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Full-Text Articles in Physical Sciences and Mathematics
On The Use Of Machine Learning For Causal Inference In Extreme Weather Events, Yuzhe Wang
On The Use Of Machine Learning For Causal Inference In Extreme Weather Events, Yuzhe Wang
Discovery Undergraduate Interdisciplinary Research Internship
Machine learning has become a helpful tool for analyzing data, and causal Inference is a powerful method in machine learning that can be used to determine the causal relationship in data. In atmospheric and climate science, this technology can also be applied to predicting extreme weather events. One of the causal inference models is Granger causality, which is used in this project. Granger causality is a statistical test for identifying whether one time series is helpful in forecasting the other time series. In granger causality, if a variable X granger-causes Y: it means that by using all information without …
Crowd-Machine Partnership On Road Infrastructure Quality Recognition And Resilience, Eric J. Thompson
Crowd-Machine Partnership On Road Infrastructure Quality Recognition And Resilience, Eric J. Thompson
Discovery Undergraduate Interdisciplinary Research Internship
Public roads are a vital component of modern-day society, as they are necessary for the transportation of people and capital; consequently, it is important that they are regularly and effectively maintained. Unfortunately, this maintenance is difficult to manage due to the sheer area that roads span. It is an arduous task to locate every instance of road damage, as well as to determine the urgency that each bit of damage necessitates. Repairing road damage has high costs in labor, time, and money. To provide a more efficient way to monitor road conditions, we are designing a mobile application that collects …