Artificial Intelligence Approach to Predicting River Water Quality: A Review

Journal of Environmental Treatment Techniques

2020, Volume 8, Issue 3, Pages: 1093-1100

J. Environ. Treat. Tech.

ISSN: 2309-1185

Journal web link: http://www.jett.dormaj.com

Artificial Intelligence Approach to Predicting River

Water Quality: A Review

1,3

1,2*

1,2

Ariani Dwi Astuti , Azmi Aris , Mohd Razman Salim , Shamila Azman , Salmiati and

Mohd Ismid Md Said¹

Department of Water and Environmental Engineering, School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, Johor, Malaysia

Centre for Environmental Sustainability and Water Security (IPASA), Universiti Teknologi Malaysia, Johor Bahru, Malaysia

Department of Environmental Engineering, Faculty of Landscape Architecture and Environmental Technology, Universitas Trisakti, Jakarta, Indonesia

Received: 26/05/2020

Accepted: 07/07/2020

Published: 20/09/2020

Abstract

Precise prediction of the water quality time series may provide directions for early warning of water pollution and help policymakers to

manage water resources more effectively. This prediction may reveal the proclivity of the characteristic water quality according to the most

recent water quality, shifting, and transformation rule of the pollutant in the watershed. The predictive capability of traditional models is

constrained due to variability, complexity, uncertainty, inaccuracy, non-stationary, and the non-linear interactions of the water quality

parameters. Since the middle of the 20th century, Artificial Intelligence (AI) approaches have been found efficient in bridging gaps,

simulating, complementing deficiencies, and improving the precision of the predictive models in terms of multiple evaluation measures for

better planning, design, deployment, and handling of multiple engineering systems. This article discusses the state-of-the-art implementation

of AI in water quality prediction, the type of AI approaches, the techniques adopted include the knowledge-based system as well as literature

and their potential future implementation in water quality modelling and prediction. The study also discusses and presents several possibilities

for future research.

Keywords: Water quality simulation; Artificial Intelligence; Knowledge-based system; Review

Introduction¹

have parameter-wide nonlinear relationships with each other.

Conventional data processing cannot address this significant

limitation (10–12). Nonlinear Artificial Intelligence (AI) models,

on the other hand, play a significant role in simulating complex

and nonlinear processes (13). This situation creates a big gap

between model designers and professionals. Selecting a suitable

numerical model is a challenging task for novice application

users. The forecast precision of traditional models is restricted

due to the uncertainty, unpredictability, obscurity, and inaccuracy

of water quality information. Progress in AI technology has made

it possible over the past decade to apply the developments in

computational modelling systems to bridge the gap, as mentioned

above (8).

Chemical, physical, and biological properties found in water

are generally referred to as the quality of water (1). Accurate

evaluation of water quality using the Water Quality Management

program is important if decision-makers are to understand,

interpret, and use these data to support resource management

practices (2,3). Modelling of water quality parameters is an

essential part of every water systems analysis. In order to properly

manage the watershed, it is necessary to predict the quality of

surface water so that appropriate measurements can be taken to

avoid pollution from the permissible concentrations. Ideal

management of water resources is based on accurate and reliable

estimates of future changes (1,4–6).

QUAL2E, Water Quality Analysis Simulation, and the U.S.

Army Corps of Engineers Hydrological Engineering Center-5Q

are several models commonly applied to water quality

management (7). These models, however, are not only time

consuming and expensive, but also lack user-friendliness and

effective knowledge transfer in model interpretation. Therefore,

more models, which do not suffer from these problems, are

needed to be developed (8,9). Several scientists noted that the

prediction of water quality is impacted by various variables that

AI methods are currently capable of mimicking this behaviour

(14), complementing the defect, and improving the precision of

forecast models in terms of multiple assessment measures for

better planning, design, operation, and management of distinct

engineering systems (15, 16). The significant contributions of the

present review article are 1) to categorise AI methods

comprehensively and 2) to discuss their advanced application to

water quality modelling and prediction.

Corresponding author: Azmi Aris, (a) Department of Water and Environmental Engineering, School of Civil Engineering, Faculty of

Engineering, Universiti Teknologi Malaysia, Johor, Malaysia and (b) Centre for Environmental Sustainability and Water Security (IPASA),

Universiti Teknologi Malaysia, Johor Bahru, Malaysia. Email: azmi.aris@utm.my.

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Journal of Environmental Treatment Techniques

2020, Volume 8, Issue 3, Pages: 1093-1100

extremely precise because it conveys all kinds of interactions

expressed in the information, including fundamental physics and

chemistry (9). Some studies (6, 11, 22-27) that explored river

water quality modelling issues using AI methods have revealed

encouraging outcomes in recent decades (Table 1).

Several researchers have attempted to predict water quality

parameters using AI-based models such as ANN, SVM, and k-

NN. In these studies, ANN has been frequently found a stronger

predictive model compared to conventional modelling

techniques. In the case of 47 sources (2007-2019) reviewed,

ANN, SVM, and k-NN have been used in 38, 10, and 1 source,

respectively. ANN has been widely used between 2007 and 2015,

but from 2015 to 2019, ANFIS and SVM have surpassed ANN,

as more recent approaches of AI. Some studies made a

comparison between the models.

The study found that different parameters are needed to be

used in water quality assessments using various techniques.

Different output parameters predictions have been studied, but the

ten most important parameters are DO, BOD, TSS, Total

Nitrogen, temperature, COD, turbidity, Total Phosphate, NH ,

and WQI. The monthly water quality data have been used most in

many of these studies to simulate water quality parameters [4, 5,

Artificial Intelligence-Based Model for River

Water Quality Simulation

The 1956 Dartmouth Conference was held at a time when AI

earned its name, purpose, and first accomplishments; it was

widely recognised as the birth of AI. Across various fields, the AI

field is currently playing an important role, focusing on machines

with a human-like mind (17). By incorporating descriptive

understanding, procedural knowledge, and reasoning, AI methods

enable researchers to simulate human knowledge in clearly

defined domains. In addition, advances in AI techniques have

enabled the creation of intelligent management systems through

the use of shells under established platforms such as MathLab,

Visual Basic, and C++. (8, 18).

Recently, AI has achieved significant progress in multiple

programs such as autonomous driving, big data, information

processing, smart search, image understanding, automatic

software development, robotics, and human-computer games,

which will have a significant effect on human society. Some of

the most important AI-based algorithms include artificial neural

networks (ANNs), support vector machine (SVM), random forest

(

RF), genetic algorithm (GA), enhanced regression tree (ERT),

simulated annealing (SA), imperialist competitive algorithm

ICA), and decision tree (DT). AI methods are also associated

0, 11, 16-28], which was followed by daily water quality data (3,

5, 35-40).

(

with experimental design (e.g., response surface methodology,

and standardised design) to improve the precision of the optimal

solution prediction (19). Advances in data science and data

mining techniques such as neural networks (NNs), supporting

vector machines (SVMs), and k-nearest neighbours (k-NN) have

helped to solve some complicated high-dimensional issues in

river water quality prediction (Figure 1).

Artificial Neural Network Modelling in River

Water Quality Monitoring

The theory of artificial neurons was first launched in 1943,

with the implementation of the back-propagation practice (BP)

algorithm for feedforward ANNs in 1986 (23). ANN is a recent

method with a versatile mathematical structure that can identify

complicated non-linear interactions between input and output

information compared to other traditional modelling approaches

(1, 25).

Over the past two decades, river water pollutants have been

considered as one of the global issues that need the full attention

of environmental scientists. River water quality, however, is one

of the main characteristics to which environmental scholars need

to pay full attention. In all developing countries, water quality is

a growing concern. Water abstraction mechanisms of domestic

use, farming, mining, energy generation, and forestry practices

may lead to a decline of water quality and quantity, which affects

not only aquatic ecosystems but also the allocation of safe water

for human consumptives (20). Thus, the assessment of surface

water quality is important in the management of water resources

and is very important in monitoring the concentration of

pollutants in rivers. Monitoring water quality is costly because

pollution control and efficient water resource management

require large quantities of data (21). Therefore, AI can be

recommended as an alternative technique with high prediction

accuracy for predicting the river water quality. AI benefits from

traditional techniques since they take account of the non-linear

relationship between influential variables and reduce the

complexity required to obtain experimental equations (20).

The overall concept behind AI techniques is to explore hidden

interactions in large quantities of information and to create

models that represent physical procedures governing the system

being studied. A model derived from data reflects a correlation

between variables of input and output. Such a model can be

ANNs are common instruments applicable to modeling

extremely complex relations, processes, and phenomena. ANNs

have been also widely used to predict water quality variables to

address contaminant source uncertainty and nonlinearity of water

quality data. Nevertheless, the issue with the initial weight

parameter and the unbalanced training data set makes it hard to

determine the optimal outcomes and hinders ANN modeling

efficiency (25). ANN consists of very basic processors called

neurons that are strongly interconnected and act together to solve

a problem (41). A neuron is an information processing unit,

essential for the functioning of the NN; it comprises weight and

activation.

From 2007 to 2019, eight types of ANN were applied by

different researchers to the prediction of river water quality,

namely Back Propagation NN (BPNN), Wavelet NN, Generalized

Regression NN (GRNN), Radial Basic NN (RBNN), Feed

Forward NN (FFNN), Multi-layer Perceptron NN (MLPNN),

Multi-layer Feed Forward NN (MLFFNN) and Adaptive

Network-Based Fuzzy Inference System (ANFIS). Among them,

five most widely-used models MLPNN (10), RBNN (6), FNNN

(5), ANFIS (5), and MLFFNN (4).

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2020, Volume 8, Issue 3, Pages: 1093-1100

Artificial Neural Network (ANN)

Single Approach

Support Vector Machine

k-nearest neighbors

Artificial Intellegency (AI)

Technique

WAVELET-ANFIS

WAVELET-ANN

ANN-ARIMA

Hybrid Approach

Figure 1: Classification Tree of AI Techniques Applied in Literature to River Water Quality

Several indicators often used to evaluate the ANN model's

simplified architecture of two weight layers with basic function

parameters in the first layer, while the second layer contains linear

combinations of those basic functions for the processing of the

output and also contains parameters for water quality (1). Some

performance are as follow: Coefficient of correlation (R) between

the values observed and the expected values, the mean square

error (which can be used to calculate how well the network output

corresponds to the expected output), mean absolute error (MAE),

root mean square error (RMSE); Coefficient of efficiency (CE),

Mean absolute prediction error (MAPE) (which usually expresses

accuracy as a percentage), interquartile range (IQR) (which refers

researchers have predicted DO, COD, TDS, NH -N, Turbidity,

and WQI using RBFNN. Cobaner et al. in 2009 used GRNN,

MLP, and RBNN to forecast SS. Their results confirmed that

RBNN performed slightly better than the others (37). In addition,

Ahmed (2017) compared some models regarding the DO

prediction, which again showed the superiority of the RBNN's

performance over the rivals (33).

to difference between the 25 and 75 percentile and is used to

calculate the entire bias error between the means of the ensemble

and the values observed), Nash-Sutcliffe coefficient (NSC), and

Determination coefficient (DC) (Table. 2).

FFNN propagates the data linearly from input to output; in many

practical applications, they are the most popular and widely-used

models (43). FFNN is used to predict DO, BOD, TN, and

temperature. Four scientists implemented monthly data in FFNN

to predict DO using different input parameters (11, 13, 33, 43).

Ahmed in 2017 predicted DO using BOD and COD parameters

as input, and obtained R = 0.936 and RMSE = 0.709. Ranković et

al. in 2010 used FFNN to check its capability to predict DO. They

added more variables of water quality. Their findings showed that

pH and the water temperature are the most powerful variables in

DO prediction. In (44), ANFIS was used to learn neural network

algorithms and fuzzy logic was used to construct a non-linear

mapping between inputs and outputs. Ahmed et al. (2017) and

Khaled et al. (2018) conducted a study into BOD prediction using

ANFIS, and they suggested that the ANFIS technique could be

successfully applied to building models for predicting the river

water quality (34, 55). Elkiran et al. (2018) attempted to predict

DO at Yamuna River using ANFIS, FFN, and MLR. They found

that even both FFNN and ANFIS were found capable of handling

nonlinear interactions, the ANFIS model performed better than

FFNN (13). The most predicted parameters for ANN are done,

DO, BOD, COD and WQI, respectively 13, 5, 5, and 4 studies.

And so far, for certain output parameters from certain inputs, each

NN type has achieved good results.

The fundamental MLPNN model has three layers: (i) input

layer, (ii) hidden layer, and (iii) output layer. The input layer

supplies the input data set, the hidden layer processes the features,

and finally, the output layer shows the expected results. As can be

seen in Table 2, MLPNN is widely used to predict the DO

parameters. (15, 28, 31, 32). Moreover, MLPNN is used to predict

BOD, COD, EC, TDS, turbidity, and WQI. In predicting the DO

parameters, different inputs are used. However, Olyaie et al. in

017 and Ay and Kişi in 2017 both used pH, EC, temperature,

and flow as input parameters (15,31). The difference is that Olyaie

et al. used daily data of seven years, while Ay and Kişi used

monthly data of 15 years. Regarding the performance, Olyaie et

al. obtained R = 0.955 and root mean square error (RMSE) =

.594, while Ay and Kişi obtained R = 0.98 and RMSE = 0.52.

The time scale of the data does not seem to have any impact on

outcomes.

Radial basis function neural network (RBFNN) is a type of

NN applicable to general purposes and to various problems. The

RBFNN model is more advantageous than other types of NN that

have a grouping phase during training, where the hidden node's

central location is calculated (36). RBFNN was developed as one

of the most common three-layer neural feedforward networks

(42) to determine parameters of water quality. This model has a

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2020, Volume 8, Issue 3, Pages: 1093-1100

Table 1: Artificial Intelligence-Based Models Applied to Water Quality Prediction

Type of

Approach

Artificial

Neural

Methods

Output Parameter

River

Authors

Back Propagation

neural networks

COD, DO, NH

Sediment

Dahan River, Taiwan; Jishan

Lake, China; River Suktel,

India; Yuqiao reservoir in

Tianjin

Zhao et al. (2007); Xu and

Liu (2013); Chang et al.

(2015); Ghose and

Network

(ANN)

Samantaray (2018)

Wavelet Neural

Network

Jishan Lake, China

Xu and Liu (2013)

Generalized

COD

Cark Creek, Turkey

Ay and Kisi (2014)

Regression NN

Radial Basic

Neural Networks

COD, DO, NH

Surma River, Bangladesh;

Yangtze River, China; Johor

River, Malaysia; Cark Creek,

Turkey; Kopili River, India

Ahmed (2017); Basis et al.

(2014); Najah et al. (2013);

Ay and Kisi (2014); Kumar et

al. (2016)

TDS, Turbidity,

Suspended

sediment

Feed Forward

Neural Network

BOD, DO, Total

Nitrogen,

Temperature,

WQI

Gomti river, India; Melen River, Singh et al. (2009); Dogan et

Turkey; Surma River,

al. (2009); Ahmed (2017); He

et al. (2011); Gazzaz et al.

(2012); Elkiran et al. (2018)

Bangladesh; 59 rivers in Japan;

Kinta River, Malaysia; Yamuna

River

Multi-layer

Perceptron Neural

Networks

WQI, DO, BOD,

COD, TDS,

Turbidity,

Johor River, Malaysia; Heihe

River, China; Cark Creek,

Turkey; Langat River, Malaysia; al. (2013); Ay and Kişi (2014,

Najah et al. (2011; 2013);

Gazzaz et al. (2012); Wen et

Electrical

conductivity

Aji-Chay River, Iran

2017); Raheli et al. (2017);

Keshtegar and Heddam

(2018); Zhang et al. (2019);

Barzegar et al. (2016)

Dogan et al. (2009); Kim and

Seo (2015); Ömer Faruk

(2010)

Multi-layer Feed

Forward Neural

Networks

BOD, DO, pH

Temperature,

Turbidity, TN,

TP, Boron

Melen River, Turkey; Nakdong

River, South Korean; Buyuk

Menderes River, Turkey

Adaptive Network-

Based Fuzzy

Inference System

BOD

Yangtze River, China; Beas

River, Hong Kong; Surma

River, Bangladesh; Aji-Chay

River, Iran; Ouizert Reservoir,

Algeria; Yamuna River

Deng et al. (2015); Barzegar

et al. (2016); Ahmed and

Shah (2017); Khaled et al.

(2018); Elkiran et al. (2018)

(ANFIS)

Support Vector Machine (SVM)

DO, BOD, COD,

CODMn, NH –N,

BOD

Johor River, Malaysia; Weihe

River, China; Sefidrood River,

Iran; Yamuna River, India;

Changle River, China; Kopili

River, India; Wen-Rui Tang

River, China; Small Prespa

Lake, Macedonia, Greece; Pond

at Dongying city, China

Najah et al. (2011); Wang et

al. (2011); Noori et al. (2012);

Liu and Lu (2014); Kisi and

Parmar (2016); Kumar et al.

(2016); Ji et al. (2017); Li et

al. (2018); Fijani et al. (2019)

k-nearest neighbors

ANN-ARIMA

TDS, EC

DO, temperature,

NH3-N, Boron

Lighvan Chay River, Iran

Yangtze River, China; Buyuk

Menderes River, Turkey

Sattari et al. (2016)

Ömer Faruk, (2010); Basis et

al. (2014)

Wavelet-ANFIS

Wavelet-ANN

TDS, EC,

Turbidity

Johor River, Malaysia; Aji-Chay Najah et al. (2012); Barzegar

River, Iran

et al. (2016)

Aji-Chay River, Iran

Barzegar et al. (2016)

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Table 2: ANN Application to DO Prediction

Output

Parameter

BOD and

Time Scale

Data

R , RMSE, and bias Monthly

Methods

FFNN

Input Parameter

Evaluation Criteria

Authors

pH, total

Singh et al.

(2009)

alkalinity, total hardness, total

computed

(10 years)

solids, COD, DO, BOD, NH -N,

NO -N, Cl, PO , K, and Na

FFNN

pH, T, chloride, total phosphate,

nitrites, nitrates, ammonia, iron,

manganese, and EC

R, MAE, and MSE

Monthly

(1 year)

Ranković et

al. (2010)

FFNN and DO, BOD, pH, and T

ANFIS

FFNN and BOD and COD

RBFNN

RMSE, MSE, and

Daily

(1999-2012)

Monthly

(2010-2012)

Elkiran et

al. (2018)

Ahmed

R, MSE, and

coefficient of

efficiency (E)

R. RMSE, MAPE,

and NSC

(2017)

MLFFNN T, boron, and DO

temperature,

boron, and

pH, DO,

turbidity,

TN, and TP

Monthly

(8 years)

Ömer

Faruk,

(2010)

Kim and

Seo (2015)

MLFFNN pH, DO, turbidity, TN, and TP

R , RMSE, and IQR Daily

(2009-2012)

MLPNN

pH, EC, Cl , Ca , total alkalinity, DO

R, RMSE, and MSE Monthly

(6 years)

Wen et al.

(2013)

3 4

total hardness, NO -N, and NH -

COD, PO4, TS, K, Na, Cl, EC,

pH, and NH -N

BOD and

R, RMSE, %RMSE, Monthly

and Willmott’s

index of agreement

RMSE, Nashe

Sutcliffe efficiency

coefficient (NSC),

MARE and, R

Raheli et al.

(2017)

(2001-2010)

pH, EC, T, and river discharge

Daily

(July 2007-

Jan 2014)

Olyaie et al.

(2017)

(

MLPNN

RBFNN

pH, EC, T, river discharge (Q),

and DO

RMSE, MAE, and

R, MAP, and RMSE Weekly

(2005-2014)

Monthly

(1996-2010)

Ay and Kişi

(2017)

Basis et al.

(2014)

DO and NH

-N

DO and

NH -N

training vectors incorporated in the models as support vectors to

improve future predictions through learning from past experience.

On the other hand, SVMs have some disadvantages. Because of

the inherent complexity of mapping non-linear input spaces to

high-dimensional character-spaces and the extrapolation of

models, it is not easy to understand and interpret the behavior of

nonlinear SVM models. Moreover, the model is entirely

dependent on records when data inconsistencies occur (57). In

several studies, SVM has been successfully applied to water

quality prediction in case of BOD (56, 60), DO (40, 53), COD

Support Vector Machine Approach in River

Water Quality Monitoring

SVM has raised expectations in recent years since it has been

successful in the context of classification problems, regression,

and prediction; for example in machine learning concepts and

methods, statistics, statistical analysis, and convex optimization

(57). Several previously-conducted studies have confirmed the

high potential and outstanding performance of SVM. This level

of effectiveness is largely due to the concept of structural risk

minimization (SRM) in SVM, which is more generalized and

superior to the theory of empirical risk minimization (ERM),

when applied to neural networks (24).

The results of this study are in line with those of the study

conducted by Chen (2010) who claimed that the concept of SRM

in SVM, which is focused on the theory of statistical learning,

may successfully resolve the Neural Network deficiency (58).

SVMs are able to produce reliable and stable classification tests

even if the input data are neither monotonous nor linearly

separable. They can quickly determine more relevant information.

A key feature of SVM is that during the training process, it

automatically defines and integrates support vectors and avoids

the control of non-supporting vectors over the configuration; this

means that the model can handle noisy conditions well (59). SVM

has the ability to track events in the past with some primary actual

(30), Total Phosphate, and Total Nitrogen (53).

Hybrid Approach to River Water Quality

Monitoring

Due to the robustness and accuracy of wavelet-AI models,

their application to hydrology has gradually increased in recent

years. The success of this model is attributable to the efficiency

of multi-resolution processing wavelet transformations, the

identification of noise and edge effects via a signal, and the high

capability of AI in dealing with optimization and prediction

problems (4). Some researchers have investigated a number of

hybrid models in terms of water quality prediction. Their results

have shown that high-vanishing wavelets could help improve the

robustness and efficiency of hybridized wavelet-AI models. The

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2020, Volume 8, Issue 3, Pages: 1093-1100

performance of these hybrid models was compared to wavelet

models. Najah et al. (2012) used the integrated wavelet-ANFIS

model to predict monthly water TDS, EC, and turbidity. They

found the greatest accuracy level obtained by making the fifth

cross-validation length of data records. Additionally, they

confirmed the superiority of the wavelet-ANFIS model compared

to ANFIS model in terms of prediction accuracy (16). It can be

inferred that wavelets with long vanishing times can be useful for

improving the quality and reliability of wavelet-AI hybrid

models. The wavelet-ANFIS model has been also found more

successful than the ANN wavelet model in terms of EC prediction

phenomena. Several plans for the future are investigated and

submitted for further advancement and their potential. More

progress in function modeling in this direction is expected to be

promising with the ever-increasing potential of AI technologies.

Acknowledgment

The authors thank Universiti Teknologi Malaysia for its

financial support, (High Impact Research Grant Research No.

PY/2018/02893), No. vote Q.J130000.2451.04G62.

Ethical issue

Authors are aware of, and comply with, best practice in

publication ethics specifically with regard to authorship

(12).

Evaluation and Assessment

After ANNs and (chronologically) fuzzy theory, the largest

(avoidance of guest authorship), dual submission, manipulation

of figures, competing interests and compliance with policies on

research ethics. Authors adhere to publication requirements that

submitted work is original and has not been published elsewhere

in any language.

development in hydrological parameters is SVM. In most

literature reviews, conventional predictive regression models

have been found ineffective compared to AI-based models such

as ANN, ANFIS, and SVM. Although ANN is vulnerable to the

hidden nodes number, the SVM is responsive to the selection of

mapping kernels, which means the optimization of these variables

will achieve comparable results with both models. In the latest

studies conducted in this field, ANN has been the most popular

technique.

Competing interests

The authors declare that there is no conflict of interest that

would prejudice the impartiality of this scientific work.

Authors’ contribution

All authors of this study have a complete contribution for data

collection, data analyses.

Recommendation for Future Research

Another promising strategy in AI development is the hybrid

combination of two or more of the above techniques to create a

further flexible modelling scheme for water quality. Moreover,

progress in AI is made in two fields in parallel: fundamental tool

capabilities and actual implementations in solving water quality

issues. Research is presently ongoing to develop better AI

instruments that can provide better representational knowledge

systems, alternative analysis methods, and alternate processes to

address uncertain or insufficient information. The use of database

management systems, visual displays, and knowledge

improvement modules can improve the precision of existing

modelling systems worldwide. In this context, with simulation

systems evolving, the criteria for better AI software will be

increased, which in turn may lead to better AI technology

implementation strategies. Most notably, simulation systems must

step out from the laboratory and onto real practice. Continued

research will develop AI technology and its implementation in the

simulation of water quality.

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