随机森林方法在机场温度预测中的应用
A Random Forest Application for Predicting Airport Temperature
中文摘要：
利用2015—2017年欧洲中心细网格数值预报产品，使用随机森林方法，结合不同数量的决策树进行模型训练，研究建立基于随机森林方法的乌鲁木齐机场逐时温度回归预报模型。通过对模型预测结果的检验可以看到，模型预测乌鲁木齐机场温度平均绝对误差≤2 ℃的占样本总数的94%，机场温度?10～30 ℃，平均绝对误差为1.2 ℃，该方法预测效果较好，因此可以尝试使用本方法制作民航机场客观要素指导产品。
中文关键词：
随机森林方法，机场温度，预报
KeyWords：
random forest method, airport temperature, forecast
Abstract：
Using the 2015?2017 European Center numerical forecasting product, combined with different numbers of decision trees for model training, establishes regression forecasting model based on the random forest method at Urumqi airport. Through the inspection of the prediction results of the model, we can see the average absolute temperature error is less than or equal 2 ℃, accounting for 94% of the total number of samples, the airport temperature is between ?10 and 30 ℃, and the average absolute error is 1.2 ℃. The effect is good, so you can try to use this method to produce objective product guidance products for civil aviation airports.