Prediction model of snow depth of snowdrift on highway based on Back Propagation Neural Network

Abstract:

As the research focus in international snow and ice field, snow-depth prediction of snowdrift on highway still has not been well solved. Based on meteorological data provided by automatic weather stations installed along the anti snow corridor on White Snow Mountain and meteorological bureau, index values of four factors (snowfall, air temperature, wind speed and humidity) which have influence on snow depth of snowdrift on highway are extracted and prediction model of snow depth of snowdrift on highway based on BP Neural Network is established. 199 sets of data during five snowfall in study area are used to train network and establish model, then use 20 sets of data to validate the model. Validation results show relative error of accumulated snow-depth predictions in 20 hours is less than 10% and 85% of relative error of snow-depth predictions is less than 20%. Therefore, the model has strong generalization ability and high accuracy. Sensitivity analysis of snowfall, air temperature, wind speed and humidity indicates that snow depth is directly proportional to snowfall and inversely proportional to other three factors, wherein snowfall has the greatest impact on snowdepth, followed by wind speed, humidity minimum.