Predicting Water Quality Parameters in Lake Pontchartrain using Machine Learning: A comparison on K-Nearest Neighbors, Decision Trees, and Neural Networks to Predict Water Quality

Abstract

This work is about the use of machine learning methods to improve the monitoring of water quality. The work aims to use machine learning to predict the normal values of a quality indicator (pH, salinity, etc.). Upon significant deviation from actual measurements, monitoring scientists would be alerted to the need to inspect the water way more closely thereby reducing the possibility of missing a problem and speeding up determinations of issues regarding water quality. This study compares methods to predict water quality parameters using water data from Lake Pontchartrain in Southeast Louisiana. K-Nearest neighbors, decision trees, and an artificial neural network have been used to determine which method most accurately predicted water quality parameters such as pH, temperature, salinity, specific conductance, and dissolved oxygen. The decision tree and k-nearest neighbors algorithms produced similar results which were only slightly below the standard deviation of the data. However, a neural network was able to predict the values with a much higher accuracy.