This study extracted 16 climatic data variables including annual temperature, freeze thaw, precipitation, and snowfall conditions from the Long-term Pavement Performance (LTPP) program database to evaluate the climatic regionalization for pavement infrastructure. The effect and significance of climate change were firstly evaluated using time as the only predictor and t-test. It was found that both the temperature and humidity increased in most States. Around one third of the 800 weather stations record variation of freeze and precipitation classifications and a few of them show significant change of classifications over time based on the results of logistic regression analyses. Three unsupervised machine learning including Principle Component Analysis (PCA), factor analysis and cluster analysis were conducted to identify the main component and common factors for climatic variables, and then to classify datasets into different groups. Then, two supervised machine learning methods including Fisher’s discriminant analysis and Artificial Neural Networks (ANN) were adopted to predict the climatic regions based on climatic data. Results of PCA and factor analysis show that temperature and humidity are the first two principle components and common factors, accounting for 71.6% of the variance. The 4-means clusters include wet no freeze, dry no freeze, dry freeze and snow freeze. The best k-mean clustering suggested 9 clusters with more temperature clusters. Both the Fisher’s linear discriminant analysis and ANN can effectively predict climatic regions with multiple climatic variables. ANN performs better with higher R square and low misclassification rate, especially for those with more layers and nodes.

中文翻译：

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通过无监督和有监督的机器学习对路面气候区域进行分类

这项研究从长期路面性能（LTPP）程序数据库中提取了16个气候数据变量，包括年温度，冻融，降水和降雪条件，以评估路面基础设施的气候区域化。首先使用时间作为唯一的预测因子和t检验来评估气候变化的影响和意义。已经发现，在大多数州，温度和湿度都增加了。800个气象站中约有三分之一记录了冻结和降水分类的变化，其中一些基于Logistic回归分析的结果显示了分类随时间的显着变化。三种无监督的机器学习，包括主成分分析（PCA），进行因子分析和聚类分析以识别气候变量的主要成分和共同因素，然后将数据集分为不同的组。然后，采用了两种有监督的机器学习方法，包括Fisher判别分析和人工神经网络（ANN），根据气候数据预测气候区域。PCA和因素分析的结果表明，温度和湿度是前两个主要成分和共同因素，占方差的71.6％。4个均值群集包括湿不冻结，干不冻结，干冻结和雪冻结。最佳k均值聚类建议9个聚类具有更多温度聚类。Fisher的线性判别分析和ANN都可以有效地预测具有多个气候变量的气候区域。