Global to Glocal: A Confluence of Data Science and Earth Observations in the Advancement of the SDGs

<p>The United Nations' (UN) Sustainable Development Goals (SDGs), part of Agenda 2030, comprise 17 interconnected goals and 169 actionable targets, providing an effective framework for addressing diverse issues ranging from individual challenges such as poverty, hunger, and health to broader corporate and global challenges like climate change and equality. Among these interconnected SDGs, this dissertation focuses on the role of climate and infrastructure in global and local sustainability. To this end, earth observations have been conducted utilizing data science techniques to advance these SDGs. For this dissertation, the author has conducted earth studies serving the following SDGs:</p> <ol> <li>SDG 3 (Good Health and Well-being): The influence of atmospheric aerosols on the fatality rate for COVID-19 was studied. Results suggested that atmospheric aerosol optical depth may be a factor in estimating the coronavirus fatality rate, though uncertainties in the analytical results could be attributed to data quality and the absence of other important factors.</li> <li>SDG 6 (Clean Water and Sanitation): A groundwater case study was conducted for California, providing insights into future groundwater availability. Forecasts using deep learning suggested continued deficits in coastal and southern hydrological regions, while increased precipitation was forecasted for some inland hydrological regions.</li> <li>SDG 9 (Industry, Innovation, and Infrastructure): Displacements observed on the Grand Ethiopian Renaissance Dam (GERD) (Ethiopia) and the Shasta Dam (California, USA) were analyzed using the latest interferometry techniques. Time series analysis revealed different displacement trends at various sections of the GERD and the Saddle Dam, with varying displacement magnitudes observed on the Shasta Dam.</li> <li>SDG 11 (Sustainable Cities and Communities) and 13 (Climate Action): A comprehensive analysis of land stability in Alexandria, Egypt, was conducted. A novel approach was developed to update Digital Elevation Models (DEMs) using the Small Baseline Subset method, facilitating the creation of a probabilistic DEM for the year 2040.</li> </ol>

Name thesis:degree_name

Doctor of Philosophy (PhD)

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Computational and Data Sciences

Year dc:date.available

2024

Author dc:creator

• Thomas, Rejoice

Contributors dc:contributor

• Hesham El-Askary
• Wenzhao Li
• Joshua B. Fisher
• Erik Linstead
• Daniele C. Struppa

• Machine Learning
• Earth Observations
• Radar
• Remote Sensing
• Interferometry
• Climate
• Data Science

Repository record dc:identifier

https://digitalcommons.chapman.edu/cads_dissertations/45

OAI identifier oai:identifier

oai:digitalcommons.chapman.edu:cads_dissertations-1046