Machine learning for water quality classification

Abstract

In the past years, there has been a lot of interest in water quality and its prediction as there are many pollutants that affect water quality. The techniques provided herein will help us in controlling and reducing the risks of water pollution. In this study, we will discuss concepts related to machine learning models and their applications for water quality classification (WQC). Three machine learning algorithms, J48, Naive Bayes, and multi-layer perceptron (MLP), were used for WQC prediction. The dataset used contains 10 features, and in order to evaluate the machine's algorithms and their performance, some accuracy measurements were used. Our study showed that the proposed models can accurately classify water quality. By analyzing the results, it was found that the MLP algorithm achieved the highest accuracy for WQC prediction as compared to other algorithms.

Prediction of Water Quality Using SoftMax-ELM Optimized Using Adaptive Crow-Search Algorithm

“…The work utilized three membership function assignments for each input variable, resulting in convincing performance. Abuzir et al [14] employed NB, MLP, and j48 ML models for classifying water quality. For this, they adopted a database comprised of ten distinct feature values.…”

Comparative Study of Algorithms Used in Water Pollution Prevention and Control

“…In a study conducted by Abuzir [15] , three different algorithms, namely J48, Plain Bayesian and MLP models were used for water quality classification. The classification accuracy of each model was analyzed and compared, taking into account the different number of features in the dataset.…”

Water Pollutant Classification Method Based on Multi-Class Support Vector Machine

“…In recent years, with the development of various classification algorithms and the continuous improvement of data collection technology, water pollution classification research has gradually become popular and mature, and many remarkable results have been achieved. Abuzir [1] used J48, naive Bayesian and MLP models to analyze and compare the classification performance of each model in the case of different feature numbers in the data set for the classification of water pollutants, and proved that the naive Bayesian algorithm can be used in small Effectiveness in sample classification. Manaf [2] used the naive Bayesian algorithm as a classifier to collect and classify the temperature, pH value and turbidity of water quality, and obtained an accuracy rate greater than 96.89%.…”