Article citationsMore>>

Liu, Y., Racah, E., Prabhat, Correa, J., Khosrowshahi, A., Lavers, D. and Collins, W. (2016) Application of Deep Convolutional Neural Networks for Detecting Extreme Weather in Climate Datasets. In: Proceeding from ABDA’16: The 3rd International Conference on Advances in Big Data Analytics, CSREA, Las Vegas, 81-88.

has been cited by the following article:

• TITLE: “Smart” Surveillance of Dusty Behavior: Illuminating the Relationship between Particulate Matter and the Atmosphere

  AUTHORS: Shelley Choi

  KEYWORDS: Atmosphere, Particulate Matter, Weather Forecast, Artificial Intelligence, Deep Learning

  JOURNAL NAME: Computational Water, Energy, and Environmental Engineering, Vol.7 No.3, July 20, 2018

  ABSTRACT: Although large amounts of research have been completed to find the relationship between particulate matter and climate change, they have still proven to be inadequate. Efforts to lay the foundations for understanding atmospheric chemical reactions have been repeatedly foiled by both the size and complexity of the task, which require more than the effort of a handful of researchers. Since the development of advanced physical models for dust behavior is projected to take years, what if laypeople could dramatically expedite this process by using their mobile devices as measurement tools? With relatively little effort by many individuals, previously unknown information about the earth’s atmosphere may at last become accessible thanks to recent advances in artificial intelligence. However, there are potential obstacles. Even if all technical problems are resolved, viable plans for battling particulate matter pollution will likely need to be accompanied by environmental policies. While technological breakthroughs give reason to hope for a brighter future, the resolution of global issues requires both grassroots changes and global efforts.