2024.1,4（1）|智能交通与数字化高速铁路沿线风速WRF集成修正预测方法段铸1，2（1.利兹大学土木工程学院，英国利兹LS29JT；2.中南大学轨道交通安全教育部重点实验室，湖南长沙410075）摘要：WRF（WeatherResearchandForecasting）等物理驱动预测方法被证实能够获取有效的风速预测结果。本研究面向WRF预测方法对初始条件的敏感性，构建多目标集成模型MOEnWRF（Multi-ObjectiveEnsembleWRF），优化WRF的预测精度。该方法主要包括两个步骤，首先构建描述预测精度与稳定性的评价指标体系，采用多目标灰狼优化算法（MOGWO，Multi-ObjectiveGreyWolfOptimizer）实现对模型精度以及稳定性的同时优化，获得集成权重的帕累托解集。然后，采用组合距离评估法（CODAS，COmbinativeDistance-basedASsessment）评估帕累托解集的有效性，从解集中选择出最优解，并应用于集成WRF模型。经过4个站点的实际验证，得到所提方法能够有效提升WRF的预测精度，并优于单目标优化集成方法。关键词：高速铁路；风速预测；WRF；多目标优化WRFintegratedcorrectionpredictionmethodforwindspeedalonghigh‑speedrailwaysDUANZhu1,2（1.SchoolofCivilEngineering，UniversityofLeeds，Leeds，UK.LS29JT；2.KeyLaboratoryofTrafficSafetyonTrackofMinistryofEducation，CentralSouthUniversity，Changsha410075，China）Abstract：PhysicallydrivenpredictionmethodssuchasWRF（WeatherResearchandForecasting）havebeenproventobeabletoobtaineffectivewindspeedpredictionresults.ThisstudyfocusesonthesensitivityoftheWRFpredictionmethodtoinitialconditions，constructsamulti-objectiveintegratedmodelMOEnWRF（Multi-ObjectiveEnsembleWRF），andoptimizesthepredictionaccuracyofWRF.Thismethodmainlyconsistsoftwosteps.First，anevaluationindexsystemdescribingthepre‐dictionaccuracyandstabilityisconstructed，andtheMulti-ObjectiveGrayWolfOptimizationalgo‐rithm（MOGWO）isusedtosimultaneouslyoptimizethemodelaccuracyandstability，andobtainPa‐retosolutionsetofintegratedweights.Then，theCOmbinativeDistance-basedASsessmentmethod（CODAS）isusedtoevaluatetheeffectivenessoftheParetosolutionset，selecttheoptimalsolutionfromthesolutionset，andapplyittotheWRFensemblemodel.Afteractualverificationatfoursites，itisfoundthattheproposedm...
