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, Naïve 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.

  • Machine learning concept was adopted to analyze the water quality.

  • The accuracy of multi-layer perceptron (MLP), is higher than other machine learning algorithms for water quality classification (WQC).

  • Extraction of useful and relevant features increases classification accuracy using principal component analysis (PCA).

  • The PCA was used for dimensionality reduction and extracts the most dominant water quality features.

Graphical Abstract

Graphical Abstract
Graphical Abstract
     
  • AI

    artificial intelligence

  •  
  • ANN

    artificial neural network

  •  
  • EC

    electrical conductivity

  •  
  • GA

    genetic algorithm

  •  
  • IoT

    Internet of Things

  •  
  • K-NN

    K-nearest neighbor

  •  
  • MLP

    multi-layer perceptron

  •  
  • PCA

    principal component analysis

  •  
  • PRC

    precision–recall curve

  •  
  • ROC

    receiver operating characteristic curve

  •  
  • SAR

    sodium absorption ratio

  •  
  • SVM

    support vector machine

  •  
  • TDS

    total dissolved solids

  •  
  • TH

    total hardness

  •  
  • TOC

    total organic carbon

  •  
  • WQC

    water quality classification

  •  
  • WQI

    water quality index

  •  
  • WQS

    water quality status

Water is one of the most important natural resources on which the planet depends, as it constitutes 71% of the Earth's area. Water is of great importance to the life of everything on earth, as we know that all living creatures cannot live without water. Water is the basis of human, animal, and plant life, and its use is not only limited to drinking but is also considered as an important resource in industry, agriculture, and global trade through the seas and oceans. Because of the importance of water for human life, research has focused on water quality and its preservation from pollution based on international standards (Cosgrove & Loucks 2015) in order for pollution not to exceed the standard limits and threaten human life and living creatures with disease or death.

Specific quality standards are available to indicate the quality of different water sources, such as groundwater, springs, rivers, lakes, and streams, and there are specific water quality criteria for agricultural, industrial, human, or other water uses. For instance, drinking water should be fresh and unpolluted, while irrigation water should not be too saline and toxic, and water standards for industrial uses have different characteristics depending on the nature of the industrial processes. The different practices and activities of humans, industrial processes, and natural processes affect the quality of water resources in a significant and alarming manner, especially for humans (Jury & Vaux 2005; WHO 2011; Adebesin et al. 2018). These activities and practices will cause pollution by leaving most wastes and pollutants without adequately being treated. This, in turn, affects natural water resources. Industrial plants and vehicles cause acidic conditions to develop in surface water and groundwater sources by lowering pH levels, decreasing acid-neutralizing capacity, and increasing aluminum concentrations (Jury & Vaux 2005). This then affects precipitation, surface water, and groundwater, as well as degrading ecosystems (Abuzir & Abuzir 2021). Sewage and runoff from farms, farmlands, and gardens can contain pesticides and nutrients, such as nitrogen and phosphorus, that cause excessive aquatic plant growth. These pollutants, which enter the water body through various channels, have become a great source of various dangers to the environment (WHO 2011; Adebesin et al. 2018).

Water quality is determined by features such as pH value, hardness caused by calcium and magnesium salts, total dissolved solids (TDS), chloramines, sulfate, electrical conductivity (EC), total organic carbon (TOC), turbidity, and trihalomethanes. Machine learning algorithms first need to pre-process the data and manage the missing data using weka.filters.unsupervised.attribute.ReplaceMissingValues, feature correlation, apply classification machine learning, and analyze the value of the feature selection (Islam Khan et al. 2021).

Water is one of the most vital resources for the sustainability of life on earth. There are many laboratory tests in research centers and universities to check the quality of water. In these research centers, water quality is assessed through laboratory and statistical analyses that are time-consuming and expensive. These analyses require the collection of samples, transportation to laboratories for examination, and a great deal of time and calculation. The results of these tests are very important to detect whether the water is contaminated with water quality features or not. These conditions call for covert treatment to detect water pollution in a faster and cheaper way (Thienen et al. 2018). The primary contribution of the current study is analyzing the performance of machine learning algorithms in predicting the water quality classification (WQC). Three machine learning algorithms, namely J48, Naïve Bayes, and multi-layer perceptron (MLP), were applied to predict the WQC. Performing measurement analysis using metrics, such as root mean squared error, recall, precision, receiver operating characteristic (ROC) area, and precision–recall curve (PRC) area, was used in this investigation.

In this study, we presented the topics according to the following structure: first, the background and the problem are presented in the section ‘Literature Review’, followed by the relevant literature in the section ‘Materials and Methods’, and classification of the data to estimate the state of water quality features based on machine learning algorithms in the section ‘Results Analysis and Discussion’. Next, we used these models to analyze and utilize them as a tool to aid in interpreting our results and decision-making and concluded with a discussion of the usefulness of the methodology and tools developed in this study.

Water is one of the most important elements for the existence of life. Drinking water safety and accessibility are urgent issues around the world. There is extensive work on using machine learning in the water quality index (WQI), WQC, and wastewater treatment (Asadi et al. 2017; Szeląg et al. 2017; Güller et al. 2019; Qiu et al. 2021). In the study of Hassan et al. (2021), various machine learning techniques such as random forests (RF), neural network (NN), multinomial logistics regression (MLR), support vector machine (SVM), and bagged tree models (BTM) have been applied to classify a dataset of water quality in India. Their results showed that nitrate, pH, conductivity, dissolved oxygen (DO), total coliform (TC), and biological oxygen demand (BOD) are the main features that affect WQC.

Ahmed et al. (2019) used three different machine learning algorithms such as gradient boosting, MLP, and polynomial regression to predict water quality. They used four different features, namely, pH, TDS, temperature, and turbidity. The results showed that MLP has the highest classification accuracy of 85.07%, with a configuration of (3, 7). Aldhyani et al. (2020) used three different machine learning algorithms to predict WQC, namely SVM, K-nearest neighbor (K-NN), and Naïve Bayes. The result showed that the SVM algorithm has the highest classification accuracy of 97.01%. In order to determine the WQI, they used two artificial intelligence (AI) techniques, namely, nonlinear autoregressive neural network (NARNET) and long short-term memory (LSTM). The NARNET technique showed slightly better performance than the LSTM.

The study by Sillberg et al. (2021) used a machine learning algorithm called the SVM algorithm and attribute realization (AR) to classify the water quality of the Chao Phraya River. Their results showed that AR-SVM has achieved 0.86–0.95 accuracy when applying three to six features to classify river water's quality. The study by Azad et al. (2017) used machine learning techniques to select the quality features mentioned in their dataset for the Gorganroud River water. They used the following three machine learning algorithms: ant colony optimization for continuous domains (ACOR), genetic algorithm (GA), and adaptive neuro-fuzzy inference system (ANFIS) for evaluating the quality features of the Gorganroud River water. The ANFIS model showed the best performance in the prediction of the features (EC, sodium absorption ratio (SAR), and total hardness (TH)) mentioned in the training stage.

Kakkar et al. (2021) collected data using the Internet of Things (IoT) and employed a machine learning technology neural network to forecast the amount of water pollution in residential overhead tanks. Khan et al. (2021) utilized the principal component regression (PCR) technique to select the most dominant WQI features. Regression algorithms are used for the principal component analysis (PCA) output and utilized gradient boosting classifiers to classify the water quality status (WQS). The study by Lerios & Villarica (2019) aimed to utilize data mining techniques for pattern extraction and model prediction of water quality in water reservoirs using different features and the WQI. The result indicated that the WQI was mostly in fair and marginal rank, providing an indication that water quality was being threatened by different water pollutants. The main objective of the study by Solanki et al. (2015) was to use deep learning for accurate predictions of water quality features using the WEKA tool. The evaluation in their approach was based on metrics, such as mean absolute error and mean square error, to examine the error rate of prediction.

In this paper, machine learning algorithms were used to classify water quality with a model based on several algorithms. In the following paragraphs, we explained the appropriate methodology to implement our approach (Figure 1):

  • Data collection: the main step in machine learning manipulation is collecting data in digital format. We can utilize it by linking the features of the data to make predictions and interpretations of our results based on our machine learning algorithms.

  • Missing values replacement: during data preparation, it is common to replace all missing values for nominal and numeric features in a dataset with the modes and means from the training data.

  • Normalization: in this method, we normalized all number values in the dataset.

  • Feature selection: we used the two techniques, Parsons’ analysis and PCA, to extract significant features from the dataset.

  • Data split: the dataset is divided into two sets: training and testing with 10-folds for cross-validation.

  • Statistical analysis of the features: this method describes the significant distribution and correlation coefficient between the features of the dataset.

  • Dimension reduction: this method is used for the selection of the most important features for WQC.

  • Classification algorithms: we used different machine learning algorithms including J48, Naïve Bayes, and MLP.

  • Evaluation: significant measurement methods are used in order to evaluate our machine learning algorithms.

  • The following algorithms were employed in our study such as Decision Tree, Naïve Bayes, and MLP.

Figure 1

Framework of the proposed approach.

Figure 1

Framework of the proposed approach.

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A Decision Tree (DS) is a supervised machine learning technique where the input data (X) and the output or class label (terminal node) are in the training data. It can be used in machine learning applications for both classification and regression. The entropy is used to determine the root variable and, accordingly, is oriented towards the values of other features. We do not know if the parent entropy or the entropy of a particular node has decreased or not. In order to find that, we used a new metric called ‘information gain,’ which tells us how much the parent entropy has decreased after splitting it with some features. We can calculate entropy which is the amount of uncertainty in the dataset and information gain by using the following formula:
formula
(1)
where S is the subset of the training example and p is the probability that the tuple belongs to class C.
The information required for exact classification after portioning is given by the formula:
formula
(2)
where P(c) is the weight of partition. This information represents the information needed to classify the dataset D on portioning by X.
Information gain is the difference between the original and expected information that is required to classify the tuples of dataset D.
formula
(3)
Naïve Bayes is a probabilistic classifier based on the Bayes theorem and is used in machine learning applications that require classification tasks. Simplicity, speed, accuracy, and reliability are among the features of Naïve Bayes. This theorem is used to calculate conditional probabilities. There are different Naïve Bayes, such as Multinomial Naïve Bayes Classifier, Bernoulli Naïve Bayes Classifier, and Gaussian Naïve Bayes Classifier. The general form is
formula
(4)

Given Xj are conditionally independent given Y.

An MLP is an artificial neural network (ANN) algorithm used for classification and regression. It is composed of multiple layers of perceptrons with a threshold activation function. The architecture of MLP consists of three layers, two of them are visible layers, the input layer and output layer, and the hidden layers.

The algorithm of MLP iterates using training data and generalizes the output model by calculating and updating the weight on each node of each layer. For classification or prediction, we use the training model with weights to decide what units to activate based on the input (Figure 2).

Figure 2

An MLP architecture based on our model.

Figure 2

An MLP architecture based on our model.

Close modal

We can apply the different machine learning algorithms and our framework to predict water quality in different countries as water bodies can be very different. Our predictive and classification approach could be applied to datasets from different countries.

Dataset

To achieve the process of building a machine learning model, we used the water_potability.csv file that contains water quality metrics for 3,276 different water bodies. Available, comprehensive, and rigorous water quality datasets are key in conducting machine learning application techniques for the purpose of scientific research. The data are comprehensive and used for research purposes, especially in machine learning applications. The dataset was downloaded from Kaggle (https://www.kaggle.com/adityakadiwal/water-potability). There are 10 features in the dataset: pH value, hardness caused by calcium and magnesium salts, TDS, chloramines, sulfate, EC, TOC, turbidity, trihalomethanes, and potability. The classification feature is potability with {0,1} values. Table 1 shows these features with basic statistical analysis.

Table 1

Basic statistical analysis for water quality features

FeatureUnit WHOMinMaxMeanSD
pH 6.52–6.83 14 7.081 1.47 
Hardness Hardness caused by calcium and magnesium salts 47.432 323.124 196.369 32.88 
TDS 500–1,000 mg/L 320.943 61,227.196 22,014.093 8,768.571 
Chloramines 4 mg/L or 4 ppm 0.352 13.127 7.122 1.583 
Sulfate 3–30 mg/L 129 481.031 333.776 36.143 
EC 200–400 μS/cm 181 753.343 426.205 80.824 
TOC EPA <2 mg/L as TOC in treated/drinking water and <4 mg/L in source water which is use for treatment 2.2 28.3 14.285 3.308 
Trihalomethanes 5.00 NTU 0.738 124 66.396 15.77 
Turbidity THM levels up to 80 ppm 1.45 6.739 3.967 0.78 
Potability 0 or 1     
FeatureUnit WHOMinMaxMeanSD
pH 6.52–6.83 14 7.081 1.47 
Hardness Hardness caused by calcium and magnesium salts 47.432 323.124 196.369 32.88 
TDS 500–1,000 mg/L 320.943 61,227.196 22,014.093 8,768.571 
Chloramines 4 mg/L or 4 ppm 0.352 13.127 7.122 1.583 
Sulfate 3–30 mg/L 129 481.031 333.776 36.143 
EC 200–400 μS/cm 181 753.343 426.205 80.824 
TOC EPA <2 mg/L as TOC in treated/drinking water and <4 mg/L in source water which is use for treatment 2.2 28.3 14.285 3.308 
Trihalomethanes 5.00 NTU 0.738 124 66.396 15.77 
Turbidity THM levels up to 80 ppm 1.45 6.739 3.967 0.78 
Potability 0 or 1     

Data normalization

Normalization is used as an initial stage in order to prepare the data for machine learning. This process will change the values of numeric features in the dataset to be on a similar scale, without distorting differences in value ranges or losing information. Therefore, the new scale will make the largest value for each feature equal to 1 and the smallest value equal to 0 (Figure 3). In general, we apply the concept of normalization when we do not know the distribution of the data or when we know that the distribution is not a normal one. In machine learning, we called min–max normalization (linear scaling), the formula is given below
formula
(5)
Figure 3

Normalization of the dataset.

Figure 3

Normalization of the dataset.

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Feature selection

Feature selection is divided into two parts:

  • Features relationship: an evaluation method for evaluating each feature in the dataset based on the output variable and the relationship that the features have with each other. In our study, we used Pearson and PCA to perform this stage in order to achieve a shortlist of the selected features.

  • Test method: to test the selected features using the different machine learning algorithms.

Using the Pearson correlation coefficient, we can calculate the correlation between each feature and the output variable and the order of the correlation. Based on the calculations, we only identify features that have a high positive or negative correlation (close to −1 or 1), i.e., features that are closely related to the output variable whether it is positive or negative correlation. For those features with low correlation (the value is close to zero), we can just drop them.

Experimental setup

The aim of this research is to detect or determine water quality using historical data from Kaggle. Three machine learning algorithms were adopted in order to achieve the results. These algorithms are J48, Naïve Bayes, and MLP. This section discusses experiments, results, and evaluation of our approach. In order to implement our model, we used the data mining tool WEKA 3.9.

Data analysis

After performing all the basic operations we needed to manipulate the data with the aim of analyzing it. In our research, we applied three machine learning algorithms to determine WQC using all features and the fewest or specified number of features depending on the feature selection techniques. Before applying the machine learning algorithms, in the early steps of data preparation, we used correlation analysis, data segmentation, and feature selections to prepare the data as input to the three machine learning algorithms.

Correlation analysis

In the early steps of our model, we filled in the missing values and applied a normalization algorithm to our dataset. The number of the features in our dataset is 10, and not all of these features are related and useful for predicting the classification label (output feature). In machine learning, the feature selection task can be useful in finding dependent features in our model. Correlation analysis can be used in order to reduce the dimension of the data and infer the possible relationships between the input features. In this work, we analyzed and applied the Pearson correlation as a measure to identify and select the relevant features from the list of features in Table 1. We removed the irrelevant features and applied our model using the values of the selected features.

The purpose of correlation analysis is to measure, analyze, and determine the degree of the relationship between two features and the dependence between features. In order to determine the relationship between the features, we used decimal values, known as the correlation coefficient. If the value of the correlation coefficient is greater than 0 (positive sign), this indicates that the relationship between the features is significant. The negative value of the correlation coefficient indicates that the relationship between the features is weak. The correlation coefficient of a value of +1 indicates that the two features have a strong correlation. On the other hand, the correlation coefficient of a value of −1 indicates otherwise. There is no correlation if the correlation coefficient is 1.

This relationship can be used to identify the strongest features that can have a significant effect (a strong predictor) and more efficiently predict the outcomes. In this research, we used two measurements, namely, Pearson's coefficient and PCA, to find the relevant features and the relationships between the features themselves and the labeled feature. These techniques are often used to determine the strong predictor for water quality features.

Pearson's correlation coefficient can be calculated using the following formula:
formula
(6)
where Cx,y is the correlation coefficient, Covariance (x,y) is the covariance, and σx and σy are the standard deviations of x and y, respectively. is the value of the x-variable in a sample, is the mean of the values of the x-variable, is the value of the y-variable in a sample, and is the mean of the values of the y-variable.

Weka can perform the correlation-based feature selection (Pearson's correlation coefficient) using the CorrelationAttributeEval. It requires the use of a Ranker search method. Table 2 shows our results by using the different methods. If we use 0.01 as our cut-off (threshold value) for relevant features, then we only keep TDS, TOC, chloramines, sulfate, and hardness caused by calcium and magnesium salts. The remaining features could be removed (Table 3).

Table 2

Ranked features based on Pearson's correlation coefficient

Feature numberFeaturePearson's correlation coefficient
Solids 0.03374 
Organic_carbon 0.03 
Chloramines 0.02378 
Sulfate 0.02062 
Hardness 0.01384 
Conductivity 0.00813 
Trihalomethanes 0.00696 
pH 0.00329 
Turbidity 0.00158 
Feature numberFeaturePearson's correlation coefficient
Solids 0.03374 
Organic_carbon 0.03 
Chloramines 0.02378 
Sulfate 0.02062 
Hardness 0.01384 
Conductivity 0.00813 
Trihalomethanes 0.00696 
pH 0.00329 
Turbidity 0.00158 
Table 3

Feature selection using Pearson's correlation coefficient

MethodsThreshold valueFeatures
Pearson's correlation coefficient Threshold value = 0.01 TDS, TOC, chloramines, sulfate, and hardness caused by calcium and magnesium salts. 
 Threshold value = 0.02 TDS, TOC, chloramines, and sulfate. 
 Threshold value = 0.03 TDS and TOC. 
MethodsThreshold valueFeatures
Pearson's correlation coefficient Threshold value = 0.01 TDS, TOC, chloramines, sulfate, and hardness caused by calcium and magnesium salts. 
 Threshold value = 0.02 TDS, TOC, chloramines, and sulfate. 
 Threshold value = 0.03 TDS and TOC. 

In machine learning, the PCA is applied in dimensionality reduction (DR). PCA is a common technique used in feature selection to reduce the dimension of the features by keeping and considering only the relevant six features. The widely used official methods to calculate the principal components (PCs) are based on solving the covariance matrix, eigen values, and eigen vector or using singular value decomposition (SVD). As a better approach, we applied the PCA before fitting our data to a model. Table 4 shows the correlation between the features themselves.

Table 4

Correlation coefficient between the input features of WQC (correlation matrix PCA)

FeaturespHHardnessTDSChloraminesSulfateECTOCTrihalomethanesTurbidity
pH 0.08   − 0.08   − 0.03 0.01 0.02 0.04 − 0.04 
Hardness 0.08 − 0.05 − 0.03   − 0.09 − 0.02   − 0.01 − 0.01 
TDS − 0.08 − 0.05 − 0.07 − 0.15 0.01 0.01 − 0.01 0.02 
Chloramines − 0.03   − 0.03   − 0.07  0.02 − 0.02 − 0.01 0.02  0 
Sulfate 0.01 − 0.09 − 0.15 0.02 − 0.01 0.03 − 0.03 − 0.01 
EC 0.02 − 0.02 0.01 − 0.02 − 0.01 0.02 0.01 
TOC 0.04 0.01 − 0.01 0.03 0.02 − 0.01 − 0.03 
Trihalomethanes − 0.01 − 0.01 0.02 − 0.03 − 0.01 − 0.02 
Turbidity − 0.04 − 0.01 0.02 − 0.01 0.01 − 0.03 − 0.02 
FeaturespHHardnessTDSChloraminesSulfateECTOCTrihalomethanesTurbidity
pH 0.08   − 0.08   − 0.03 0.01 0.02 0.04 − 0.04 
Hardness 0.08 − 0.05 − 0.03   − 0.09 − 0.02   − 0.01 − 0.01 
TDS − 0.08 − 0.05 − 0.07 − 0.15 0.01 0.01 − 0.01 0.02 
Chloramines − 0.03   − 0.03   − 0.07  0.02 − 0.02 − 0.01 0.02  0 
Sulfate 0.01 − 0.09 − 0.15 0.02 − 0.01 0.03 − 0.03 − 0.01 
EC 0.02 − 0.02 0.01 − 0.02 − 0.01 0.02 0.01 
TOC 0.04 0.01 − 0.01 0.03 0.02 − 0.01 − 0.03 
Trihalomethanes − 0.01 − 0.01 0.02 − 0.03 − 0.01 − 0.02 
Turbidity − 0.04 − 0.01 0.02 − 0.01 0.01 − 0.03 − 0.02 

For PCA, positive values greater than 0 show correlation, while negative values less than 0 represent no correlation between the features. As the magnitude of the positive values increases they were more strongly correlated with the other features. The analysis of the correlation values in Table 4 indicates that:

  • pH is not strongly or poorly related with hardness, sulfate, EC, and TOC and not correlated with TDS, chloramines, and turbidity.

  • Hardness is weakly correlated with pH and very loosely related with the other features.

  • TDS is correlated with EC, TOC, and turbidity and loosely correlated with pH, hardness, TDS, chloramines, sulfate, and trihalomethanes.

  • Chloramines are correlated with sulfate and trihalomethanes and is not correlated with pH, hardness, TDS, chloramines, EC, TOC, and turbidity.

  • Sulfate is correlated with chloramines and trihalomethanes and is not correlated with pH, hardness, TDS, sulfate, EC, TOC, and turbidity.

  • EC is correlated with pH, TDS, TOC, and turbidity and is not correlated with hardness, chloramines, sulfate, EC, and trihalomethanes.

  • TOC is correlated with pH, TDS, sulfate, and EC and is not correlated with hardness, chloramines, TOC, trihalomethanes, and turbidity.

  • Trihalomethanes are correlated with chloramines and is not correlated with pH, hardness, TDS, sulfate, EC, TOC, trihalomethanes, and turbidity.

  • Turbidity is correlated with TDS and EC and is not correlated with pH, hardness, chloramines, sulfate, TOC, trihalomethanes, and turbidity.

To illustrate the machine learning models in this study, we used a water quality dataset and 10-fold cross-validation. The resulting training set consisted of 3,276 different water bodies, 1,278 of them classified as non-potability and the rest 1,998 as potability. The performance measure for evaluating the models was the different measurements as root mean squared error (RMSE), precision, recall, F-measure, area under the ROC curve, and PRC area. The previously mentioned methods and measurements can be calculated using the following formulas:
formula
(7)
formula
(8)
formula
(9)
formula
(10)
An ROC curve is a plot of the false positive rate (TPR) on the x-axis and the true positive rate on the y-axis. The term referred to as the sensitivity or the recall.
formula
(11)
formula
(12)

We can think of the plot as the fraction of correct predictions for the positive class (y-axis) versus the fraction of errors for the negative class (x-axis).

To achieve our goal, we used three machine learning methods to classify water quality classes based on the datasets. We utilized J48, Naïve Bayes, and MLP to achieve our model. We applied each machine learning algorithm with the complete list of features and after applying feature selection. Initially, all the features were considered (Table 5). We used ROC area as the accuracy measurement for utilizing these algorithms. Among these algorithms, MLP performed better than the other and has the highest accuracy of 0.661 for ROC and 0.734 for PRC, while Naïve Bayes has 0.589 for ROC and 0.684 for PRC. The J48 algorithm has the lowest accuracy with 0.565 for ROC and 0.652 for PRC. We also obtained the same order of accuracy when we applied feature selection in our model when we used four to five features in our model (Tables 6 and 7), whereas Naïve Bayes performed better in prediction or classification of WQC (Table 8).

Table 5

Classification results using all features

ML algorithmFeature selectionCorrectly classified instancesIncorrectly classified instancesRMSEPrecisionRecallF-measureROC areaPRC area
J48 All features 2,074
63.3089% 
1,202
36.6911% 
0.49 0.637 0.928  0.755 0.565 0.652 
Naïve Bayes All features 2,040
62.2711% 
1,236
37.7289% 
0.4841 0.639 0.878 0.740 0.589 0.684 
MLP All features 2,172
66.3004% 
1,104
33.6996% 
0.4712 0.673 0.869 0.759 0.661 0.734 
ML algorithmFeature selectionCorrectly classified instancesIncorrectly classified instancesRMSEPrecisionRecallF-measureROC areaPRC area
J48 All features 2,074
63.3089% 
1,202
36.6911% 
0.49 0.637 0.928  0.755 0.565 0.652 
Naïve Bayes All features 2,040
62.2711% 
1,236
37.7289% 
0.4841 0.639 0.878 0.740 0.589 0.684 
MLP All features 2,172
66.3004% 
1,104
33.6996% 
0.4712 0.673 0.869 0.759 0.661 0.734 
Table 6

Classification results using five features

ML algorithmFeatures selectionCorrectly classified instancesIncorrectly classified instancesRMSEPrecisionRecallF-measureROC areaPRC area
J48 Threshold value = 0.01 2,041
62.3016% 
1,235
37.6984% 
0.4874 0.626 0.948 0.754 0.534 0.631 
Naïve Bayes Threshold value = 0.01 2,028
61.9048% 
1,248
38.0952% 
0.4861 0.636 0.877 0.737 0.576 0.659 
MLP Threshold value = 0.01 2,046
62.4542% 
1,230
37.5458% 
0.4857 0.629 0.937 0.753 0.568 0.650 
ML algorithmFeatures selectionCorrectly classified instancesIncorrectly classified instancesRMSEPrecisionRecallF-measureROC areaPRC area
J48 Threshold value = 0.01 2,041
62.3016% 
1,235
37.6984% 
0.4874 0.626 0.948 0.754 0.534 0.631 
Naïve Bayes Threshold value = 0.01 2,028
61.9048% 
1,248
38.0952% 
0.4861 0.636 0.877 0.737 0.576 0.659 
MLP Threshold value = 0.01 2,046
62.4542% 
1,230
37.5458% 
0.4857 0.629 0.937 0.753 0.568 0.650 
Table 7

Classification results using four features

ML algorithmFeatures selectionCorrectly classified instancesIncorrectly classified instancesRMSEPrecisionRecallF-measureROC areaPRC area
J48 Threshold value = 0.02 2,041
62.3016% 
1,235
37.6984% 
0.4873 0.625 0.953 0.755 0.533 0.629 
Naïve Bayes Threshold value = 0.02 2,036
62.149% 
1,240
37.851% 
0.4856 0.634 0.895 0.743 0.562 0.651 
MLP Threshold value = 0.02 2,071
63.2173% 
1,205
36.7827% 
0.484 0.633 0.942 0.757 0.568 0.647 
ML algorithmFeatures selectionCorrectly classified instancesIncorrectly classified instancesRMSEPrecisionRecallF-measureROC areaPRC area
J48 Threshold value = 0.02 2,041
62.3016% 
1,235
37.6984% 
0.4873 0.625 0.953 0.755 0.533 0.629 
Naïve Bayes Threshold value = 0.02 2,036
62.149% 
1,240
37.851% 
0.4856 0.634 0.895 0.743 0.562 0.651 
MLP Threshold value = 0.02 2,071
63.2173% 
1,205
36.7827% 
0.484 0.633 0.942 0.757 0.568 0.647 
Table 8

Classification results using two features

ML algorithmFeatures selectionCorrectly classified instancesIncorrectly classified instancesRMSEPrecisionRecallF-measureROC areaPRC area
J48 Threshold value = 0.03 1,998
60.989% 
1,278
39.011% 
0.4878 0.610 1.000 0.758 0.499 0.499 
Naïve Bayes Threshold value = 0.03 1,984
60.5617% 
1,292
39.4383% 
0.488 0.611 0.976 0.751 0.512 0.619 
MLP Threshold value = 0.03 1,999
61.0195% 
1,277
38.9805% 
0.4893 0.611 0.994 0.757 0.509 0.620 
ML algorithmFeatures selectionCorrectly classified instancesIncorrectly classified instancesRMSEPrecisionRecallF-measureROC areaPRC area
J48 Threshold value = 0.03 1,998
60.989% 
1,278
39.011% 
0.4878 0.610 1.000 0.758 0.499 0.499 
Naïve Bayes Threshold value = 0.03 1,984
60.5617% 
1,292
39.4383% 
0.488 0.611 0.976 0.751 0.512 0.619 
MLP Threshold value = 0.03 1,999
61.0195% 
1,277
38.9805% 
0.4893 0.611 0.994 0.757 0.509 0.620 

The ROC curve is only defined for binary classification problems. If we have a score of around 0.5 that means the system is essentially randomly guessing. Anything above 0.5 means that the system is performing better than random guessing. Anything below means the system is not classifying correctly. When we iterated through our results, we can see that J48 algorithms do not classify when the number of features is reduced to two features (Table 8) in our model (Figure 4).

Figure 4

Representation of ROC area with all features and 4–5 features.

Figure 4

Representation of ROC area with all features and 4–5 features.

Close modal

It is clear from the results that machine learning is sensitive to irrelevance and number of features. As shown in the results, the reduced predictive performance was related to the number of selected features. The results showed for all algorithms, especially MLP.

From the results, feature reduction makes sense to provide these algorithms with minimal features that provide acceptable results (Tables 57). In some cases, removing features can reduce the cost of acquiring data or improve the productivity of the software used to make predictions. It is generally better to have fewer features in the dataset file.

The use of a variety of machine learning algorithms in our study together to predict WQ gives better results than using a single model. There are several proposed methodologies for predicting WQ. These methodologies include different statistical methods and machine learning algorithms. In order to determine the correlation and relationship between different water quality features, we applied the PCA to the dataset. The PCA is a statistical analysis used for dimensionality reduction and extracts the most dominant water quality features. For predicting the WQ, three different machine learning algorithms were used. Significant results have been observed after using different statistical analysis methods (PCA), accuracy measurement methods (ROC), and machine learning algorithms. Finally, the best prediction model is selected by comparing the performances of such models.

The study used the dataset from Kaggle for training and testing. Three different machine learning algorithms such as J48, Naïve Bayes, and MLP models were used. The machine learning models are trained and tested using the default configuration of the features of the machine learning classifier. We analyzed and compared the results of our models, especially the accuracy measurement that is based on ROC for classifying water quality conditions into potable (1) or non-potable (0) classes. We studied the accuracy results for each model and compared their performance in classifying our data with different configurations of the number of selected features. The performance of our model showed that MLP performs better than the other two algorithms with all features and with a good number of selected features. Naïve Bayes predicts better with fewer numbers of features selected. We used ROC area as the accuracy measurement for utilizing these algorithms.

In future work, we recommend using IoT technology and integrating it with an online monitoring system using the required sensors and machine learning algorithms. Other algorithms and techniques can be proposed like the deep learning approach to improve the efficacy of the classification process. Another proposal is to use other different machine learning algorithms, namely ensemble learning, SVM, and K-NN. Further research can be done on management/measures or practices, as well as the natural characteristics of water bodies that will influence water quality.

All relevant data are available from an online repository or repositories. Water Quality: Drinking water potability https://www.kaggle.com/adityakadiwal/water-potability.

The authors declare there is no conflict.

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