Bayesian Belief Networks and Score-Based Network Construction for Nitrate and Nitrite Level Prediction in Water Quality Testing of the Vaal River in South Africa

Abstract

This research addresses the essential task of monitoring water quality, particularly in accurately predicting nitrate and nitrite levels in river water. These parameters are crucial indicators of water's suitability for human consumption and ecological health. Traditional monitoring methods, such as manual sampling and expensive sensors, are often prohibitive due to their high costs and labour-intensive nature. In response to these challenges, this research leverages machine learning (ML) algorithms to more efficiently predict nitrate and nitrite levels, utilising a dataset from one water monitoring station on the Vaal River comprising 1071 data points gathered over 21 years (1995-2016). This dataset includes a variety of water quality variables such as calcium (Ca), chloride (Cl), dissolved major salts (DMS), electrical conductivity (EC), fluoride (F), potassium (K), magnesium (Mg), ammonium (NH4), sodium (Na), phosphate (PO4), sulphate (SO4), silicon (Si), total alkalinity (TAL), and temperature. This research employs a series of Score-Based Constructed Bayesian Belief Network models, including Tree-Based All-Features (AF), Hill Climb (HC) with various criteria like Bayesian Information Criterion (BIC) and Random Features (RF), and a Random Forest model with All-Features with varying number of trees, including optimisations. These models are evaluated for their ability to predict nitrate and nitrite values, aiming to provide a cost-effective and efficient alternative to traditional monitoring techniques. The research assesses each model's prediction accuracy, striving for reliable predictions of nitrate and nitrite levels with the Akaike Information Criterion and Enhanced Bayesian Information Criterion Score-Based models performing with the best accuracy and capable of predicting nitrate and nitrite with Root Mean Square Error (RMSE) on the test data of 0.356 and 0.360 respectively, Mean Square Error (MSE) on the test data of 0.127 and 0.130 respectively, Mean Absolute Error (MAE) on the test data of 0.197 and 0.197 respectively and R-squared of 0.7940 and 0.7944 respectively. The study's findings have significant implications for monitoring and managing water resources, potentially shifting the paradigm for ensuring water quality sustainably.