Water Quality Prediction Using KNN Imputer and Multilayer Perceptron

Abstract

The rapid development to accommodate population growth has a detrimental effect on water quality, which is deteriorating. Consequently, water quality prediction has emerged as a topic of great interest during the past decade. Existing water quality prediction approaches lack the desired accuracy. Moreover, the available datasets have missing values, which reduces the performance efficiency of classifiers. This study presents an automatic water quality prediction method that resolves the issue of missing values from the data and obtains a higher water quality prediction accuracy. This study proposes a nine-layer multilayer perceptron (MLP) which is used with a K-nearest neighbor (KNN) imputer to deal with the problem of missing values. Experiments are performed, and performance is compared with seven machine learning algorithms. Performance is further analyzed regarding two scenarios: deleting missing values and the use of a KNN imputer to deal with missing values. Results suggest that the proposed nine-layer MLP model can achieve an accuracy of 0.99 for water quality prediction with the KNN imputer. K-fold cross-validation further corroborates this performance.