Classification of pavement climatic regions through unsupervised and supervised machine learnings

Journal of Infrastructure Preservation and Resilience

Shi Dong¹  Xueqin Chen²  Jun Zhang³  Qiao Dong⁴ 

[1] College of Transportation Engineering, China Engineering Research Center of Highway Infrastructure Digitalization, Ministry of Education of PRC, Chang’an University;Department of Civil Engineering, Nanjing University of Science and Technology;Louisiana Transportation Research Center;School of Transportation, Southeast University;

关键词: Climatic regions;    Climate change;    Machine learning;    Principle component analysis (PCA);    Factor analysis;    K-means cluster analysis;   

DOI  :  10.1186/s43065-021-00020-7

来源: DOAJ

【 摘 要 】

Abstract 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.

【 授权许可】

Unknown