Prediction of River Water Quality Based on Artificial Neural Network First Author\[1\]\*, Second Author2 (Double Blind Review: Please do not type or edit anything here until final-camera ready submissions) *1First affiliation, City and Country (Please do not type or edit anything here, until final camera-ready paper submission)* *2Second affiliation, City and Country (Please do not type or edit anything here, until final camera-ready paper submission)*
ARTICLE INFO ABSTRACT

Article history:

Received

Revised

Accepted

Online first

Published 1 March 2024

 In machine learning, a prediction is an assumption that may be supported by historical data and is often used in various fields. It may be used to forecast factors like river water quality, which is a major source of life, particularly for human. The health of the river is impacted by the river’s diminishing water quality, increasing the danger to human health and making it more difficult to ensure the sustainable production of drinking water. Water contamination may be a result of civilization and the economy developing so quickly. This research looks at how the Artificial Neural Network (ANN) algorithm predicts the Water Quality Index that will aid environmental department and consumers. Besides that, this study also aims to create an ANN-based water quality index prediction system that is effective at managing noisy data. Furthermore, the constructed prototype's ability to accurately forecast water quality will be assessed. The water quality forecast method is based on Selangor's three main rivers, which are Sungai Buloh, Langat, and Kuala Selangor and takes into account variables such the biological oxygen demand (BOD), dissolve oxygen (DO), and more. The performance metric used in the study is the calculation of the accuracy for factors such as the number of neurons in the hidden layer, the epoch number, the split data ratio and the learning rate. The result has shown that the ANN model has produced good and acceptable performance with 88.44% accuracy. In future, the ANN model will be improved with more data for its training and the performance will be compared with other prediction algorithms.

Keywords:

prediction

river

water quality

ANN

DOI:

10.24191/jcrinn.v9i1
# Introduction Rivers are regarded as one of the most important sources of water for agricultural demands, industrial needs, and other applications (Najah et al, 2019). Water quality is the state or condition of water that takes into consideration the physical, chemical, and biological characteristics of the water \[6\]. The Water Quality Index is a metric used to determine whether the overall water quality in a certain area is contaminated or not. In addition, Dissolve Oxygen (DO) and Chemical Oxygen Demand (CODMn) are also significant metrics for assessing water quality \[1\]. However, the fast expansion of civilization and the economy, including urbanization, industry, and agricultural output, may have polluted the river \[4\]. Furthermore, the river systems are especially susceptible to the negative impacts of environmental contamination because of their dynamic nature and ease of accessibility for garbage dumping \[6\]. For instance, the river that is close to an industrial activity will be at a higher risk of being exposed to contaminants discharged by negligent factories. So, according to Bi *et al.* \[1\] in a real-world water environment, having water quality management and its forecast is crucial to facilitate many parties. The prediction of river water quality system will help environmental department and consumers to forecast the condition of the river water in term of its state, as well as the classification of its condition. Besides that, the system could assist the environmental agency in determining whether a river could serve as a primary source for the processed water for consumers. This technology could reduce the amount of time needed for the environmental agency to get the categorization of the river water quality. It will improve their effectiveness in keeping an eye on all the major rivers in a given area that serve as a major source of food for local residents. According to Ho et al. a crucial first step toward improving river water quality management is the creation of water quality prediction models \[7\]. Due to the lengthy study of the water quality characteristics, in addition to the significant work and time required for gathering and analyzing water samples, the traditional method for calculating WQI (Water Quality Index) is always accompanied by inaccuracies \[7,19\]. Besides that, the present computations of the water quality index (WQI), which include sub-index calculations like BOD and COD, can occasionally be exceedingly difficult and time-consuming \[18,19\]. Due to extensive data collection on river pollution and ambiguous water quality parameter results, measuring river water quality may also be challenging \[21\]. Therefore, the necessity for the river water quality forecast system arises in order to reduce errors. In other research, any site's classification of the water quality can employ prediction to get findings as quickly as feasible and with the fewest parameters \[9\]. Large water quality dataset might be difficult to examine \[10\], so, it is necessary to have a prediction system that can predict river water quality, especially when using a high number of samples. Based on the importance of the river water quality prediction, this research has suggested utilizing the Artificial Neural Network (ANN) method to forecast river water quality. The objective of the research is to explore the capability of ANN for the river water quality prediction. The ANN makes very accurate predictions and enhances the performance of other algorithms \[1-5\]. Additionally, compared to other approaches, it provided the most accurate results in term of accuracy \[11\]. Previous studies in a particular subject, have shown that ANN has a lot of promise \[13\]. When a large number of samples are involved, the ANN technique is a very successful algorithm for river water quality prediction. Other algorithms' shortcomings are solved by using this method \[3\]. Therefore, ANN has been chosen to be investigated for the prediction of river water quality in this study. The paper is organized into five main sections which are the Introduction, Literature Review, Methodology, Results and Finding and finally the Conclusion. # literature review This section provides a brief description of the Artificial Neural Network (ANN) method and reviews on how river water quality may be predicted using machine learning techniques as shown in Table 1. ## Prediction of River Water Quality Water quality predictions have been made utilizing a variety of techniques and fields. Nayak has conducted research to develop a river forecasting system for the Godavari in India. The study focuses on using Artificial Neural Networks (ANN) in combination with other techniques to improve performance and get beyond conventional Water Quality Indices' (WQI) limitations. Levenberg Marquardt (LM) and Scaled Conjugate Gradient (SCG) are two other techniques that have been utilized in conjunction with ANN. The final result demonstrates that the ANN can accurately estimate the water quality at the Godavari River when combined with another algorithm. According to a related study by Bi, the suggested approach outperforms all other models when used to forecast time series data on water quality. This study uses artificial neural networks and the Savitzky-Golay filter. In addition, a study is being conducted in Wuhan, China, to evaluate the standard of the city's drinking water supply. 863 samples of tap water and 137 samples of finished water were utilised in the study. The study's results, which reveal that the quality of Wuhan's municipal drinking water is steady and in compliance with national hygiene standards, were obtained using the water quality index and an artificial neural network. Due to pollutant penetration into water pipelines, leaching, disinfection byproducts, chemical or microbiological permeation, and pollution, pure water can become contaminated. In order to forecast the quality of the water using artificial neural networks and risk analysis techniques, Dawood performed a research. Because of the strong capabilities of ANN and its great tolerance for data noise, the suggested technique performs well. In addition, drought can have an impact on the water quality, particularly for drinking. So, using artificial neural networks (ANNs), a team of academics conducted study to forecast Iran's drinking water quality. The result achieved while utilising the Artificial Neural Network method exhibits great accuracy with 98.8% of accuracy. Therefore, this research emphasizes the usage of Artificial Neural Network (ANN) itself in order to forecast the river water quality. The anticipated outcome will be displayed in terms of the river's water status and water quality classification. Table 1. Summary of the Recent Water Quality Prediction | No | Technique/Algorithm | Objective | Problem | Result | Reference | | -- | --------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------ | ----------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------- | --------- | | 1 | Artificial Neural Network using Levenberg Marquadt algorithm. | To overcome the limitation of conventional WQIs. | Water quality assessment is a need and is crucial for maintaining human health. | The Artificial Neural Network model using Levenberg Marquadt algorithm have very high accuracy. | \[3\] | | 2 | Encoder-Decoder Neural Network with long-short term memory and a Savitzky-Golay filter. | To ensure efficient water resource management. | People’s worries about the severe contamination of the aquatic environment have grown. | Encoder-Decoder neural network with long-short term memory and Savitzky-Golay filter provide more accurate prediction. | \[1\] | | 3 | Artificial Neural Network | To assess the spatial-temporal distribution of municipal drinking quality across time. | Rapid growth caused the water system and biological environment to deteriorate. | Artificial Neural Network predict water quality with high accuracy. | \[4\] | | 4 | Artificial Neural Network and risk analysis technique | To further explain, via the use of Artificial Neural Networks and risk analysis technique, the effect of aforementioned elements on the quality of portable water. | The distribution stage quality failure will be delivered to the public. | The use of ANN optimize the prediction result. | \[5\] | | 5 | Artificial Neural Network and Fuzzy Clustering | To estimate the probability of occurrence of adverse annual changes in the water quality of drinking water. | Knowledge of the future state of water resources, particularly in lessons on standard drinking water. | The use of Artificial Neural Network give high prediction accuracy. | \[2\] | | 6 | Artificial Neural Network with Environmental fluid dynamic code. | To improve the prediction accuracy of water quality in a large lake | Limited field data can often be the major cause of errors in water quality prediction | The Artificial Neural Network showed more accurate result | \[14\] | ## Artificial Neural Network An Artificial Neural Network (ANN) is one of the machine learning and Artificial Intelligence techniques that were often used \[20\]. Besides that, ANN is a universal approximate in mathematics, demonstrated to be very good at modelling non-linear problems, and used to solve a variety of complex issues, such as pattern recognition, classification, and control \[15\]. It is a computer model made up of many processing components that accept input and produce output in accordance with a specified activation function. A technology known as artificial neural networks was developed using research on the nervous system and the brain \[8\]. Although they employ a condensed set of biological brain system ideas, these networks mimic biological neural networks \[8\]. ANN models mimic the brain and nervous system's electrical activity \[8\]. The input, hidden, and output layers are the three layers that make up an ANN \[16\]. Each layer has a number of neurons that are used to calculate the activation function as well as the weight and bias values for the layer \[12\]. Weight and bias values can be initialized at random. ANN consist of two training rules which are feedforward and backward propagation \[17\]. The basic flowchart of Artificial Neural Network algorithm are shown in Figure 1. 1: initialize weight, bias, neuron 2: compute the activation function value for each neuron in all layers. 3: select class from the result The first stage in the ANN process is to initialize the weight, bias, and neuron values for each layer. At initially, they will be randomly initialized, but afterwards, the number of neurons can be adjusted to discover the model's optimal performance. The model will then determine the value of the activation function, which includes the weight, bias, and value of each neuron. It will begin with the input to the hidden layer and move on to the hidden to output layer after that. The model then chooses the outcome depending on the output neuron that have been calculated after the computation was done in the last layer, which is the output layer. ![](65be445757ff2_media/media/image1.jpg) Fig. 1. Artificial Neural Network’s Flow # methodology ## Experimental Data The Selangor Environmental Department provided the dataset for the model's development. Due of its quality, it frequently draws numerous complaints from customers and involves Selangor's three main rivers. In order to categorize future behavior for a specific river, the dataset will be utilized throughout the model's training and testing phases. This will allow it to learn from previous behavior of river water characteristics. The characteristics or elements that are taken into account are shown in Table 2. It can determine whether or not the supplied river samples were contaminated are the values of Biological Oxygen Demand (BOD), Dissolve Oxygen (DO), Sulfate, PH, Nitrite, Conductivity, Nitrate, Phosphate, and Water Quality Index. There were 683 rows of data and 9 total characteristics involved in this study. The new inputs that the user will enter will be used to compute the classification's outcome. The result will display the various water quality classes, which range from 1 to 5, with the lower the number, the better the quality of the water. In addition, it displays the level of pollution in the river's water, which are clean, polluted or slightly polluted. Table 2. Attributes of the Prediciton and the Output | Prediction Attributes | Biological Oxygen Demand (BOD) | | --------------------- | ------------------------------------------------ | | | Dissolve Oxygen (DO) | | | Sulfate | | | PH | | | Nitrite | | | Conductivity | | | Nitrate | | | Phosphate | | | Water Quality Index | | Classification Output | Water Status: Clean, Polluted, Slightly Polluted | | | Water Class: I, II, III, IV, V | ## System Architecture The system architecture of the river water quality prediction system is depicted in Figure 2. It is consisted of three basic sections, which are the data collection and preparation, user interface and the system’s engine. The raw data were collected from the Selangor's Environment Department. A number of rivers, including those in Kuala Selangor, Sungai Buloh and Langat were selected to make up the sample of data. Due to the exposure of industrial activities and other factors, these rivers have been the subject of several consumer complaints in recent years. There were 683 data that had been collected, which represented the monthly data starting from January 2017 to December 2022. The data had gone through data cleaning in order to replace the missing values, eliminate unnecessary characteristics and other tasks. Then the dataset were trained and tested using the ANN algorithm. There were 3 percentage splits that had been tested in order to avoid the overfitting of the algorithm. The algorithm was also fine tuned to obtained the best parameters such as the best number of neuron, epoch and also the learning rate. The best split had been chosen for the ANN model for this research. After the testing and training, the ANN model was ready to be deployed for the river water quality prediction. In this system, the user has to enter the 9 value of attributes, which are the dissolve oxygen, biological oxygen demand, sulfate, PH, nitrite, conductivity, nitrate, phosphate and water quality index. The ANN model will process the input data and the output will be the the river water quality status, which is categorized into clean, polluted or slightly polluted. The river water class from 1 to 5 will also be displayed with the river water quality result. ![](65be445757ff2_media/media/image2.jpg) Fig. 2. System Architecture ## Prediction Model Process Flow The model's flowchart, which is represented in Figure 3, begins with the gathering of data through Selangor's environmental department, which includes 683 rows of data from 2017 to 2021. The data will be preprocessed in order to increase its usefulness, which includes actions like replacing missing values and uninteresting attributes. The data will subsequently be prepared for use in the ANN's training and testing phases. The setting of weight, bias, and the number of neurons for each layer—especially the hidden layer—is the first step in the ANN algorithm. This is due to the fact that the value will be required to compute the value from each neuron until the final neuron in the output layer. The output neuron's value truly represents the prediction's value. The accuracy of the ANN models will be noted and assessed to see if any parameter adjustments are necessary to improve performance. ![](65be445757ff2_media/media/image3.jpg) Fig. 3. Prediction Model Flowchart # RESULTS AND FINDING In order to improve the performance of ANN models, performance evaluation takes into account a number of factors. Parameter tuning is the procedure where the parameter will be adjusted to get the optimum accuracy. The number of neurons in the hidden layer, the split data ratio, the epoch number and the learning rate were among the parameters that were tuned. In addition, this section displays the system's user interface and the manner in which the forecast result will be presented. ## Number of Neuron in Hidden Layer First, the hidden layer's neuron count is crucial since it is not constrained to a certain range. However, differing neuron values in the hidden layer might result in varying degrees of accuracy. The higher number of neurons in the hidden layer does not always indicate the effective it will be. Determining a specific range of neurons in the hidden layer is necessary for this reason. Based on Table 3 and Figure 4, the model's accuracy is assessed using three alternative numbers of neurons. The accuracy obtained with the first set of neurons, 8, was 87.16%; with 16, 88.02%; and with 32, 84.98%. So, 16 number neurons were chosen as it provided the maximum number of accuracy out of the three various numbers of neurons that were put to the hidden layer of the ANN. - All other parameter being constant: 1) Split data ratio: 70,30 2) Epoch value: 450 3) Learning rate: 0.1 Table 3. Evaluation on Number of Neuron | Number of neuron | Accuracy | Loss | | ---------------- | -------- | ----- | | 8 | 87.16% | 0.088 | | 16 | 88.02% | 0.082 | | 32 | 84.98% | 0.107 | ![](65be445757ff2_media/media/image4.JPG) Fig. 4. Number of Neuron Performance’s Evaluation Graph ## Split Data Ratio The split data ratio divides the dataset into two subsets, the training dataset and the testing dataset. While the testing dataset will be utilized throughout the ANN's testing phase, the training dataset will be used during training phase. As an illustration, if the split data ratio was 60:40, then 60% of the dataset would be designated as training data and the remaining 40% as testing data. Table 4 and Figure 5 displays the three alternative splits for the data split ratio parameter. The accuracy for the split data ratios that were used was 88.44%, 85.48%, and 86.75% correspondingly for 70:30, 80:20, and 90:10. Therefore, a split data ratio of 70:30 delivered the best performance with an accuracy of 88.44% out of the three split data ratios used to the artificial neural network model. With a split data ratio of 70:30, the dataset is divided into 30% for testing and 70% for training. - All other parameter being constant: 1) Number of neuron in hidden layer: 16 2) Epoch value: 450 3) Learning rate: 0.1 Table 4. Evaluation on Split Data Ratio | Split ratio | Accuracy | Loss | | ----------- | -------- | ----- | | 70,30 | 88.44% | 0.081 | | 80,20 | 85.48% | 0.104 | | 90,10 | 86.75% | 0.096 | ![](65be445757ff2_media/media/image5.JPG) Fig. 5. Split Data Ratio Performance’s Evaluation Graph ## Epoch Number Epoch, which refers to how many times the dataset will be utilized in the ANN model, is the following parameter. In the context of machine learning, this refers to how frequently the model will draw knowledge from datasets or previous values. The performance of the model may be impacted by the number of epochs. As indicated in table 5 and Figure 6, the value of the epoch was also examined to determine how well it performed in terms of accuracy. The range of tested epochs is 100 to 1000, with 100 as the beginning point and 50 as the increment. There will therefore be 19 different epoch values that were assessed. The maximum accuracy of 88.37% was obtained using the 700 and 1000 epoch values out of all 19 possible epoch values. So, the comparison of loss for both values was assessed in order to decide which epoch should be chosen. According to the loss, the 700 epoch had a lower loss than the 1000 epoch, with 0.086 instead of 0.095. - All other parameter being constant: 1) Number of neuron in hidden layer: 16 2) Split data ratio: 70,30 3) Learning rate: 0.1 Table 5. Evaluation on Epoch Number | Epoch (+50) | Accuracy | Loss | | ----------- | -------- | ----- | | 100 | 69.41% | 0.249 | | 150 | 77.96% | 0.174 | | 200 | 76.14% | 0.172 | | 250 | 88.03% | 0.090 | | 300 | 87.62% | 0.101 | | 350 | 87.52% | 0.097 | | 400 | 84.38% | 0.115 | | 450 | 84.60% | 0.114 | | 500 | 86.11% | 0.091 | | 550 | 88.11% | 0.093 | | 600 | 87.05% | 0.104 | | 650 | 87.50% | 0.096 | | 700 | 88.37% | 0.086 | | 750 | 86.40% | 0.095 | | 800 | 84.87% | 0.117 | | 850 | 86.32% | 0.096 | | 900 | 85.74% | 0.116 | | 950 | 87.09% | 0.089 | | 1000 | 88.37% | 0.095 | > ![](65be445757ff2_media/media/image6.JPG) Fig. 6. Epoch Number Performance’s Evaluation Graph ## Learning Rate When weight and bias are adjusted in an ANN's backward propagation, learning rate is a parameter that is taken into consideration. This is due to the fact that weight and bias values must first be changed to match the model because they are initially created at random. As indicated in table 6 and Figure 7, similar to other parameters, learning rate has been assessed with values ranging from 0.1 to 1.0 with a 0.1 increment. Thus, there will be 10 distinct learning rate values. Based on all possible learning rate values, 0.1 provided the best accuracy (88.11%), while 0.4 provided the lowest accuracy (74.27%). Therefore, 0.1 will be used in the ANN model. - All other parameter are being constant 1) Number of hidden neuron: 16 2) Split data ratio: 70,30 3) Epoch value: 700 Table 6. Evaluation on Learning Rare | Learning rate | Accuracy | Loss | | ------------- | -------- | ----- | | 0.1 | 88.11% | 0.091 | | 0.2 | 82.81% | 0.129 | | 0.3 | 82.47% | 0.142 | | 0.4 | 74.27% | 0.230 | | 0.5 | 78.51% | 0.210 | | 0.6 | 82.73% | 0.171 | | 0.7 | 79.22% | 0.189 | | 0.8 | 86.79% | 0.131 | | 0.9 | 77.04% | 0.214 | | 1.0 | 84.72% | 0.151 | > ![](65be445757ff2_media/media/image7.JPG) Fig. 7. Learning Rate Performance’s Evaluation Graph ## Prediction Model Interface The system's user interface is divided into three sections: an about page, a prediction page, and a performance assessment page. The major objective of the system, which was to assess and examine the effectiveness of Artificial Neural Networks (ANN) in forecasting river water quality, is described on the about page, as seen in Figure 8. ![](65be445757ff2_media/media/image8.png) Fig.8. About Page The forecast of the river's water quality is on the second page, as shown in Figure 9. The user must provide the input value that corresponds to the attribute values. The output will then be presented in the result area once the user clicks the predict button to see the outcome of the prediction. In addition, the user can refer to the output reference included in the reference section. ![](65be445757ff2_media/media/image9.png) Fig. 9. Prediction Page The performance assessment page is the last page, as illustrated in figure 10. It has buttons that provide information on the ANN's structure and parameter comparisons in the form of graphs and tables. ![](65be445757ff2_media/media/image10.png) Fig. 10. System’s Performance Page # conclusion ***In this research, the ANN model has successfully predicted the river water quality with an acceptable accuracy of*** 88.44%. The accuracy was obtained using the following parameters; 16 hidden neurons, a 70:30 split of the data, 700 epochs and a 0.1 learning rate. The siginificance of the research is that the prediction of the river water quality system could help the environmental department and consumers to forecast the condition of the river water in term of its state, as well as the classification of its condition. Besides that, the system could assist the environmental agency in determining whether a river could serve as a primary source for the processed water for consumers. This technology could reduce the amount of time needed for the environmental agency to get the categorization of the river water quality. It will improve their effectiveness in keeping an eye on all the major rivers in a given area that serve as a major source of food for local residents. The future work are to add more data for the training and testing of the algorithm and to compare its performance with other prediction algorithms such as the Support Vector Machine and Random Forest. # Acknowledgements # The authors would like to express greatest gratitudes to XXX for the support given in the advancement of the research in the university. # Conflict of interest statement There are no conflict of interests in the research. # Authors’ contributions XX carried out the research and wrote the article. XX supervised and edited the article. XX and XX also helped in editing and finalized the article. # References Bi, J., Lin, Y., Dong, Q., Yuan, H., & Zhou, M. (2021, September). Large- scale water quality prediction with integrated deep neural network. *Information Sciences*, *571*, 191–205.[https://doi.org/10.1016/j.ins.2021.04.057](https://doi.org/10.1016/j.ins.2021.04.057) Azimi, S., Azhdary Moghaddam, M., & Hashemi Monfared, S. A. (2019). 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