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ORCID
https://orcid.org/0000-0002-7321-6051
Access Type
Campus-Only Access for One (1) Year
Document Type
thesis
Embargo Period
8-1-2024
Degree Program
Civil Engineering
Degree Type
Master of Science in Civil Engineering (M.S.C.E.)
Year Degree Awarded
2024
Month Degree Awarded
February
Abstract
This thesis presents machine and statistical learning approaches for sustainable planning in infrastructure and mobility systems. First, I have developed a convolutional neural network (CNN) to predict tree failure likelihood. Such assessments have traditionally been performed manually. I conduct a visual analysis of the predictions, indicating an approach for incorporating interpretability into model selection. Benchmarking the results against those produced by state-of-the-art CNNs, I show that a relatively simple model produces better results in a computational time that is three times faster. Via this novel framework, I demonstrate the potential of machine learning to automate and consequently reduce the costs of tree failure likelihood assessments in proximity to power lines, thereby promoting sustainable infrastructure. Secondly, I examine the effects of COVID-19 on mobility, segmented by transportation type, as well as social activity such as workplaces and residential, and their interdependencies. Using time series data across five continents, I estimate a Bayesian global vector autoregression model which explains patterns in activity and mobility trends and analyze their relationship with COVID-19 spread. I expect that the model framework and outcomes will guide policymakers to adopt appropriate measures to mitigate and safely recover from future disease outbreaks.
DOI
https://doi.org/10.7275/36519325
First Advisor
Jimi Oke
Creative Commons License
This work is licensed under a Creative Commons Attribution-Share Alike 4.0 License.
Recommended Citation
Apostolov, Atanas, "Machine and Statistical Learning for Sustainable Infrastructure and Mobility Systems" (2024). Masters Theses. 1395.
https://doi.org/10.7275/36519325
https://scholarworks.umass.edu/masters_theses_2/1395