University of Denver
A Geospatial and Machine Learning Framework for Forecasting Ground Level Ozone Pollution
Abstract
dc:description.abstract<p>The major detrimental health effects of ground-level ozone (GLO) pollution make it imperative that both policy makers and ordinary citizens have access to high accuracy, high-resolution forecasts of their local area. Recently, advancements in computing power have made it possible to apply artificial intelligence (AI) techniques to a variety of big data modelling problems, including GLO forecasting and estimation. Of these AI methods, deep neural networks (DNN) have demonstrated the highest accuracy due to their ability extract non-linear relationships from high dimensional, noisy data inputs.</p> <p>This research effort uses novel data sources, namely NOAA’s High Resolution Rapid Refresh (HRRR) meteorology model, and a long-short-term-memory (LSTM) neural network to forecast and interpolate ozone values at high spatiotemporal resolution of 1 hour and 3 km. The accuracies of the LSTM models are analyzed using lagged ozone at various forecast horizons and across the varying geographies of eleven ground sensors. I use Denver, Colorado as my study area due to its long-standing GLO pollution problem and relatively high density of EPA ozone monitoring stations.</p>
Degree
thesis:*- Name thesis:degree_name
- M.A. in Geography
- Level thesis:degree_level
- Masters Thesis
- Year dc:date.available
- 2024
Author and committee
dc:creator, dc:contributor.*- Author dc:creator
-
- Keenan, William J.
- Contributors dc:contributor
-
- Jing Li
- Michael Keables
- Kris Kuzera
Subjects
dc:subject × 13Rights
dc:rights- Statement dc:rights
-
- <p>Copyright is held by the author. User is responsible for all copyright compliance.</p>
- Language dc:language
- English (eng)
Identifiers
dc:identifier.*- Repository record dc:identifier
- https://digitalcommons.du.edu/etd/2398
- OAI identifier oai:identifier
- oai:digitalcommons.du.edu:etd-3387