Alejandro Torres

School of Computing & Information Sciences


Lecture Information:
  • June 24, 2019
  • 11:00 AM
  • CASE 349

Speaker Bio

Alejandro Torres is a Master’s student at the School of Computing and Information Sciences and a member of CREST CAChE, Florida International University (FIU). He obtained his Bachelor’s degree and Master’s degree in Civil Engineering at FIU. His subject of interest is Artificial Intelligence, Machine Learning, Deep Learning, and Data Science.

Description

Water quality is a very active subject of research in the community, where its importance includes maintaining the environment, managing wastewater, and securing fresh water. However, due to the increase of human developments, it has led to problems that are affecting the ecosystem, and potentially affecting the general public. Such issues can be seen in the Florida Keys, where coast waters suffer from events like the red tide. This has caused an ongoing effort in understanding why these events happen since the economy of the zone is dependent on water recreation activities and the fishing market. Several studies have been conducted to forecast and monitor water quality by making use of supervised and unsupervised machine learning algorithms. However, the complexity and variety of water systems means that there is no standard methodology for implementing machine learning algorithms for their analysis.

Motivated by these problems, this proposal offers a solution for understanding the coastal water of the Florida Keys. By making use of a large dataset provided by FIU South East Environmental Research Center, where 25 years of data were cleaned and structured, this research aims to use machine learning methods to find a correlation between water quality and profile measurements to monitor the waters of the Florida Keys. To complete this project, three important objectives will be addressed: (1) Cluster the data based on spatial, temporal, and chemical properties, and find all the classes of water quality in the Florida Keys, (2) Develop a multiclass classification machine learning model that can classify new water profile data, and (3) Develop an initial hardware architecture to gather new data.