Environmental Internship Program - 2023 Booklet
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Jamie Kim ’24<br />
CIVIL AND ENVIRONMENTAL ENGINEERING<br />
Certificate: Applications of Computing<br />
WATER AND THE<br />
ENVIRONMENT<br />
102<br />
PROJECT TITLE<br />
The Interface of Hydrology<br />
and Machine Learning:<br />
Generating Better<br />
Information for Decisionmakers<br />
and Educating the<br />
Decision-makers of the<br />
Future<br />
ORGANIZATION(S)<br />
Integrated GroundWater<br />
Modeling Center (IGWMC),<br />
Department of Civil<br />
and <strong>Environmental</strong><br />
Engineering, Princeton<br />
University<br />
LOCATION(S)<br />
Princeton, New Jersey<br />
MENTOR(S)<br />
Reed Maxwell,<br />
William and Edna Macaleer<br />
Professor of Engineering<br />
and Applied Science,<br />
Professor of Civil and<br />
<strong>Environmental</strong> Engineering<br />
and the High Meadows<br />
<strong>Environmental</strong> Institute;<br />
Lisa Gallagher, Education<br />
and Outreach Specialist,<br />
IGWMC, Department of Civil<br />
and <strong>Environmental</strong><br />
Engineering<br />
Providing accurate information about our<br />
water sources is important for future decisionmakers<br />
to make wise and sustainable plans<br />
concerning water management. However,<br />
due to climate change, future environmental<br />
conditions have become further unpredictable,<br />
making it challenging to understand our water<br />
supplies. Using both physics-based and datadriven<br />
modeling in combination is essential<br />
for predicting conditions, as both modeling<br />
methods have advantages and disadvantages.<br />
To inform the integration of these methods, I<br />
analyzed the correlations between pumping<br />
data and climate condition variables and<br />
determined how groundwater pumping affects<br />
water table depth. I focused on implementing a<br />
machine learning, data-driven model that used<br />
a regression-enhanced random forest method<br />
to estimate water table depth in New Jersey. In<br />
addition, as part of the educational aspect of<br />
the internship, I helped teach at The Watershed<br />
Institute’s Watershed Academy for High School<br />
Students where I introduced the idea of pattern<br />
recognition and discussed the importance of data<br />
quantity and quality for training, all of which are<br />
fundamental to machine learning. Through this<br />
experience, I learned various practical skills in<br />
machine learning and data analysis which I hope<br />
to utilize for my senior thesis and other future<br />
work.