Classification of Pavement Climatic Regions through Unsupervised and Supervised Machine Learnings

Abstract

This study extracted 16 climatic data variables including annual temperature, freeze thaw, precipitation, and snow fall 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. 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.

An analysis of finding the best strategies of water security for water source areas using an integrated IT2FVIKOR with machine learning

“…On the contrary, unsupervised learning deals with unlabeled data, output, and input, and its goal is to discover relationships and patterns between the data. Therefore, supervised learning can be used to predict the classification, and unsupervised learning can be used to find the optimal classification (Dong et al, 2021). Nowadays, research in ML is in demand due to the huge amount of data and the nature of ML itself, easy and cheap computation.…”

“…From the K-means clustering, numerous significant attributes of the COVID-19 risk were emphasized. Dong et al (2021) extracted 16 climatic data variables to estimate the climatic regionalization for pavement infrastructure using both unsupervised and supervised ML, including three unsupervised ML approaches, namely, factor analysis, principal component analysis (PCA), and cluster analysis, and two supervised ML approaches, namely, artificial neural network (ANN) and Fisher's discriminant analysis. Ahmed et al (2019) compared the supervised and unsupervised approaches to extract traffic-related tweets.…”