基于GA-PSO-BP神经网络的气象能见度预测

针对安徽省气象能见度数据缺测问题,本文选取安徽省四种不同地形条件下的自动气象站点(黄山站、灵璧站、山南溪谷站、白泽湖站)2017-2019年的气象数据,首先采用灰色关联分析法筛选出与能见度联系紧密的气象要素,然后构建遗传算法(Genetic algorithm, GA)和粒子群算法(Particle swarm optimization algorithm, PSO)混合算法优化BP(Back Propagation)神经网络的预测模型,对四种不同地形条件下的自动气象站点的能见度进行预测,并与RF预测模型、XGBoost预测模型的预测效果进行对比,结果表明采用GA-PSO-BP神经网络预测模型无论在哪种地形条件下,预测误差更小,模型精度更高。

Meteorological Visibility Prediction Based on GA-PSO-BP Neural Network

In view of the lack of meteorological visibility data in Anhui Province, this paper selects the meteorological data of four automatic meteorological stations (Mount Huangshan Station, Lingbi Station, Shannan Xigu Station, and Baize Lake Station) under different terrain conditions in Anhui Province from 2017 to 2019, and firstly selects the meteorological elements closely related to visibility by using the gray correlation analysis method. Then, a hybrid algorithm of Genetic Algorithm (GA) and Particle Swarm Optimization Algorithm (PSO) is constructed to optimize the prediction model of the Back Propagation neural network. The visibility of automatic weather stations under four different terrain conditions is predicted, and the prediction results are compared with the RF prediction model and XGBoost prediction model. The results indicate that the GA-PSO-BP neural network prediction model has smaller prediction error and higher model accuracy under any terrain condition.