Soft Computing Techniques for Predicting Chemical Oxygen Demand in River Water

Journal of Soft Computing in Civil Engineering 7-4 (2023) 110-131
How to cite this article: Sahu P, Londhe SN, Kulkarni PS. Soft computing techniques for predicting chemical oxygen demand in
river water. J Soft Comput Civ Eng 2023;7(4):110–131. https://doi.org/10.22115/scce.2023.366329.1544
2588-2872/ © 2023 The Authors. Published by Pouyan Press.
This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Contents lists available at SCCE
Journal of Soft Computing in Civil Engineering
Journal homepage: www.jsoftcivil.com
Soft Computing Techniques for Predicting Chemical Oxygen
Demand in River Water
Pali Sahu1*
, Shreenivas N Londhe2
, Preeti S Kulkarni3
1. Research Scholar, Civil Department, Vishwakarma Institute of Information Technology (VIIT), Pune, India
2. Professor, Civil Department, Vishwakarma Institute of Information Technology (VIIT), Pune, India
3. Associate Professor, Civil Department, Vishwakarma Institute of Information Technology (VIIT), Pune, India
Corresponding author: palisahu18@gmail.com
https://doi.org/10.22115/SCCE.2023.366329.1544
ARTICLE INFO ABSTRACT
Article history:
Received: 19 October 2022
Revised: 22 April 2023
Accepted: 23 April 2023
Organic matter in water is assessed through Chemical
oxygen demand (COD). COD prediction utilizing Data
driven technique (DDT) has shown to be promising and may
be utilized as supplemental techniques due to the time-
consuming procedure and nonlinear correlations between the
factors. The current study aims to determine how well three
different DDT, namely Artificial Neural Network (ANN),
Multi-Gene Genetic Programming (MGGP), and Model Tree
(M5T), can estimate the concentration of COD in water
taken from three different sections of the Mula, Mutha, and
Mula-Mutha Rivers in Pune, India. The performance of the
models demonstrates that both ANN and MGGP worked
brilliantly, with a correlation coefficient (between observed
and projected values) that was more than 0.88 and a root
mean square value of 0.7 mg/l across all three parts. The
input frequency distribution in MGGP and the input variable
coefficient in M5T indicate that both techniques can identify
the influential factors. MGGP and MT score with readily
available equations as model.
Keywords:
ANN;
MGGP;
Soft computing;
Modelling;
Water quality;
Chemical oxygen demand.

P. Sahu et al./ Journal of Soft Computing in Civil Engineering 7-4 (2023) 110-131 111
1. Introduction
River water quality is mainly assessed on the basis of organic matter content, like biochemical
oxygen demand (BOD) and chemical oxygen demand (COD). BOD is the quantity of dissolved
oxygen consumed by aerobic microorganisms to oxidize organic matters [1]. The COD
measures the total amount of oxygen-consuming compounds in the full chemical breakdown of
organic molecules in water. It is a key measure for monitoring water quality and defining the
sort of organic load present [2]. The traditional approach for COD analysis is done by multistep
chemical technique i.e., reflux method, which is a nearly 2-hour operation, in which sealed
tubes filled with chemicals are heated to 150 degrees, creating tremendous pressure beneath the
tubes, thus making the analysis risky due to the risk of explosion [3,4]. Additionally, the
presence of various inorganic interfering matter, most of the time distorts the outcome of the
analysis and makes this process less accurate [3]. Hence, with limited resources and time, cost-
effective model development with accurate modelling and forecasting of river water quality is
necessary for ecologically sound water management. Traditional deterministic and probabilistic
models need precisely established rate coefficients, many of which are temporally and
geographically unique, for a variety of hydro-chemical, physical, and biological processes [4].
To circumvent these constraints, researchers have developed Data-driven techniques (DDT) to
model water quality parameters. DDT is a computational approach that uses system state
variables (input-output) to replace physical-behaviour-based knowledge-driven models [5].
DDT can be Soft computing and Hard computing. Soft computing employs partial truth,
ambiguity, and approximation. Soft computing has grown in popularity due to its various
features such as optimisation, intelligent control, decision-making, and nonlinear programming
[5]. Adaptive-network-based fuzzy inference system (ANFIS), artificial neural network (ANN),
Multi-Layer-Perceptron (MLP), genetic algorithm (GA), and Fuzzy Logic (FL) are some of the
most often utilized DDT for water quality metrics [5–10]. Over the last decade, several articles
have asserted that DDT based models accurately replicate dissolved oxygen (DO), BOD
concentration, and other important water quality variables.
Out of the various techniques mentioned above, ANNs have been utilised for many water and
environmental research like Mehr [11] employed GP-SARIMA to improve long-term
streamflow forecasting in a lake-river system of Oulujoki River, Finland. A model for one-step-
ahead streamflow predictions is tested. The results demonstrated that a combination of
correlogram and average mutual information (AMI) analysis may help to pick the best lags for
streamflow model predictors. Karami [12] employed Neural Network based method (NN) to
model and simulate rate of evaporation for Garmsar city of Iran. Testing phase results shows
that the NN model is able to simulate evaporation with minimum error. While Palani et al. [6]
utilised ANN to predict and forecast temperature, salinity and DO in Singapore coastal waters.
Singh et al. [7] utilized ANN technique to simulate the DO and BOD levels in the Gomti River
(India). `Najah et al. [13] and Basant et al.[14] employed linear and nonlinear modelling using
ANN’s feed-forward-back-propagation and partial least squares (PLS) regression to forecast the
DO and BOD levels in river water at the same time. Results revealed that both models could
predict DO and BOD levels, but the non-linear (ANN) model outperformed the linear (PLS)

112 P. Sahu et al./ Journal of Soft Computing in Civil Engineering 7-4 (2023) 110-131
model. The application of ANN for the prediction and modelling of COD removal from
antibiotic aqueous system by the Fenton process was explored by Elmolla et al. [15] and found
that the outcome was extremely close to the actual data, with high value of R2
(coefficient of
determination) i.e., 0.997 and an MSE of 0.000376. Emamgholizadeh et al. [16] employed
MLP, radial basis network (RBN) and ANFIS to model three important water quality
parameters (DO, BOD, and COD) for highest water flow Iranian Karoon River. Results
demonstrated that estimated values of DO, BOD, and COD by using both ANN and ANFIS
were reflecting balanced scatter with observed values (R= 0.86, 0.94, 0.96 respectively).
Akilandeswari and Kavitha [8] estimated COD concentration for textile effluent using the
ANFIS and multiple linear regressions (MLR). As a consequence, the ANFIS technique
outperformed the MLR in modelling COD concentration. Convolutional Neural Network-Long
Short-Term Memory Network (CNN-LSTM) technology, which is based on an attention
mechanism, was used by Xijuan et al. [17] to anticipate the water quality of the Yellow River in
China. The results show that the hybrid model performs better than the conventional NN model
in solving nonlinear time series prediction issues. In Kayseri, Turkey, Ozkan [18] used MLP to
forecast BOD in a sewage water treatment facility using temperature, suspended solids (SS),
COD, total dissolved solids (TDS), total nitrogen, and total phosphorous. The correlation value
was 0.915, and the ANN technique for modelling DO prediction was determined to be
satisfactory. Dogan et al. [19] used chemical oxygen demand, flow (Q), temperature, nitrites,
total ammonia, and nitrate variables to explore the possibility of an `MLP model to increase the
precision of BOD prediction in the Melen River (Turkey) and findings were satisfactory
(R=0.89). Heddam & Kisi [9], explored different approach i.e., multi-variate adaptive
regression spline (MARS), model tree (MT) in order to model DO and evaluate its correctness
using a least-squares support vector machine (LSSVM). Compared to LSSVM, MARS fared
the best, with a R of 0.965 and RMSE and MAE of 0.547mg/L and 0.386 mg/L, respectively.
Similarly, Mehr et al. [20] used a multi-step evolutionary search algorithm in which high-
performance rain-borne genes from a multigene GP solution is combined through a classic
Genetic Programming engine for predicting 1 month ahead rainfall measurements from
meteorology stations in Lake Urmia Basin, Iran. The model proposed outperforms the
benchmark models: standard GP and autoregressive state-space in both the rain gauge stations.
The results show around 24% and 60% improvement (average of two case studies) in terms of
Root mean square error and Nash-Sutcliffe coefficient of efficiency metrics. The proposed
model: Multiple genetic programming (MGP) eliminates genes of lower performance and only
allows those of higher performance to contribute to the final solution is stated as the reason for
better accuracy.
The majority of research reveals that most of the work is focused on the prediction of DO and
BOD for river water rather than COD and employs ANN, ANFIS, and a small number of
studies also used LGP and SVM techniques to simulate water quality matrices. Despite the fact
that these models do enhance accuracy to some degree, they also have certain drawbacks. For
instance, ANN's major operational hurdle is its inability to generate the final outcome using a
simple mathematical equation, making it less portable, whereas ANFIS was limited by its
computing complexity. As a result, the established model for monitoring water quality has to be

revised in order to reduce the inaccuracies, computational complexity, and over-fitting issues
that plagued prior approaches. In light of the above information, to the author's knowledge, no
research has been conducted to leverage the potential of data-driven methodologies such as
multi-gene genetic programming (MGGP) and model tree (M5T) for the prediction of water
quality variables. Thus, current study is focused to utilize Multi gene genetic programming and
Model tree with M5 algorithm to predict COD content for Mula-Mutha river Pune, India and
compare the results with ANN model [8,15,18,19].
The water quality data for Mula, Mutha and Mula-Mutha River were employed over the last
fifteen years (2003-2018) to attain this goal. The outcome of three developed models were
analyzed and compared using root mean square error (RMSE), mean absolute relative error
(MARE), and coefficient of correlation (R) statistical error measures, as well as visual
presentation of values on graphs and scatter plots between actual recorded and model-predicted
values. The next part will discuss the techniques employed, followed by information on the water
quality data and study area. The results and discussions will follow next and the study will be
concluded by conclusion.
2. Techniques utilized
Soft computing techniques treat human brain as their role model and mimic the ability of the
human mind to effectively employ modes of reasoning that are approximate rather than exact.
They do not assume any mathematical model a priori and hence are more flexible in data mining.
The underlying idea of soft computing is to provide tractability, resilience, and a better
connection with reality by allowing for imperfection, ambiguity, and partial truth. In the current
study soft computing techniques like Artificial Neural Network and Genetic Programming are
utilized along with other Model Tree with M5 technique.
2.1. Artificial neural network
Artificial Neural Network is a massively parallel, distributed processor with a natural tendency
for storing experiential information to make it accessible for usage. In two aspects, it resembles
the human brain: it utilizes information to recognize complex nonlinear behavior or patterns
acquired by the network throughout the learning process, and it retains knowledge using synaptic
weights, which are the strengths of interneuron connections. In general, an ANN is made up of
three layers: an input layer, one or more hidden layer(s), and an output layer. Synaptic weights,
biases, and transfer functions link the hidden layer to the other layers. Error functions are based
on network output against aim. Modifying weights and biases backward using algorithms
reduces error until the desired output accuracy is obtained. Training ANN via feed forward back
propagation distributes error backward and processes weight and bias forward. Until output
accuracy is achieved, the training cycle is repeated. The network's weights and biases can be
used to validate unknown data after training. The connection between weights, input, and output
is shown in Figure 1. Readers may refer to [5,10,21–24] for more information on how an ANN
works in detail.

Fig. 1. Typical three-layer network architecture of ANN [25].
2.2. Model tree
Model Tree employs a divide-and-conquer strategy to offer rules for a linear model to arrive at
the leaf node. The linear models are then used to determine how much each parameter
contributes to the total anticipated value. Quinlan's M5 technique is reconstructed as M5 Model
Tree (M5T) to trigger decision/regression trees of models [26]. M5T is a decision tree that
incorporate a traditional decision tree with the possibility to use linear regression algorithms at
the last nodes. To begin, a tree is built using the decision tree technique, and the splitting
approach is utilized to curtail the standard deviation (sd) in the intra-subset of the M5 Model
tree, resulting in linear models in the leaf node [27,28]. The M5T approach utilizes the two
important steps: the tree growth step (splitting) and the tree pruning step [29,30]. Figure 2 shows
how the M5 method of Model tree splits the input X1 X2 input variables into multiple alternative
linear regression functions as model namely LM1-LM6 at leaf node. The model equation is y =
b0 + b1X1 + b2X2, where b0, b1, and b2 are linear regression constants and depicts the relationship
between branches as a tree diagram [29,31] are references for MT readers.
Fig. 2. Diagram of model tree with LM 1-6 at the leaves.
2.3. Genetic programming
Genetic programming (GP) is a domain-agnostic approach for resolving problems by breeding a
population of randomly produced computer programs genetically. It belongs to the category of
supervised machine learning, in which the answer is found in a program space rather than a data
space. Traditional GP solutions are expressed as tree structures and stated in a functional
programming language. Genetic Programming (GP) is based on the survival of the fittest

concept, which states that the fittest will live and participate in the next generation's evolution by
breeding. The three genetic operations for breeding are as follows: Reproduction, Mutation, and
Crossover.
Fig. 3. Example of standard genetic programming (GP) tree representation for the function.
A few GP variants include GEP, LGP and MGGP. GEP employs defined length linear genomes,
also known as chromosomes, to describe computer programmes in the form of expression trees.
The chromosome is made up of a linear, symbolic string of single or multiple genes that is fixed
in length. The ability of GEP chromosomes to code ETs of all sizes and forms will also be
demonstrated, despite their constant length. A specific type of linear representation is used for
computer programmes in LGP, a linear version of GP. Instead of functional genetic programmes
that are restricted to a single linear list of nodes, the term "linear" refers to the shape of the
(imperative) programme representation. Expressions from functional programming languages,
such as LISP, are swapped out for imperative programming language programmes, like C or
C++, in LGP. LGP's main characteristics are the graph-based data flow from indexed variables
and programming in a low-level language that allows solutions to be directly changed into binary
machine codes and run without an interpreter [32]. It is intended to build "multi-gene"
mathematical models for specific response (output) data. In general, nonlinear input variables are
merged into a low-order linear weighted tree (by limiting the gene or tree depth). The evaluation
of a single tree (model) expression is used in the standard GP form. Multigene individuals in
MGGP are made up of several genes, each of which corresponds to the "conventional" GP tree
expression [32,33]. For MGGP model development firstly population and generation size are
determined by the issue's complexity and the number of possible solutions. To construct models
with the least level of uncertainty, several populations and generations are studied. Genes are
changed by natural selection via mutations and crossings to produce offspring. The mutation
mechanism selects branches and sub-nodes, replacing each with a random subtree. During the
crossover procedure, random parent tree terminals or branched nodes are picked and swapped.
This procedure is repeated until the termination requirement is reached, improving model fitness.
Although a maximum number of genes (Gmax) of an individual and maximum depth of tree
(Dmax), directly influence the size of the search space and the number of solutions explored
within it. The MGGP algorithm's success frequently increases with these settings. Finally, the
best model is chosen from the output values based on simplicity and fitness. Parameters allow

the user to change the model's simplicity (e.g., Gmax or Dmax). Figure 4 depicts a typical
MGGP model built from two standard GP trees. With input variables x1, x2, and x3, this model
predicts an output variable. Although linear in the parameters concerning the coefficients
variables d0, d1& d2, the given model structure incorporates nonlinear components (e.g., tan, sin,
sqrt. etc.). The fundamental arithmetic operators (/, x, +, - etc.) and Boolean logic functions are
included in the function set (sin, cos, tanh, etc.). For details of GP, readers are referred to [33,34]
and MGGP readers are referred to [34].
Fig. 4. Example of MGGP symbolic methods.
3. Study area
The current research is focused on Pune city, a developing city in Maharashtra, which is blessed
with the rivers Mula and Mutha and are major sources of water supply. Pune is located at 18° 31'
22.45" of North and 73° 52' 32.69" of East, near the western boundary of the Deccan Plateau.
Pune lies on the downwind side of the Sahyadri hills and Western Ghats, 1837 feet above the sea
level, at the conflux of the Mula and Mutha rivers, which are tributaries of the River Bhima. The
Mula River flows roughly 64 kilometers from its source in the Pune District's hilly areas, with 40
kilometers of it being mountainous terrain. It then approaches Pune from the north-west, passing
through thickly populated districts before meeting the Mutha River [35]. Within the Pune
Metropolitan Region, many small town and small & micro scale (SSI) industries like paper-pulp
mills and sugar mills, including some agriculture runoff, are responsible for garbage generation
in the Mula River [36].
The Mutha River rises from Western Ghats and flows roughly 15 kilometers eastward until
merging into the Mula River near Pune [37]. Along the Mutha River in the Pune Metro-Politian
Region, there are several villages and historic city areas.
After the confluence of the Mutha and Mula Rivers at Sangamwadi, the united Mula-Mutha
River runs through the city of Pune (Fig. 5) and then travels downstream to merge with Bhima
River, a significant tributary of the Krishna River going southeast. The Mula-Mutha River is the
most contaminated since it carries trash from sewage treatment plants and common effluent
treatment plants [38]. The data was collected from stations M1, M2 and M3 as shown in figure 5
below.

Fig. 5. Detailed location map of water study area Mula-Mutha River (www.mahap.gov.in).
4. Data set
Monthly water quality data for the rivers: Mula, Mutha and Mula-Mutha, requisite for the present
research was received from Hydro Nashik Maharashtra, India for the years 2003 to 2018.The
statistical analysis of the data from the year 2003 to 2018 for the reaches of the Mula, Mutha, and
Mula-Mutha rivers utilized in this research is provided in table 2, Statistical analysis provides
insight into or underlying pattern of any data, according to table 2, the skewness coefficient
(Csx) is somehow very less for the majority of data sets, which is considered desirable since a
high value of skewness has a detrimental effect on ANN performance. Additionally, coefficients
of variation indicated significant changes in the Mula-Mutha River as compare to Mula River
and Mutha River, owing to the river's route through various townships and the presence of
numerous untreated wastewaters drains and tributaries.
Table 1
Correlation factor of COD with various influencing parameters.
Station Mutha Mula Mula-Mutha
Variables COD COD COD
BOD 0.97 0.85 0.88
DO -0.88 -0.73 -0.46
EC 0.77 0.7 0.005
Hardness 0.25 0.66 0.112
pH -0.31 -0.21 -0.579
TS 0.73 0.69 -0.26
TDS 0.78 0.71 -0.145
No3-No2 0.051 0.078 -0.458
No3-N 0.089 0.338 -0.019

Table 2
Statistical analysis of entire hydro-chemical data for Mutha, Mula and Mula-Mutha station.
Station Parameters Unit Min Max Mean Sd Csx CV%
Mutha River
COD mg/L 1.6 140 22.65 23.52 1.35 103.8
DO mg/L 0.2 9.2 5.7 3.1 1.24 54.3
BOD mg/L 0.4 38 0.8 9.5 1.54 118.75
EC µs/cm 2 640 216 172 1.66 79.62
TS mg/ L 50 413 176 98 0.62 55.68
Hardness mg/ L 24 231 88.76 54.14 1.15 60.95
TDS mg/ L 13 381 134 102 1.59 76.1
pH Unit 6.7 8.9 7.9 0.5 0.48 6.32
No3-N mg/ L 0.01 11.31 0.24 0.86 1.04 358
No3-No2 mg/ L 0.01 0.35 0.26 0.45 0.07 173
Mula River
COD mg/L 1.6 76.8 21.19 14.6 1.39 66.6
DO mg/ L 0.28 9 4.6 2.7 1.23 58.5
BOD mg/ L 0.8 38 3.2 6.8 1.54 212
EC µs/cm 62 712 304 192 1.17 9.5
TS mg/ L 55 471 211 109 1.06 51.6
TDS mg/ L 45 434 180 108 1.05 6
Hardness mg/ L 24.6 284 102.3 26.5 0.53 25.9
pH Unit 6.93 8.75 7.7 0.3 0.08 3.8
No3-N mg/L 0.01 10.6 0.29 1.29 1.15 445
No3-No2 mg/ L 0.02 3.88 0.27 0.53 0.96 196
Mutha-Mula River
COD mg/L 5.6 162 29.62 18.99 1.14 64.11
DO mg/L 0 9.4 3.6 3 1.2 83.33
BOD mg/ L 6 48 11.8 9.53 1.6 80.76
EC µs/cm 84 980 436 239 1.12 54.8
TS mg/ L 100 900 359 144 0.3 40.11
TDS mg/ L 57 820 315 137 0.33 43.49
Hardness mg/ L 58.8 316 142.33 36.5 0.77 25.64
pH Unit 6.6 8.7 7.7 0.7 0.05 9.09
No3-N mg/ L 0.01 14.02 1.9 2.26 0.71 118.7
No3-No2 mg/l 0.03 8.07 0.89 1.64 1.32 179.7
The human activities and the nonlinear and complicated biochemical processes have a significant
impact on the quality characteristics of water. Water quality variables such as dissolved oxygen
(DO), biochemical oxygen demand (BOD), total solids (TS), alkalinity (Alk.), pH and electrical
conductivity (EC) etc. all have an influence on the chemical oxygen demand (COD) of a given
sample. For model development, it is vital to understand the relationship between these
components and COD, like pH is a critical indicator of water quality because it is regulated by a
series of interrelated chemical process that produce or utilize H+
or OH-
ions. A low pH implies
that organic compound is decomposing [39]. Various inorganic matter and salt ions dissolve in
water and break-down into minute electrically charged particles called ions, hence boosting the

conductivity of water while lowering its solubility of oxygen. Similarly, when the salt content
increases, the total dissolved solids in the river water stream increase, resulting in less
atmospheric oxygen being dissolved. As noted, before, BOD and COD are indicators of the
amount of biodegradable and non-biodegradable organic matter in a water sample that must be
oxidized. As a consequence, total solids directly influence the COD content. Additionally, while
nitrite is extremely toxic to aquatic life, it rapidly oxidizes to nitrate, which promotes the growth
of water hyacinth, which covers the surface of a river, obstructing sunlight from reaching
beneath the water's surface, reducing aeration and, as a result, increasing the demand for
dissolved oxygen [39,40]. To select an adequate group of input variables from all possible
variables is crucial for developing a high-quality model in any form of DDT model creation
[4,5,11]. Numerous ways to choose input variables are explored in various studies. They are
based on heuristics, expert knowledge, statistical analysis, or a combination of these [41–43].
However, there is a compelling need to do a thorough analysis of the input variable selection
process. At the moment, there is no consensus on how this task should be carried out [44,45].
Thus, to identify input variables for the prediction of COD; expert knowledge, correlation factor
(Table 1) and statistical analysis (Table 2) were utilized. A statistical approach has traditionally
been used for providing a representative and reliable analysis of the water quality data. The
statistical analysis of water quality characteristics reveals that the lowest values of all parameters
were within the acceptable range; however, the maximum values were fairly high. In addition,
the pattern of variation for the stretch between the Mula River and Mutha River was almost
same, although substantial variations were detected in the Mula-Mutha River. Considering the
above variation, correlation, and analysis, nine water quality parameters were identified for the
prediction of COD for all three stretches. They are: BOD, DO, pH, hardness, electrical
conductivity, total solids, total dissolved solids, nitrite, and nitrate (No3-No2/No3-N). Table 1
depicts the relationship between input parameters and COD for all three lengths of the Mula-
Mutha River. The key contributing variables for COD in the Mutha and Mula rivers are BOD,
DO, electrical conductivity (EC), total dissolved solids (TDS), total solids (TS), hardness
(Hardn.) and nitrite (No3-N). However, for the Mula-Mutha River, a distinct pattern is found,
with BOD, pH, nitrate (NO3-NO2), and DO as the key contributors following total solids, total
dissolved solids and hardness, since nitrite quickly oxidizes to nitrate in the presence of
dissolved oxygen, reflecting the breakdown or decomposition of organic matter (i.e., BOD).
5. Model development
The current study aims to explore the feasibility and effectiveness of DDT- MGGP and M5T to
predict COD content for rivers: Mula, Mutha and Mula-Mutha. As discussed in the preceding
section and illustrated in tables 1 and 2, all three stretches of river demonstrated distinct patterns
of influence with respect to the input parameters because of different point and non-point sources
of pollution and variation in the population nearby the river stretch. Hence, for Mutha river and
Mula river commonly seven parameters were finalized out of nine i.e., BOD, DO, EC, hardness,
TS, TDS, No3-N, while for Mula-Mutha River-BOD, DO, pH, hardness, TS, TDS, No3-No2

were finalized. Table 3 shows the model information for each set, including model numbers and
input variables for each length of the Mula, Mutha, and combined Mula-Mutha River.
To predict COD, ANN’s 3-layered FFBP model was trained to a low targeted error (mean
squared error). ANN models were developed by utilizing the levernberg-marquardt algorithm to
train the network using the "log-sigmoid" and "purelin" transfer functions. The data was
standardized from 0 to 1. There are 7 neurons (or nodes) in the input layer, 1-15 neurons (or
nodes) in the hidden layer, and 1 neuron (or node) in the output layer. The trial-and-error
technique were utilized to determine the hidden nodes/neurons because finding a suitable number
of nodes in the hidden layer is critical, as a greater number may result in over-fitting, meanwhile,
a lesser number may not capture the information sufficiently [45]. All models were trained until a
minimum-error target was reached, and their weights and biases were saved for testing on the
remaining data sets. The ANN was trained in MATLAB 9.1 (2016) [45–47].
MGGP models were developed in the MATLAB 2016 with the source code of GPTIPS. The
control parameters, such as the initial population, mutation frequency, and crossover frequency,
were changed in every run to check the accuracy of the model. Every run was kept going until
there was no more significant drop-in fitness, which meant generations without any more
progress. In a single run, evolved programs are chosen for each data space at random. The
parameters for a GP run were as follows: the population size was 500, the crossover rate was
84% and the mutation rate was 14%, The mean squared error was selected as the fitness criteria
[25]. The best MGGP models were chosen based on high accuracy and less complexity. Pareto
charts are also used to judge the complexity of a model. These charts show how the model
evolved in terms of both complexity and accuracy. The MGGP algorithm was run several times
with different populations and generations until the best model was found.
The M5 algorithm for MT was made with WEKA 3.9 [28]. The M5T technique employs the
decision tree induction process to construct the tree, and the splitting function tries minimize the
amount of intra-subset variation present on each level/branch of the tree. The tree was built using
the standard deviation reduction factor (SDR) by decreasing intra-subspace variability and intra-
subspace variability with a divide and conquer function [48]. The data of instances was divided
into 70% for calibration, 15% for validation and 15% for testing respectively. Actual data was
compared to projected or modelled values during the course of this investigation. Statistical
indicators were utilised to assess the effectiveness of the developed DDT model e.g.: Root mean
squared error (RMSE), Mean absolute relative error (MARE), and Coefficient of correlation (R)
with visual presentation of values on graphs and scatter plots between observed and model-
predicted values. RMSE and MARE should be as low as possible, and R ought to be close to 1,
which is deemed to be the most accurate model [34]. For a model to be used in real life, it needs
to be shown in a way that is easy to understand. For example, ANN shows the model as a set of
trained weights and biases, M5T as a set of linear equations, and MGGP as a simplified equation.
The MGGP method is known for the fact that the parameters that have little or no effect are
removed from the final equation. In this way, these techniques can be used on-site and the
confidence in the results can be raised.

Table 3
Detailed information of the developed model.
S. No.
Model
No.
Station Input variables
No.
Data
Output
variable
1
ANN 1
MGGP 1
M5T 1
Mutha
(14km stretch)
BOD, DO, EC, Hardness, TS, TDS, No-N3/ No3-N 144 COD
2
ANN 2
MGGP 2
M5T 2
Mula
(30 km stretch)
BOD, DO, EC, Hardness, TS, TDS, No-N3/ No3-N 206 COD
3
ANN 3
MGGP 3
M5T 3
Mula-Mutha
(25 km stretch)
BOD, DO, pH, Hardness, TS, TDS, No-N3/ No3-N 162 COD
5. Results and discussion
Table 4 displays the results of all three approaches (ANN, MGGP, and M5T) for predicting COD
for the rivers Mutha, Mula, and Mula-Mutha. Individual set performances are addressed in depth
further on. Table 5 illustrates the details of the best COD model in terms of the ANN model's
architecture, the number of linear equations developed by M5T, and the parameters not included
in the final developed equation by MGGP as output for the various models.
Table 4
RMSE, MARE & R for model developed using ANN, MGGP, and M5T for prediction of COD.
Targeted OutputTechnique Model No. RMSE (mg/L) MARE (mg/L) R
COD
ANN
1 (Mutha) 0.078 0.006 0.92
2 (Mula) 0.107 0.08 0.91
3 (Mutha-Mula) 0.015 0.003 0.90
MGGP
1 (Mutha) 0.68 0.009 0.91
2 (Mula) 0.109 0.009 0.86
3 (Mutha-Mula) 0.46 0.01 0.88
M5T
1 (Mutha) 1.35 0.09 0.91
2 (Mula) 2.23 0.19 0.90
3 (Mutha-Mula) 1.98 0.09 0.89
Table 5
Detailed information of model developed using ANN-MGGP-MT.
Targeted
output
Model No. ANN-Architecture
Number of the
equation in M5T
MGGP Parameters not
considered in the equation
COD
1 (Mutha) 7:5:1 1 Hardness, TS, NO3-NO2
2 (Mula) 7:8:1 3 TS, TDS, Hardness, NO3-NO2
3 (Mutha-Mula) 7:1:1 1 TDS, Hardness, EC
The effectiveness of an ANN model is contingent upon how well the developed or constructed
model is trained, and whether the synaptic weights and biases are appropriately adjusted to
provide desirable output. This research makes use of the root-mean-squared error, or RMSE,
since it illustrates the dispersion of the residual error (between observed and expected values),
also known as the standard deviation of the residuals. The ANN model-3 (Mula-Mutha)

exhibited reasonable performance, with an RMSE of 0.015 mg/L and an R of 0.91. Similarly,
ANN model-1 (Mutha) resulted in good performance with a RMSE of 0.078 mg/L and an R of
0.92. However, Model-2 (Mula) displays a high RMSE of 0.107 mg/L and R of 0.91. The higher
RMSE is because the data are more dispersed, as seen by the larger deviation.
The subsequent approach used is MGGP, which provide the outcome as a streamlined equation-
based model. MGGP instantly generates a mathematical-equation into a symbolic form that then
can be analyzed to see how various parameters influence the final output and in which trend; this
is the technique's distinctive feature (Refer equation 1).
𝐶𝑂𝐷 = 8.72𝑡𝑎𝑛ℎ(2.38𝑁𝑂3 − 𝑁𝑂2
2
𝑇𝑆2
) − 13.9𝑡𝑎𝑛ℎ(𝑡𝑎𝑛ℎ(2.45𝐷𝑂𝑁𝑂3 − 𝑁𝑂2)) +
11.2𝐵𝑂𝐷1/2
+ 5.81𝐷𝑂1/4
− 10.7 (1)
In MGGP each gene or tree has a unique weighted coefficient [33]. Table 6 and figure 6 reflect
that the statistical significance of genes 2, 4, and bias terms was greater than that of any other
gene present. This means the input parameters included in genes 2 and 4 (DO, Nitrates, and Total
solids) show a higher contribution or more influential toward the prediction of COD. To make
the model simpler to use, the final outcome is presented in the form of an equation that is a linear
sum of both the outputs and a bias value, the relative weights of which are indicated in Table 6
and illustrated in equation-1. The weight associated with each tree is derived using the least
squares approach by trying to reduce/minimize the goodness of fit error between the
predicted and the observed data. It is evident from equation 1 that the coefficient of weight for
BOD, nitrates, and total solids is greater than that of DO. This result is consistent with the basic
understanding of water quality acquired via environmental studies [49,50].
Table 6
Individual gene/tree weights (MGGP Model 3).
Term Value
Bias -10.7
Gene 1 5.81 (BOD)1/4
Gene 2 -13.9 Tanh(Tanh(2.45(DO×NO3-NO2)))
Gene 3 8.72 Tanh(2.38(NO3-NO2)2
(TS)2
)
Gene 4 -11.2 (BOD)1/2
Fig. 6. Weights of the genes and bias (MGGP model 3).

Figure 7 and table 7 illustrates input frequency analysis, that is used to determine whether input
variables are significant to the result for a particular model or a user-specified proportion of the
whole population [47,51]. Three of the seven input variables in the MGGP model are significant:
BOD, DO, and TDS, followed by hardness, TS and conductivity. MGGP is distinguished by its
ability to reject input variables that do not significantly contribute to the final outcome.
Table 7
Individual Gene/Tree weights (MGGP Model 1).
Term Value
Bias -7.9
Gene 1 -5.27 e-6
(BOD) (TDS)2
Gene 2 0.0471 (BOD-DO)2
Gene 3 12.3 (BOD)0.5
Gene 4 0.0058 (EC)-0.0058 (Hard.-7.45) (BOD-DO)
Fig. 7. Input frequency for MGGP model 1.
Table 1 also supports this statement as BOD, DO and TDS displayed the highest influence
towards COD as compare to other input variables. This finding is compatible with recognized
water quality concepts in environmental research [50]. Thus, one may conclude that MGGP's
data-driven method has a decent grasp of the underlying COD phenomena and the correlation
between the input and output parameters. In contrast to other methods, MGGP does not require a
transfer function in order to develop successive generations of "offspring" according to the
"specific fitness criteria" and genetic operations, allowing it to better detect and explore
underlying patterns [5]. Additionally, the MGGP approach gives the developer a range of model
possibilities. With the use of a Pareto chart, the best single model may be picked depending on
the application requirements. The Pareto chart (Fig. 8) plots expressional complexity versus
goodness of fit (R2
) or accuracy for models that are not dominated in terms of both complexity
and performance by other solutions. A tree's complexity may be quantified by the number of
nodes it contains, or by its expressive complexity [26,33]. Using a Pareto curve, a user may see
the performance of solutions and pick a solution that maintains a balance between complexity
and accuracy and its mathematical expression were presented in simplified equation [52]. The
optimal model (high accuracy and simplicity) is denoted by a red dot/circle. Pareto (Green dots)
models are those that are not substantially dominated by other models in terms of survival
or fitness and complexity, while Non-Pareto (Blue dots) models are those that are strongly
dominated.

Fig. 8. Pareto front model report for MGGP model 1.
Model Tree is the third strategy used to estimate COD, and it is based on the divide and conquers
principle. This technique measures the forecast value and sieves it on the routine route,
smoothing it at each node in accordance with the linear node value anticipated for that node
using the Linear Model. Figure 9 illustrates an example Model Tree generated using the M5
algorithm for M5T Model 2, along with developed linear regression equations. The below tree in
figure 9 illustrates linear models (LM1-3) at various leaf nodes. The very first number inside the
bracket indicates the number of related samples in the sorted subset of node, while the second
number indicates the root mean square error (RMSE) of the associated linear model divided by
the standard deviation of the sample's subset given in percentage [26]. Similarly, the number of
equations derived for different models is shown in Table 5. Linear equations developed for M5T
Model 2 using the M5 algorithm depict a negative coefficient for DO and a positive coefficient
for all other parameters, particularly BOD and hardness, indicating that increasing the
concentration of any of these impurities in a river increases the COD content, which is consistent
with the theoretical understanding of their influence on COD [49]. Thus, it can be observed that
M5T acquires a reasonable amount of knowledge about the underlying phenomena.
Fig. 9. Classifier tree for M5T model 2.
Linear equation developed for M5T Model 2 is as follows:
𝐶𝑂𝐷 = 0.69𝐵𝑂𝐷 − 0.0765𝑇𝐷𝑆 + 0.0475𝐸𝐶 + 0.0865 𝐻𝑎𝑟𝑑𝑛. +13.865 (2)

𝐶𝑂𝐷 = 1.40𝐵𝑂𝐷 − 5.755𝐷𝑂 − 0.983𝑇𝐷𝑆 + 0.0596𝐸𝐶 + 0.0565𝐻𝑎𝑟𝑑𝑛. +9.7162 (3)
𝐶𝐶𝑂𝐷 = 1.99𝐵𝑂𝐷 − 1.033𝐷𝑂 − 0.013𝑇𝐷𝑆 + 0.0067𝐸𝐶 + 0.215𝐻𝑎𝑟𝑑𝑛. +0.8904 (4)
It is clearly visible from the scatter plot (fig.10) that the predicted COD values for model 1 was
consistent with the actual values for all three developed models. All three data-driven strategies
seem to have caught the fundamental phenomena. In scatter plot, the regression line supported by
high value of correlation coefficient R (0.92, 0.91 and 0.91) respectively for ANN, MGGP and
M5T. Further comparison between data-driven strategies using RMSE reflects that ANN have
improved the performance of the proposed model as it has lower value of RMSE (0.078). The
RMSE is a statistic that measures the average difference between the model's predicted values
and the data set's actual values. The amount to which RMSE surpasses is a measure of the
presence of outliers in the data. The ANN algorithm creates an approximation function that
matches chosen input parameters to the intended outputs, which is then evaluated. As part of the
process, it makes adjustments to weights and biases to achieve the expected goal, which results
in a more flexible approach. While considering Eq. 5 for M5T model 1, showing direct
contribution of only three main input parameters i.e., BOD, TDS, and TS with COD, which is
also supported by fundamentals of water quality as COD, reflects the total organic matter in
water that is contributed by biodegradable matter as well as total solids too [49]. Hence M5T
Model 1 shows good performance as compare to M5T Model 2 &3 in terms of RMSE.
Fig. 10. COD model-1 by ANN-MGGP-M5T.
𝐶𝑂𝐷 = 2.234𝐵𝑂𝐷 + 0.0432𝑇𝑆 + 0.0726𝑇𝐷𝑆 + 7.7016 (5)
The scatter plot (figure 11) shows that ANN, MGGP, and M5T for model 2, all had a balanced
dispersion, except for a few high COD values recorded by the MGGP model with the lowest R =
0.86. In addition, the RMSE value for ANN (0.107) is the lowest, followed by MGGP (0.109)
and M5T (2.23). Eq. 6 generated by MGGP for model 2, emphasizes that it has considered only
three parameters (out of seven), namely BOD, DO, and EC, while rejecting all other parameters;
this may be the explanation for the model's poor performance, since TDS, TS, and hardness
contribute to an increase in organic matter (non-biodegradable) in water and also show a good
correlation with COD. Similarly, model trees don't employ all of the parameters included as input
variables to create linear models (LM) at each leaf node. Only those parameters that meet the
constraints of particular criteria (standard deviation reduction) fall under one sub-tree, which

ends in a leaf node, which is also reflected in eq. 2-4. Thus, this quality of M5T makes the model
outperform in terms of R (0.90).
1.26𝑒−4
(𝐸𝐶)2
𝑡𝑎𝑛ℎ (𝐷𝑂) + 3.9 (𝐵𝑂𝐷)3/4
𝑡𝑎𝑛ℎ(𝐵𝑂𝐷) − 3.13𝑒−5
(𝐷𝑂) (𝐸𝐶)2
+
8.29𝑒−4
(𝐷𝑂)2
(𝐸𝐶) + 1.39 (6)
ANN, MGGP, and M5T all showed good performance in terms of R as shown in the scatter plot
(figure 12) for model 3 (0.90-0.89). In terms of RMSE, however, the ANN and the MGGP model
came out on top (0.015 - 0.46). Table 2 demonstrate that the standard deviation of Mula and
Mutha data is lower than that of the combined Mula-Mutha dataset (model 3). Thus overall ANN
technique displays similar values as observed values which can be seen through lower RMSE
(0.078 with ANN, 0.680 with MGGP and 1.35 with MT) with Mutha river and other rivers as
well (refer table 4). All these models with ANN, MGGP and MT also support their performance
with lower MARE values (0.006 with ANN, 0.009 and 0.09 with MT) and higher r value (0.92
with ANN, 0.91 by MGGP and 0.91 with M5T). The similar trend is seen in models developed
for Mula and Mula-Mutha river. Lower RMSE shows weighted measure of the error in which the
standard deviation contributes the most between observed and modelled values. RMSE is
sensitive towards higher values, MARE shows its inclination towards lower predicted values and
r value towards the higher values. The higher value observed for Mula river is 56 and predicted
by ANN is 56.433, 64.545 by MGGP and 65.832 by MT. This trend is also seen in other
developed models as well. Thus, ANN predicts the values closer to observed values followed by
model developed using MGGP and then by M5T. P value analysis is another statical method used
to accept or reject the null hypothesis which states that there is no anomaly between the observed
value and predicted value of ANN, MGGP and M5T respectively. The p value for Mula river is
calculated as 0.551 with ANN, 0.981 with MGGP and 0.342 with M5T. P values for Mutha river
with ANN:0.763, MGGP:0.997 and M5T:0.870 along with p values for Mula-Mutha river (0.810
with ANN, 0.960 with MGGP and 0.741 with M5T). These p values for Mula, Mutha and Mula-
Mutha river are greater than 0.05 thus indicating the failure to reject the null hypothesis.
It is also dicussed in above section and reflected in table 1, that major contributing parameters
for Mula river and Mutha river where DO, BOD, EC, TS and TDS while for Mula-Mutha
Stretch, BOD, pH, nitrate (NO3-NO2), and DO were found to be major contributing parameters.
These discrepancies in data with its variation might be explained by variations in regional

climate and pollution patterns. The Divide and conquer approach used by M5T assist the leaf
node to determine splitting criteria by minimising the standard deviation. As already discussed in
the preceding section, the standard deviation of the 3rd stretch, i.e., Mula-Mutha, is on the higher
side, which indicates more spread of values and contributes to the larger margin error. Therefore,
M5T displays RMSE as 1.98, which is on the higher side than expected. Overall, ANN and
MGGP performed better than M5T for the majority of segments. When the current study's results
are compared to previous findings, it is noticed that Murat [53] utilized ANFIS to forecast COD
for a waste water treatment plant and obtained a minimum RMSE of 54.9 mg/L, which is rather
high when compared to current results; in contrast, Olyaie et al. [40] and Heddam [9] used ANN,
LSVM, and LGP to predict the water quality parameter DO and obtained RMSEs of 0.592 mg/L,
0.645 mg/L, and 0.374 mg/L, respectively, exhibiting a similar pattern as shown here.
7. Conclusion
In recent years, it has been shown that data-driven strategies perform best in complex, non-linear,
and unexpected environments. Analyzing environmental data using data-driven approaches like
ANN, MGGP, and M5T is the focus of this research. Using DDT in environmental research has
progressed over the last two decades, but the results also reveal areas that need more
investigation to improve water quality management. The present study highlighted the potential
of DDT in predicting water quality measures that are difficult to measure in the field. It's
reasonable to say that all of the models for the prediction of important water quality parameter
COD for all the three stretches of the river Mula-Mutha did well enough. ANN-developed
models outperformed MGGP and M5T with higher R and lower RMSE values. In terms of
accuracy, resilience, and fault tolerance, ANN excels all other analytical approaches because of
its model-free structure and capacity to map nonlinear input-output relationships. The MGGP
models 2 and 3 display better performance by providing RMSE values of less than 0.5 mg/L
(0.109, 0.46), and R values of more than 0.85 (0.86, 0.88). When compared to the M5T models,
the MGGP models exhibited a considerable reduction in their RMSE values, which is an
indication of significant progress. It would seem that the outcomes of all three models are
impacted by the data's inherent variability. Findings from this research also show that these three
models can learn from examples and display input parameters that are in sync with the
environmental studies domain knowledge that they were designed to learn from. It is also

suggested that the models be developed by combining data from all three stretches into a single
dataset, since this would give more data to train and perhaps increase accuracy.
Acknowledgments
I express my sincere gratitude to Nashik Hydro Work, Maharashtra for providing us water
quality data for my research work. I am thankful to Dr. Pradnya Dixit Associate Professor VIIT
college for the help and moral support to carry out the research.
Funding
This research received no external funding.
Conflicts of interest
The authors declare no conflict of interest.
Authors contribution statement
P.S. (Ph.D. Scholar) Conducted all the data collection, model formation, calculations and wrote
the manuscript. S.N.L (Professor) revised the manuscript. P.S.K. (Associate Professor) revised
the manuscript.
References
[1] Zare Abyaneh H. Evaluation of multivariate linear regression and artificial neural networks in
prediction of water quality parameters. J Environ Heal Sci Eng 2014;12:40.
https://doi.org/10.1186/2052-336X-12-40.
[2] Verma AK, Singh TN. Prediction of water quality from simple field parameters. Environ Earth Sci
2013;69:821–9. https://doi.org/10.1007/s12665-012-1967-6.
[3] Jingsheng C, Tao Y, Ongley E. Influence of High Levels of Total Suspended Solids on
Measurement of Cod and Bod in the Yellow River, China. Environ Monit Assess 2006;116:321–
34. https://doi.org/10.1007/s10661-006-7374-2.
[4] Rice EW, Bridgewater L, Association APH. Standard methods for the examination of water and
wastewater. vol. 10. American public health association Washington, DC; 2012.
[5] Londhe SN, Panchang V. Correlation of wave data from buoy networks. Estuar Coast Shelf Sci
2007;74:481–92. https://doi.org/10.1016/j.ecss.2007.05.003.
[6] Palani S, Liong S-Y, Tkalich P. An ANN application for water quality forecasting. Mar Pollut Bull
2008;56:1586–97. https://doi.org/10.1016/j.marpolbul.2008.05.021.
[7] Singh KP, Basant A, Malik A, Jain G. Artificial neural network modeling of the river water
quality—A case study. Ecol Modell 2009;220:888–95.
https://doi.org/10.1016/j.ecolmodel.2009.01.004.
[8] Akilandeswari S, Kavitha B. Comparison of ANFIS and statistical modeling for estimation of
chemical oxygen demand parameter in textile effluent. Der Chem Sin 2013;4:96–9.

[9] Heddam S, Kisi O. Modelling daily dissolved oxygen concentration using least square support
vector machine, multivariate adaptive regression splines and M5 model tree. J Hydrol
2018;559:499–509. https://doi.org/10.1016/j.jhydrol.2018.02.061.
[10] Maier HR, Dandy GC. Neural networks for the prediction and forecasting of water resources
variables: A review of modelling issues and applications. Environ Model Softw 2000;15:101–24.
https://doi.org/10.1016/S1364-8152(99)00007-9.
[11] Danandeh Mehr A, Ghadimi S, Marttila H, Torabi Haghighi A. A new evolutionary time series
model for streamflow forecasting in boreal lake-river systems. Theor Appl Climatol 2022;148:255–
68. https://doi.org/10.1007/s00704-022-03939-3.
[12] Karami H, Ghazvinian H, Dehghanipour M, Ferdosian M. Investigating the Performance of Neural
Network Based Group Method of Data Handling to Pan’s Daily Evaporation Estimation (Case
Study: Garmsar City). J Soft Comput Civ Eng 2021;5:1–18.
https://doi.org/10.22115/scce.2021.274484.1282.
[13] Najah A, El-Shafie A, Karim OA, El-Shafie AH. Application of artificial neural networks for water
quality prediction. Neural Comput Appl 2013;22:187–201. https://doi.org/10.1007/s00521-012-
0940-3.
[14] Basant N, Gupta S, Malik A, Singh KP. Linear and nonlinear modeling for simultaneous prediction
of dissolved oxygen and biochemical oxygen demand of the surface water — A case study.
Chemom Intell Lab Syst 2010;104:172–80. https://doi.org/10.1016/j.chemolab.2010.08.005.
[15] Elmolla ES, Chaudhuri M, Eltoukhy MM. The use of artificial neural network (ANN) for modeling
of COD removal from antibiotic aqueous solution by the Fenton process. J Hazard Mater
2010;179:127–34. https://doi.org/10.1016/j.jhazmat.2010.02.068.
[16] Emamgholizadeh S, Kashi H, Marofpoor I, Zalaghi E. Prediction of water quality parameters of
Karoon River (Iran) by artificial intelligence-based models. Int J Environ Sci Technol
2014;11:645–56. https://doi.org/10.1007/s13762-013-0378-x.
[17] Wu X, Zhang Q, Wen F, Qi Y. A Water Quality Prediction Model Based on Multi-Task Deep
Learning: A Case Study of the Yellow River, China. Water 2022;14:3408.
https://doi.org/10.3390/w14213408.
[18] Ozkan O, Ozdemır O, Azgın ST. Prediction of biochemical oxygen demand in a wastewater
treatment plant by artificial neural networks. Asian J Chem 2009;21:4821–30.
[19] Dogan E, Sengorur B, Koklu R. Modeling biological oxygen demand of the Melen River in Turkey
using an artificial neural network technique. J Environ Manage 2009;90:1229–35.
https://doi.org/10.1016/j.jenvman.2008.06.004.
[20] Danandeh Mehr A, Safari MJS. Multiple genetic programming: a new approach to improve
genetic-based month ahead rainfall forecasts. Environ Monit Assess 2019;192:25.
https://doi.org/10.1007/s10661-019-7991-1.
[21] Ay M, Kisi O. Modeling of Dissolved Oxygen Concentration Using Different Neural Network
Techniques in Foundation Creek, El Paso County, Colorado. J Environ Eng 2012;138:654–62.
https://doi.org/10.1061/(ASCE)EE.1943-7870.0000511.
[22] ASCE Task Committee on Application of Artificial Neural Networks in Hydrology. Artificial
Neural Networks in Hydrology. I: Preliminary Concepts. J Hydrol Eng 2000;5:115–23.
https://doi.org/10.1061/(ASCE)1084-0699(2000)5:2(115).
[23] Dawson CW, Wilby RL. Hydrological modelling using artificial neural networks. Prog Phys Geogr
Earth Environ 2001;25:80–108. https://doi.org/10.1177/030913330102500104.

[24] Jain P, Deo MC. Neural networks in ocean engineering. Ships Offshore Struct 2006;1:25–35.
https://doi.org/10.1533/saos.2004.0005.
[25] Shahin MA. State-of-the-art review of some artificial intelligence applications in pile foundations.
Geosci Front 2016;7:33–44. https://doi.org/10.1016/j.gsf.2014.10.002.
[26] Quinlan JR. Learning with continuous classes. 5th Aust. Jt. Conf. Artif. Intell., vol. 92, World
Scientific; 1992, p. 343–8.
[27] Kulkarni P, Londhe SN, Dixit PR. A comparative study of concrete strength prediction using
artificial neural network, multigene programming and model tree. Chall J Struct Mech 2019;5:42.
https://doi.org/10.20528/cjsmec.2019.02.002.
[28] Hashmi S, Halawani SM, Barukab OM, Ahmad A. Model trees and sequential minimal
optimization based support vector machine models for estimating minimum surface roughness
value. Appl Math Model 2015;39:1119–36. https://doi.org/10.1016/j.apm.2014.07.026.
[29] Abolfathi S, Yeganeh-Bakhtiary A, Hamze-Ziabari SM, Borzooei S. Wave runup prediction using
M5′ model tree algorithm. Ocean Eng 2016;112:76–81.
https://doi.org/10.1016/j.oceaneng.2015.12.016.
[30] Solomatine DP, Xue Y. M5 Model Trees and Neural Networks: Application to Flood Forecasting
in the Upper Reach of the Huai River in China. J Hydrol Eng 2004;9:491–501.
https://doi.org/10.1061/(ASCE)1084-0699(2004)9:6(491).
[31] Solomatine DP, Dulal KN. Model trees as an alternative to neural networks in rainfall-runoff
modelling. Hydrol Sci J 2003;48:399–411. https://doi.org/10.1623/hysj.48.3.399.45291.
[32] Searson DP, Leahy DE, Willis MJ. GPTIPS: an open source genetic programming toolbox for
multigene symbolic regression. Proc. Int. multiconference Eng. Comput. Sci., vol. 1, Citeseer;
2010, p. 77–80.
[33] N. S, R. P. Genetic Programming: A Novel Computing Approach in Modeling Water Flows. Genet.
Program. - New Approaches Success. Appl., IntechOpen Publishing London, UK; 2012.
https://doi.org/10.5772/48179.
[34] Gandomi AH, Alavi AH. A new multi-gene genetic programming approach to nonlinear system
modeling. Part I: materials and structural engineering problems. Neural Comput Appl
2012;21:171–87. https://doi.org/10.1007/s00521-011-0734-z.
[35] Pune Municipal Corporation. JICA Project | Pune Municipal Corporation 2023.
https://www.pmc.gov.in/en/jica-project.
[36] Report On Environmental Status of Pune Region. Kalpataru Point, Sion Circle, Sion (East)
Mumbai. n.d.
[37] Sahu P, Karad S, Chavan S, Khandelwal S. Physicochemical Analysis Of Mula Mutha River Pune.
Civ Eng Urban Plan An Int J 2015;2.
[38] Central Pollution Control Board (CPCB) | The Official Website of Ministry of Environment, Forest
and Climate Change, Government of India n.d.
[39] Fletcher D, Goss E. Forecasting with neural networks. Inf Manag 1993;24:159–67.
https://doi.org/10.1016/0378-7206(93)90064-Z.
[40] Olyaie E, Zare Abyaneh H, Danandeh Mehr A. A comparative analysis among computational
intelligence techniques for dissolved oxygen prediction in Delaware River. Geosci Front
2017;8:517–27. https://doi.org/10.1016/j.gsf.2016.04.007.

[41] Ranković V, Radulović J, Radojević I, Ostojić A, Čomić L. Neural network modeling of dissolved
oxygen in the Gruža reservoir, Serbia. Ecol Modell 2010;221:1239–44.
https://doi.org/10.1016/j.ecolmodel.2009.12.023.
[42] Chowdhury MZI, Turin TC. Variable selection strategies and its importance in clinical prediction
modelling. Fam Med Community Heal 2020;8. https://doi.org/10.1136/fmch-2019-000262.
[43] Adeniran KA, Adelodun B, Ogunshina M. Artificial Neural Network Modelling of Biochemical
Oxygen Demand and Dissolved Oxygen of Rivers: Case Study of Asa River. Environ Res Eng
Manag 2017;72:59–74. https://doi.org/10.5755/j01.erem.72.3.14120.
[44] G. E. McCuen. Protecting water quality 1986:180.
[45] Hem JD. Study and interpretation of the chemical characteristics of natural water. US Geological
Survey; 1959. https://doi.org/10.3133/wsp1473_ed1.
[46] MathWorks Announces Release 2016b of the MATLAB and Simulink Product Families -
MATLAB & Simulink. MathWorks 2016.
[47] Singh HK. Prediction of shear strength of deep beam using Genetic Programming 2014.
[48] Melesse AM, Khosravi K, Tiefenbacher JP, Heddam S, Kim S, Mosavi A, et al. River water
salinity prediction using hybrid machine learning models. Water 2020;12:2951.
[49] Garg SK. Water Supply Engineering. Khanna Publishers; 2010.
[50] Metcalf E. Wastewater Engineering Treatment and Reuse (4th edition) (2004) | Akhid Maulana -
Academia.edu. 4th editio. 2004.
[51] Rahimikhoob A, Behbahani SMR, Banihabib ME. Comparative study of statistical and artificial
neural network’s methodologies for deriving global solar radiation from NOAA satellite images.
Int J Climatol 2013;33:480–6. https://doi.org/10.1002/joc.3441.
[52] Danandeh Mehr A, Jabarnejad M, Nourani V. Pareto-optimal MPSA-MGGP: A new gene-
annealing model for monthly rainfall forecasting. J Hydrol 2019;571:406–15.
https://doi.org/10.1016/j.jhydrol.2019.02.003.
[53] Ay M, Kisi O. Modelling of chemical oxygen demand by using ANNs, ANFIS and k-means
clustering techniques. J Hydrol 2014;511:279–89. https://doi.org/10.1016/j.jhydrol.2014.01.054.

Soft Computing Techniques for Predicting Chemical Oxygen Demand in River Water

More Related Content

Similar to Soft Computing Techniques for Predicting Chemical Oxygen Demand in River Water (20)

More from Journal of Soft Computing in Civil Engineering (20)

Recently uploaded (20)

Soft Computing Techniques for Predicting Chemical Oxygen Demand in River Water