Correlating Water Quality and Profile Data in the Florida Keys

Abstract

Aquatic ecosystems present complex structures susceptible to changes that can cause adverse effects in the water. These problems have turned the attention of researchers to understand them, and possibly take action to prevent further damages. This interest led to the accumulation of large amounts of data with limited personnel and resources to analyze it. An example of this is the collection of data in South Florida for 25 years by Florida International University. By making use of a depth profile and surface water quality data sets collected in the same location at the same time, a methodology is proposed to correlate these two data sets. By using Machine Learning, we represented depth profiles with coefficients followed by clustering analysis. Similarly, a water surface chemical data set was clustered using k-means. We then used statistical methods to test the connection between these two data sets.