponding parameter pressure, cloud fraction, cloud optical thickness, ozone, precipitation water vapor and DEM, slope, and aspect to train the three model, The solar radiation estimates based on three machine learning methods were evaluated using ground measurements at four radiation sites from 2010 to 2016. The validation results show that the RF model has the best simulation effect on the Loess Plateau and surrounding areas. The average deviation is -0.17 MJ·m-2, the root mean square error is 1.48 MJ·m-2, and has a good fit of 0.96. The results show that combined RF model and meteorological data and remote sensing data can effectively solve the problem about solar radiation simulation on the non-radiation observation area of the Loess Plateau, which is of great significance to the research of regional solar radiation.

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