Prediction of wind speed at high plateau airports can not only provide certain theoretical basis for the safe and efficient operation of the airports,but also save cost and time for their flight scheduling.In this pap...Prediction of wind speed at high plateau airports can not only provide certain theoretical basis for the safe and efficient operation of the airports,but also save cost and time for their flight scheduling.In this paper,based on the data of average wind speed and related meteorological factors at the meteorological station of Lhasa Gonggar Airport from 1964 to 2019,a prediction model of wind speed was constructed based on the support vector regression(SVR)algorithm.After the analysis of correlations between various meteorological features,significant features were selected by the random forest algorithm,thereby further improving the prediction performance of the model.The results indicate that both visibility and temperature having high correlations with wind speed are key features determining the final accuracy of the prediction model.Meanwhile,compared with other machine learning algorithms,the SVR algorithm represents more highlighted prediction performance for small sample data.展开更多
[Objective] The research aimed to study forecast models for frozen and melted dates of the river water in Ningxia-Inner Mongolia section of the Yellow River based on SVR optimized by particle swarm optimization algori...[Objective] The research aimed to study forecast models for frozen and melted dates of the river water in Ningxia-Inner Mongolia section of the Yellow River based on SVR optimized by particle swarm optimization algorithm. [Method] Correlation analysis and cause analysis were used to select suitable forecast factor combination of the ice regime. Particle swarm optimization algorithm was used to determine the optimal parameter to construct forecast model. The model was used to forecast frozen and melted dates of the river water in Ningxia-Inner Mongolia section of the Yellow River. [Result] The model had high prediction accuracy and short running time. Average forecast error was 3.51 d, and average running time was 10.464 s. Its forecast effect was better than that of the support vector regression optimized by genetic algorithm (GA) and back propagation type neural network (BPNN). It could accurately forecast frozen and melted dates of the river water. [Conclusion] SVR based on particle swarm optimization algorithm could be used for ice regime forecast.展开更多
文摘Prediction of wind speed at high plateau airports can not only provide certain theoretical basis for the safe and efficient operation of the airports,but also save cost and time for their flight scheduling.In this paper,based on the data of average wind speed and related meteorological factors at the meteorological station of Lhasa Gonggar Airport from 1964 to 2019,a prediction model of wind speed was constructed based on the support vector regression(SVR)algorithm.After the analysis of correlations between various meteorological features,significant features were selected by the random forest algorithm,thereby further improving the prediction performance of the model.The results indicate that both visibility and temperature having high correlations with wind speed are key features determining the final accuracy of the prediction model.Meanwhile,compared with other machine learning algorithms,the SVR algorithm represents more highlighted prediction performance for small sample data.
基金Supported by National Nature Science Fund Item,China (51009065)Key Science and Technology Research Plan Program in Henan Province,China(112102110033)
文摘[Objective] The research aimed to study forecast models for frozen and melted dates of the river water in Ningxia-Inner Mongolia section of the Yellow River based on SVR optimized by particle swarm optimization algorithm. [Method] Correlation analysis and cause analysis were used to select suitable forecast factor combination of the ice regime. Particle swarm optimization algorithm was used to determine the optimal parameter to construct forecast model. The model was used to forecast frozen and melted dates of the river water in Ningxia-Inner Mongolia section of the Yellow River. [Result] The model had high prediction accuracy and short running time. Average forecast error was 3.51 d, and average running time was 10.464 s. Its forecast effect was better than that of the support vector regression optimized by genetic algorithm (GA) and back propagation type neural network (BPNN). It could accurately forecast frozen and melted dates of the river water. [Conclusion] SVR based on particle swarm optimization algorithm could be used for ice regime forecast.
