Parcel Search

Type: Undergraduate
Author(s): Ana Maria Guerrero-Campos Computer Science Aime Byiringiro Computer Science Peter Chen Computer Science Duc Toan Nguyen Computer Science Brooke Ratcliff Computer Science Maribel Vargas Computer Science
Advisor(s): Bingyang Wei Computer Science
Location: Basement, Table 9, Position 1, 11:30-1:30

Public property tax data is often presented in raw formats, making it difficult for the average user to interpret. Our client initially developed a product that provided access to Kern County property tax information only. Our project enhances accessibility by developing ParcelSearch.com, a platform that centralizes property tax data. With this rebranded system, we have expanded coverage to include Kern, Monterey, and Tulare Counties, with plans for further expansion. Users can create accounts and choose from various subscription plans to conduct property searches using multiple search criteria: owner name, parcel number, and legal descriptions. With the development of a user-friendly interface and expanded search functionalities, the platform caters to realtors, investors, and homeowners seeking property insights. This system was built using modern web technologies, including Vue.js for the frontend, Java and Spring Boot for the backend, and PostgreSQL for database management, to name a few. Future plans include expanding nationwide to create an all-encompassing and user-friendly property data platform.

Longspeeds Auto Parts E-commerce Platform

Type: Undergraduate
Author(s): Peter Ho Computer Science
Advisor(s):
Location: Basement, Table 14, Position 2, 1:45-3:45

Longspeeds is an innovative e-commerce platform designed to streamline the buying and selling of auto parts, providing a seamless experience for both individual customers and automotive businesses. The platform offers a comprehensive catalog of high-quality parts for a wide range of vehicles, from everyday cars to performance and specialty models. Leveraging modern web technologies such as Next.js, Longspeeds ensures fast performance, responsive design, and secure transactions. Key features include advanced search and filtering, user-friendly inventory management, real-time order tracking, and support for both retail and wholesale transactions. With a focus on reliability, affordability, and user satisfaction, Longspeeds aims to become a trusted destination for auto enthusiasts and mechanics alike.

Echelon: Your AI Academic Advisor

Type: Undergraduate
Author(s): Harrison Leath Computer Science
Advisor(s): Bingyang Wei Computer Science
Location: FirstFloor, Table 4, Position 2, 1:45-3:45

Academic advising presents significant challenges in both time management and complexity. Currently, students navigate between two advising options: generic online resources and personalized consultations with professors and advisors. While personalized advisement offers tailored advice, professors cannot be expected to meet with every undergraduate in their major, especially as enrollment grows, and academic advisors may lack specialized knowledge required for some majors. Echelon addresses this gap by creating a middle ground between generic and personalized advising, offering students an effective supplement and saving time for all parties involved. Echelon functions as an intelligent chatbot assistant powered by large language models such as Llama 3 and Mistral. Upon signup, students share their academic records, enabling Echelon to create individualized profiles that consider key factors such as major/minor selection and performance in critical courses. The project is being built using TypeScript and Rust with Svelte and Axum frameworks, respectively. Echelon utilizes PostgreSQL for user account and conversation storage and Qdrant for vector storage and retrieval. Designed with flexibility in mind, Echelon can be deployed at any university, given basic institutional information such as course catalogs and degree requirements.

Using AlphaFold2 to Identify Novel Drug Targets against Cryptococcus

Type: Undergraduate
Author(s): Francisco Lugo Gonzales Computer Science
Advisor(s): Natalia Castro Lopez Biology Floyd Wormley Biology

Cryptococcus is an invasive fungus that causes cryptococcosis, an infection that highly affects immunocompromised people. There are currently a limited number of antifungals available to treat Cryptococcus, and with the increased in antimicrobial resistance, we need different alternatives to treat fungal infections. Our lab has identified proteins involved in the synthesis of eicosanoids, which are lipid signaling molecules involved in regulating the immune response. Moreover, fungi can produce eicosanoids using different enzymes that humans do, opening a line to identify new drug targets using these pathways. Previously, our lab had identified genes upregulated in the presence of the eicosanoid’s precursor, arachidonic acid. Our goal is to use bioinformatics to predict and characterize the protein structure, using AlphaFold2, a machine learning application based on a deep neural network, and using this tool, identify small molecules that will bind to the proteins and help make drug design more efficient.

Fort Worth Diagnostics BioBlade

Type: Undergraduate
Author(s): Michael Nguyen Computer Science Carson Freeman Computer Science Blake Good Computer Science Harrison Leath Computer Science Kyle Stagner Computer Science Nicholas Tullbane Computer Science
Advisor(s): Bingyang Wei Computer Science
Location: SecondFloor, Table 8, Position 2, 1:45-3:45

FWDX BioBlade is a web-based system designed to automate genetic sequence comparison for Fort Worth Diagnostics (FWDX), a company specializing in high-quality diagnostic reagents. FWDX faces a significant challenge: ensuring its reagents remain effective as pathogens mutate over time. Currently, this process is manual, time-intensive, and costly, relying on external bioinformatic consultants to compare existing reagent sequences against national and international genetic databases like NCBI and GISAID.

BioBlade improves this workflow by automating sequence comparisons, detecting mutations or deletions, and generating real-time reports. This automation significantly reduces turnaround time, improves accuracy, and lowers costs, empowering FWDX scientists and regulatory personnel with timely and accurate information. Key features include:
- Automated sequence analysis for efficient reagent validation
- Customizable query intervals for up-to-date comparisons
- A user-friendly reporting dashboard for streamlined decision-making

Harnessing Vector Databases for AI and Data Search

Type: Undergraduate
Author(s): Michael Nguyen Computer Science
Advisor(s): Bo Mei Computer Science
Location: Basement, Table 13, Position 1, 11:30-1:30

As artificial intelligence and machine learning continue to evolve, the need for efficient search and retrieval mechanisms for unstructured data has grown exponentially. Traditional relational databases, optimized for structured queries, struggle with the high-dimensional nature of modern AI-generated embeddings. This challenge has led to the rise of vector databases, specialized systems designed to store, index, and retrieve data based on similarity rather than exact matching.

This symposium explores the fundamental concepts of vector databases, their key components—such as vector embeddings, indexing techniques, and similarity search algorithms—and their advantages over traditional databases. We discuss how vector search operates using distance metrics like cosine similarity and Euclidean distance and compare the roles of vector databases and standalone vector indexes.

Forecasting ILI Trends Using Machine Learning

Type: Undergraduate
Author(s): Hieu Pham Computer Science
Advisor(s): Bo Mei Computer Science
Location: FirstFloor, Table 3, Position 2, 11:30-1:30

This project presents an interactive forecasting tool for Influenza-like Illness (ILI) trends using historical CDC data and machine learning models. Built with Python and Streamlit, the app enables users to visualize yearly ILI patterns, compare predictive models, and forecast future cases based on recent trends. Three models — Linear Regression, Random Forest, and XGBoost — were evaluated using Root Mean Squared Error (RMSE) and R² Score. Surprisingly, Linear Regression achieved the best performance with an RMSE of 0.106 and R² of 0.960, indicating that simple models can be effective for this type of time-series data. The app also includes features for dynamic forecasting and CSV export, making it a practical tool for public health analysis and planning.

Trailspur Data Project

Type: Undergraduate
Author(s): Hieu Pham Computer Science Ishaan Bhagwat Computer Science Alice Nguyen Computer Science James Nogueira Computer Science Duy Pham Computer Science Carlos Prudhomme Computer Science Arushi Thakur Computer Science
Advisor(s): Wei Bingyang Computer Science
Location: Basement, Table 3, Position 1, 1:45-3:45

Our client, Trailspur Capital Partners, is a real estate investment company based in Texas. We assist the company by building a database about commercial / industrial real estate to manage the market more easily and better decision-making. The business requires both the Geographic data from the County’s officials and the properties listings with vacancies information. Our goal is to design a database that can handle the aggregate data coming from both sources, which includes arranging and categorizing the properties, coming with several built-in functions namely identifying listings / vacancy changes, before deploying everything to the server. Our frontend, built with Vite and Vue, provides a smooth and interactive user experience while on the backend, we utilize AWS Lambda with Python to automate essential tasks, including downloading official county appraisal data, performing spatial merges using GIS functions, and managing our Supabase database. After successfully aggregating real estate data from both sources into a structured database, which enables easier tracking of property status changes, the platform efficiently processes and visualizes real-time property listings, allowing our client to analyze market trends and make data-driven investment decisions. Our project enhances real estate market intelligence for Trailspur Capital Partners. The system’s automated functions minimize manual workload and improve the accuracy of property tracking, providing a scalable solution for future expansion.

MENDmate

Type: Undergraduate
Author(s): Rostyslav Shelashskyi Computer Science Amaya Harris Computer Science Vishal Seelam Computer Science Aaron Swinney Computer Science Alvie Thai Computer Science Samuel Williams Computer Science
Advisor(s): Bingyang Wei Computer Science
Location: SecondFloor, Table 2, Position 1, 1:45-3:45

Cognitive Behavioral Therapy often relies on patients consistently completing therapeutic homework, regularly assigned by their therapist. A leading cause of Cognitive Behavioral Therapy failure for patients is non-compliance with their assigned therapeutic homework. About 20%-50% of patients fail to complete assignments due to inconvenience, a lack of clear instructions, or forgetting to finish the assignment. MENDmate is an online platform designed to solve this problem by providing a streamlined user experience for homework assignment and completion. MENDmate allows providers to assign homework to their patients and monitor their progress. It also provides patients with the ability to track and complete their homework assignments. Additional features of MENDmate include a learning library that gives patients an opportunity to learn about mental illness and practice coping techniques, a journal that allows patients to record their experiences and daily mood and anxiety assessments. MENDmate also tracks and reports the patient's data trends such as completed assignment, mood level, and anxiety level, which allows both the therapist and the patient to keep track of their progress over time.

PsychWorks Report Generation System

Type: Undergraduate
Author(s): ryan smith Computer Science Roland Andrade Computer Science Ben Blake Computer Science Hien Dau Computer Science Sion Kim Computer Science Will Peck Computer Science Alexandra Teran Computer Science
Advisor(s): Bingyang Wei Computer Science
Location: FirstFloor, Table 4, Position 1, 11:30-1:30

Fort Worth PsychWorks, a leading psychiatry office, provides comprehensive neuropsychological and psychological assessments for patients across all age groups. Currently, after administering a variety of cognitive and behavioral tests, psychiatrists must manually input the resulting data into report templates, a process that is both labor-intensive and inefficient. This manual approach can take between 45 minutes to two hours per report, detracting from the time available for direct patient care and reducing the clinic’s overall operational efficiency.
To address this challenge, our senior design project introduces an automated report generation system named the PsychWorks Report Generation System. This software solution empowers psychiatrists to select or customize templates tailored to individual patient needs, add or remove specific tests, and automatically generate detailed report text, tables, and charts based on input scores. By maintaining the flexibility of the existing Excel-based system while significantly reducing the time and effort required for report creation, the PsychReport Automator enhances the user experience for clinic staff, boosts the potential for billable hours, and ultimately supports improved patient outcomes.

MotivateMe

Type: Undergraduate
Author(s): Mary Beth Walsh Computer Science Drake Do Computer Science JC Gurdian Computer Science Carolina Heredia Computer Science Kien Pham Computer Science Jailyn Ruffin Computer Science
Advisor(s): Bingyang Wei Computer Science
Location: SecondFloor, Table 6, Position 2, 1:45-3:45

Obesity disproportionately affects underserved communities due to systemic barriers such as limited healthcare access, socioeconomic challenges, and a lack of culturally relevant health resources. Under the leadership of Dr. Christina Robinson and her team of medical students—Rumaila Hussain, Kavita Patel, Joice Song, and Fatema Jafferji—we are developing a mobile health application designed to support individuals in managing their health more effectively. This app will provide users with tools to track biometrics, manage chronic conditions, and set SMART (Specific, Measurable, Achievable, Relevant, Time-bound) health goals. A key feature of the app is a motivational text messaging system that encourages users to stay engaged with their health objectives. By integrating personalized and accessible interventions, this project aims to bridge healthcare gaps and empower individuals to take proactive steps toward healthier lifestyles.

AI-Driven Personalized Education: Enhancing Learning Through Tailored Experiences

Type: Undergraduate
Author(s): nibesh yadav Computer Science
Advisor(s): Robin Chataut Computer Science
Location: Third Floor, Table 8, Position 2, 11:30-1:30

The rapid advancement of artificial intelligence (AI) presents a unique opportunity to revolutionize education through personalized learning experiences. Traditional teaching methods often fail to address the diverse learning needs of students. This research explores the application of AI in education, focusing on machine learning algorithms, intelligent tutoring systems, and adaptive learning models to create personalized educational experiences. By analyzing student data, AI can optimize learning pathways, improve comprehension, and enhance engagement. The study discusses the potential, challenges, and future directions of AI-driven personalized education.

Enhancing Power Quality in the Modern World

Type: Undergraduate
Author(s): Clarice Achola Engineering An Dinh Engineering Ashley Gutierrez Engineering Addison Hudelson Engineering Jannet Leon Padilla Engineering
Advisor(s): Morgan Kiani Engineering
Location: FirstFloor, Table 6, Position 2, 11:30-1:30

As global energy demand evolves, maintaining power quality has become a critical challenge in modern electrical systems. This research project examines key factors influencing power quality, focusing on maintaining a stable voltage magnitude and frequency across the grid. To achieve this, we explore techniques such as power factor correction and its role in improving energy efficiency and reducing costs. With the increasing integration of electric vehicles, data centers, and other high-power loads, new challenges arise in grid stability and demand management. Additionally, we investigate system overloading and transmission line considerations, addressing the risks of rising power demand and strategies for mitigating losses. Through this comprehensive study, we highlight the importance of power quality in ensuring the efficiency, reliability, and resilience of modern electrical infrastructures.

Senior Design: Smart Roller Bracket Assembly

Type: Undergraduate
Author(s): Clarice Achola Engineering Brandon Arteaga Engineering Alvaro Corona Engineering An Dinh Engineering Alec Hubbard Engineering Claire Morrison Engineering Chloe Neuyemer Engineering Reese Rivera Engineering Cameron Vieck Engineering Trent Westbrock Engineering Thomas Wilkerson Engineering Emile Zabaneh Engineering
Advisor(s): Robert Bittle Engineering
Location: Third Floor, Table 4, Position 2, 1:45-3:45

This project focuses on automating and standardizing the crowning process of a 15-foot Farnham roll form machine, used to shape aluminum parts, including fuselage and wing skins. The current crowning adjustment compensates for force imbalances caused by screws positioned at the machine’s ends and requires extensive manual shimming for optimal contact along 18 adjustable brackets. This process is detrimental to the manufacturing flow, as the time it takes to adjust the Farnham Press for different types of sheet metal or bends is long enough to significantly slow down production. To streamline this process, the project’s objectives are to design a method to measure bracket-to-material contact accurately, create an adjustable bracket system without the need for shims, and provide operators with real-time measurement feedback to optimize crowning adjustments efficiently. This will be achieved by redesigning the brackets with integrated sensors to accurately read the changing force along the beam.

Progress to date includes multiple bracket designs developed by the mechanical team, featuring adjustable mechanisms such as vertical screws, wedges, and easily insertable shims for depth control. Concurrently, the electrical team has conducted extensive research into sensor options and collaborated with sensor companies to identify suitable measurement solutions. Efforts are also underway to establish a data display interface that can provide real-time readouts from all 18 sensors, enabling operators to make informed adjustments during operation. Future work aims to explore a CNC-style interface for full control automation, which would allow streamlined adjustments for different part profiles and material thicknesses. This approach is expected to significantly reduce setup time and improve consistency in part quality.

Structural acoustic characterization of a tenor trombone

Type: Undergraduate
Author(s): William Cunningham Engineering
Advisor(s): Hubert Hall Engineering
Location: Basement, Table 1, Position 1, 1:45-3:45

An analysis of the sound-producing characteristics of a tenor trombone has been initiated at TCU. Focus of the effort will be on the model Conn 44H "Vocabell" tenor trombone due to its unique rimless bell. A numerical model of the instrument using Autodesk Inventor has been created. The model was imported into NASTRAN for further structural and acoustic analyses.

Key areas of focus include understanding the interaction between the instrument's structural vibrations and the sound radiated from the bell. The "Vocabell" design, known for its unique construction and acoustic qualities, will be critically examined to assess how its geometry and material properties influence sound production and associated frequency spectrum. Radiated sound and structural vibration measurements have been conducted on the physical instrument, providing data for model correlation and validation. Once validated, the numerical model will be used to explore more advanced concepts of brass instrument design.

The Effects of Composition, Curing and Rebar Placement on the Flexural Strength of Engineered Concrete

Type: Undergraduate
Author(s): Andrea De Leon Engineering Judah Crawford Engineering Cris Gamez Engineering Elijah Klein Engineering
Advisor(s): Jim Huffman Engineering
Location: Third Floor, Table 9, Position 1, 11:30-1:30

The engineered concrete slab is a fundamental structure in construction with its mechanical properties influenced by the rebar placement, curing process, and the ratios of its primary components aggregate, cement, and sand. This study investigates how variations in rebar placement, concrete composition and curing methods effect the flexural strength of the sample. In ENGR 30014, 18 engineering teams produced their best sample of concrete with different ratios, rebar patterns, and different types of curing. The results provide insights into optimizing the concrete ratios, rebar placement, and methods for curing and their effect on flexural strength.

Phase Light Modulation: Encryption and Light-Based Information Transmission

Type: Undergraduate
Author(s): Andrea De Leon Engineering Devin Olmedo Engineering
Advisor(s): Sue Gong Engineering
Location: FirstFloor, Table 3, Position 2, 1:45-3:45

The goal of this research was to enable information transmission through light using a Phase Light Modulation (PLM) module to decode and display encrypted information. We conducted a literature review and set up an evaluation module capable of sending encrypted messages and transmitting data without the need for optical cables. Our setup includes a laser light source, a beam expander, a Digital Micromirror Device (DMD) controlled by an electronic control board, and a laptop running the software GUI provided by Texas Instruments. We conducted various experiments with these components to optimize the design and explore potential applications. Our findings highlight the potential of this technology for future data transmission and optical devices.

Pipe Freeze Showdown: Which Plumbing Material Wins the Winter War?

Type: Undergraduate
Author(s): Ryan Golden Engineering Alec Hubbard Engineering Angel Mota Engineering Devin Olmedo Engineering
Advisor(s): James Huffman Engineering
Location: SecondFloor, Table 2, Position 2, 11:30-1:30

Winter can turn plumbing into a battlefield, with frozen pipes bursting and their joints failing under pressure. In this study, 18 teams of student researchers will face off in a school lab to test three common plumbing materials, copper, PVC, and PEX studying their joining techniques; soldering, solvent welding, and crimping against freezing conditions.
Over two weeks, we will subject a series of pipe assemblies into brutal freeze to thaw cycles, mimicking harsh winter weather, to see which ones crack, leak, or stand strong. By analyzing failure rates and durability, we aim to uncover the ultimate cold weather champion and share practical insights for homeowners and plumbers. Get ready our pipes are about to feel the chill!

Senior Capstone: Bearing Installation Assembly

Type: Undergraduate
Author(s): Addison Hudelson Engineering Jason Murphy Engineering Cameron Vieck Engineering
Advisor(s): Robbert Bittle Engineering
Location: Third Floor, Table 2, Position 2, 11:30-1:30

This design proposal outlines the development of a bearing installation and proof-load testing tool intended to streamline and enhance the bearing installation process for Aero Components. The project focuses on creating an efficient and innovative solution using hydraulic press technology, with particular attention to the requirements of staking and swaging methods for securing bearings. The proposed design utilizes the HSP-30M Baileigh Hydraulic Press, which will be customized to meet specific operational needs, such as accommodating bearing diameters up to 3 inches and applying precise deformation forces. Key features include the development of a versatile attachment system, safety enhancements, and a digital feedback mechanism to monitor and control the hydraulic pressure during both installation and testing phases. The project aims to meet performance criteria, including visual inspection standards and proof-load testing requirements, ensuring the tool’s effectiveness and repeatability. Through a comprehensive testing regimen, the system’s reliability will be validated, with results documented to confirm the tool’s ability to perform under operation conditions. The proposal also includes a detailed project timeline, budget projections, and cost-management strategies, ensuring the project will be completed on time and within budget. The ultimate goal is to provide Aero Components with a tailored solution that optimizes bearing installation efficiency while maintaining high standards of safety, precision, and performance.

Sustainable Bricks for a Better Tomorrow

Type: Undergraduate
Author(s): Zac Schmitt Engineering London Bachelet Engineering
Advisor(s): James Huffman Engineering
Location: FirstFloor, Table 5, Position 1, 11:30-1:30

The growing environmental concern surrounding plastic waste has prompted the exploration of innovative recycling and reusing methods. This research investigates the potential of utilizing high-density polyethylene (HDPE) and low-density polyethylene (LDPE) plastic waste to create sustainable bricks. Building on the work of Gjenge Makers, who have developed pavers from recycled plastic and sand, this study aims to evaluate the strength, durability, and environmental impact of plastic-sand composites and assess their viability as a substitute for conventional construction materials.

Structural Integrity of Reinforced Concrete

Type: Undergraduate
Author(s): Zac Schmitt Engineering
Advisor(s): James Huffman Engineering
Location: SecondFloor, Table 1, Position 3, 1:45-3:45

This study evaluates the structural integrity of reinforced concrete by comparing the mechanical properties of steel and fiberglass rebar. The primary objective is to assess the differences in material performance, performing compressive and flexural tests to quantify the ductility, load-bearing capacity, and durability of each rebar type under stress. The expected outcome is to determine the viability of fiberglass rebar as an effective alternative to traditional steel, particularly in terms of its mechanical performance and long-term reliability.

Seeing Double: A Bifurcated Cable’s Guide to Fluorescence Detection

Type: Undergraduate
Author(s): Anna Tucci Engineering Ugur Topkiran Physics & Astronomy
Advisor(s): Anton Naumov Physics & Astronomy
Location: Third Floor, Table 4, Position 3, 1:45-3:45

Graphene quantum dots (GQDs) have emerged as a promising platform for drug delivery and bioimaging due to their nanoscale size, water solubility, biocompatibility, and fluorescence properties. When functionalized, they enable both therapeutic delivery and real-time tracking in biological systems. This study focuses on the engineering of an optical system designed to cost effectively perform ex vivo spectra collection of GQDs. We utilized a bifurcated fiber optic cable connected to a laser and spectrometer, enabling simultaneous excitation and signal collection through a single optical path. Because excitation and collection occurred at the same angle rather than the conventional 90-degree configuration, a high optical density 840 nm long pass emission filter is utilized to optimize signal collection and minimize scattering. The system's cheap and easy to build design offers a streamlined method for studying nanomaterial-based therapeutics, providing a foundation for future advancements in biomedical imaging.

The Art of Grain Structure: Recognizing Shapes and Patterns in Materials

Type: Undergraduate
Author(s): Abigail Venegas Engineering Kevin Guajardo Engineering Monica Lopez Engineering Damilare Olukosi Engineering
Advisor(s): Jim Huffman Engineering
Location: SecondFloor, Table 7, Position 1, 1:45-3:45

This study aims to educate participants about the formation and significance of grain structures in metals, focusing on the processes by which grains form and how these structures influence material properties. Using 1018 steel (low-carbon), 1045 steel (medium-carbon), 1080 steel (medium-carbon), ductile and grey cast iron, and PbSn (lead-tin) samples, 18 teams explored the random formation of grain structures through a series of preparatory steps, including mounting, grinding, polishing, etching, and hardness testing. Each team examined their samples at four magnifications to identify microstructural features and measure grain size using two different methods. In addition to the technical analysis, the teams focused on uncovering the artistic patterns that emerge from the randomness of grain formation. The study will highlight the art found in these naturally occurring structures, demonstrating how materials science and art intersect. By the end, participants gain an understanding of grain theory and microstructural analysis while also developing an appreciation for the unexpected artistic forms created by these random processes in materials like steel, cast iron, and lead-tin alloys.

Assessing the Impact of Land Use Change on Water Quality in Lake Worth, Texas

Type: Graduate
Author(s): Portia Asare Environmental Sciences
Advisor(s): Environmental Sciences Esayas Gebremichael Geological Sciences
Location: SecondFloor, Table 7, Position 3, 11:30-1:30

Rapid urbanization in the Dallas-Fort Worth metropolitan area is increasing pressure on water resources, including Lake Worth. This project will investigate the relationship between land use, land cover change, and water quality degradation in Lake Worth, a reservoir facing increasing development pressure near Fort Worth. The project will use historical land data to quantify land use/land cover change (LULC) within the watershed between 2000 and 2023. This land use data will be integrated with the publicly available water quality data (nutrients, dissolved oxygen, pH, turbidity) from the Surface Water Quality Monitoring Program and locations of permitted industrial discharge points from the Texas Commission on Water Quality. GIS techniques, including spatial joins, buffer analysis, and statistical modeling (regression, hotspot analysis), will be used to analyze the correlation between LULC and water quality parameters and identify pollution hotspots. The expected outcomes include detailed land use maps, a geodatabase of water quality and discharge points, statistical models quantifying the land use-water quality relationship, and identifying areas requiring management intervention. The study's findings will inform land use planning, water resource management, and sustainable urban development practices in the region while acknowledging limitations related to data availability, spatial resolution, causality, and model generalizability.

The Effect of Urban Development-Driven Tree Removals on Land Surface Temperature in a Growing City

Type: Graduate
Author(s): Hannah Buckhalter Environmental Sciences
Advisor(s): Brendan Lavy Environmental Sciences