University of Illinois Urbana-Champaign

Machine learning models on geographic spatial-temporal data predictions

Li, Yanye

Content Files
LI-THESIS-2022.pdf

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https://hdl.handle.net/2142/115632

Description

Title
Machine learning models on geographic spatial-temporal data predictions
Author(s)
Li, Yanye
Issue Date
2022-04-29
Director of Research (if dissertation) or Advisor (if thesis)
Brunner, Robert J
Department of Study
Electrical & Computer Eng
Discipline
Electrical & Computer Engr
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
Machine Learning, Time Series, Spatial-temporal Data
Abstract
Geographic data was not a primary area for early machine learning research. But just as computers rapidly became important tools in radiology, financial trading, and other fields that require fast, highly accurate prediction-based work, machine learning is also showing its ability to push the limits in geospatial data prediction in a very short period. Furthermore, many geographic data analysis include the time dimension to accommodate the temporal dependencies of observations since they often desire to quantify certain changes in environments or landscapes. This added dimension often makes machine learning predictions much harder. In general, it is common for scientists to migrate models used in speech processing, such as recurrent neural networks, to geographic spatial-temporal datasets because the knowledge about temporal dependencies can relatively easily be applied in a similar manner. This thesis first introduces simple regression models and discusses the special considerations required for the three-dimensional data, and this thesis also introduces a state-of-the-art deep learning method, spatial-temporal neural network (STNN), together with its variations. STNN is a specialized recurrent neural network that aims to learn from a series of observations that share both spatial and temporal interactions. We implement these models and compare their performances on experimental results from two different geographic spatial-temporal datasets. Both of the datasets are representative of predictions works in geographic information science, although they differ in some characteristics such as size, timescale, and reversibility. In the end, the comparison leads to a discussion on different strategies of learning and potential improvement.
Graduation Semester
2022-05
Type of Resource
Thesis
Handle URL
https://hdl.handle.net/2142/115632
Copyright and License Information
Copyright 2022 Yanye Li

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