Physical Studies of Airborne Pollen and Particulates Utilizing Machine Learning

This dissertation presents an approach for estimating the abundance of airborne pollen and particulates using a comprehensive description of the physical environment coupled with machine learning. The aspects of the physical environment are characterized by eighty-five variables that quantify the physical state of the land surface and soil, and the physical state of the atmosphere. The physical environment of plants naturally affects their rate of maturing and pollen generation. Then, once the pollen is released, conditions such as wind speed will affect how the pollen is dispersed. Machine learning is helpful for studying such a complex system. Machine learning allows us to ‘learn by example’, since at present, we do not have a complete theoretical description, from first principles, of the entire system from the plant growth and development to the plants’ full interaction with its physical environment. Machine learning allows us to objectively highlight which physical parameters play a central role in determining the atmospheric abundance of the pollen, and hence, the impact on human health. Some key aspects in building a physical model of airborne particulates using machine learning that are explored in this dissertation include:

1. The collection of an appropriate and comprehensive training dataset that machine learning algorithms can use to learn from. This involves characterizing the appropriate temporal and spatial scales involved. Variograms were used to perform this analysis. Machine learning is an automated encapsulation of the scientific method, an automated paradigm for learning by example to build descriptive models that can be tested and iteratively improved.
2. Identifying the physical parameters which are the most appropriate input variables (or features) to build an accurate machine learning model. This is a key step in machine learning called feature engineering. Feature engineering can provide useful physical insights into the key drivers of the system being studied.
3. Provide a framework for updating the machine learning model as new observational data is collected. This was done by providing a mini-batch training process that allows the machine learning model to be updated in almost real-time.