Correlating Water Quality and Profile Data in the Florida Keys using Machine Learning Methods

Authors

Alejandro M. Torres Castellanos, Florida International UniversityFollow

Document Type

Thesis

Degree

Master of Science (MS)

Major/Program

Computer Science

First Advisor's Name

Dr. Leonardo Bobadilla

First Advisor's Committee Title

Committee chair

Second Advisor's Name

Dr. Mark Finlayson

Second Advisor's Committee Title

Committee member

Third Advisor's Name

Dr. Piero Gardinali

Third Advisor's Committee Title

Committee member

Keywords

Water Quality, Machine Learning, Sensing

Date of Defense

3-10-2021

Abstract

Water quality is a very active subject of research in the water science field, where its importance includes maintaining the environment, managing wastewater, and securing fresh water. However, the increase of human development has led to problems that are affecting the ecosystem. Motivated by these problems, this research aims to find a solution for understanding the coastal water of the Florida Keys. The research used machine learning methods to find a correlation between water quality dataset and profile measurements dataset. To achieve this objective, the research first went through cleaning, rescuing, and structuring a readable dataset of the profile measurements that could be used in the analysis. Once the profile measurements dataset was completed, the next step was to find the correlation. To get a correlation between two datasets, the research proposed the use of regression coefficients coming from four different measurements in the profile dataset. Then, the coefficients were clustered using k-means and an independency test was carried out on the two datasets. Lastly, the research also built a water drone in the form of an airboat, which can collect data and can be controlled through an android app.

Identifier

FIDC009561

Recommended Citation

Torres Castellanos, Alejandro M., "Correlating Water Quality and Profile Data in the Florida Keys using Machine Learning Methods" (2021). FIU Electronic Theses and Dissertations. 4693.
https://digitalcommons.fiu.edu/etd/4693