When making regression predictions, the traditional random forest (RF) algorithm can only make predictions within the training set, which can easily lead to overfitting when modeling data have some specific noise. To solve the problem of over-fitting, an improved RF method is proposed in this paper for wind pressure prediction. With the aim to verify the prediction performance of the improved RF algorithm, this paper predicts the wind pressure coefficients of a high-rise building model without wind pressure measurement points. The results show that the improved RF can achieve good results in predicting the mean and fluctuating wind pressure coefficients of high-rise buildings, and its relative error for each measurement point is basically controlled at 5%, which is acceptable in engineering terms. Further applications show that this improved RF can be used for wind pressure distribution prediction in other large-span building type wind tunnel tests.

中文翻译：

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一种改进的随机森林算法及其在风压预测中的应用

在进行回归预测时，传统的随机森林（RF）算法只能在训练集内进行预测，当建模数据有一些特定的噪声时，很容易导致过拟合。针对过拟合问题，本文提出了一种改进的RF方法进行风压预测。为验证改进后的RF算法的预测性能，本文对无风压测点的高层建筑模型风压系数进行了预测。结果表明，改进后的RF在预测高层建筑的平均风压系数和脉动风压系数方面取得了较好的效果，其每个测量点的相对误差基本控制在5%，在工程上是可以接受的。