Modeling air quality index using optimized neuronal networks inspired by swarms

Dimple Pruthi; Rashmi Bhardwaj

doi:10.4491/eer.2020.469

Environ Eng Res > Volume 26(6); 2021 > Article

Pruthi and Bhardwaj: Modeling air quality index using optimized neuronal networks inspired by swarms

Research

Environmental Engineering Research 2021; 26(6): 200469.

Published online: November 27, 2020

DOI: https://doi.org/10.4491/eer.2020.469

Modeling air quality index using optimized neuronal networks inspired by swarms

Dimple Pruthi, Rashmi Bhardwaj^†

Non-Linear Dynamics Research Lab, University School of Basic and Applied Sciences, Guru Gobind Singh Indraprastha University, Dwarka, Delhi, India

^†Corresponding author: Email: rashmib22@gmail.com, Tel: +91-9868818880

Received August 14, 2020 Accepted November 25, 2020

(open-access):

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Air quality prediction is a significant field in environmental engineering, as air and water are essential for life on Earth. Nowadays, a common parameter used worldwide to measure air quality is termed as Air quality index. The parameter is measured based on the air pollutant concentration. The hybrid neuronal networks have been widely used for modeling air quality index. In the quest of optimizing the error in modeling air quality index, the existing adaptive neuro-fuzzy inference system is improved in this study using algorithms based on evolution and swarm movement. The model is based on the prominent air pollutants- nitrogen oxide, particulate matter of size equal to or less than 2.5microns (PM_2.5), and sulphur dioxide. The proposed hybrid model using wavelet transform, particle swarm optimization, and adaptive neuro-fuzzy inference system accurately predicts the Air Quality Index and can be used in the public interest to take necessary precautions beforehand.

Keywords: Air Quality Index, Ant Colony Optimization, Differential Evolution, Firefly Algorithm, Particle Swarm Optimization

1. Introduction

In a world of industrialization and modernization, air pollution appears to be on the rise. A recent report IQAir Visual 2018 concluded that Delhi is a highly polluted capital across the world and ranked 11^th. India rules the list with 22 of the worst 30 cities globally [1]. Air pollution is an insidious destroyer of the country’s health and wealth. With every inhale, microscopic particles get deep into the lungs. Air pollution is the major cause of cancers, strokes, and heart disease [2]. In recent studies, it is found that pollution is stunting children’s growth and development [3, 4]. The total number of cases is likely to rise in the coming years if pollution is not regulated. Moreover, additional deaths as well as disabilities will be introduced [5]. Air pollution has reduced the average life expectancy to 1.8 years. $225 billion is the estimated financial cost in lost labor and a thousand times more cost in healthcare. Rising pollution and its severity have forced researchers and scientists to carry out various studies involving its health effects and future trends. Air pollution dynamics is a complex process due to randomness involved in air pollutants behavior. Adaptive Neuro-Fuzzy Inference System (ANFIS) is effective in dealing with non-linear real-time problems. ANFIS has been widely used in disaster management, rock engineering [6, 7] healthcare sector, finance, and many other real-time fields [8–10]. ANFIS deals with both regression and classification problems [11].

The large error existing in the air quality modeling is discussed and optimized in this study. The major drawback observed in the past studies is the dependency of the model on various parameters. The complexity involved due to the various parameters leads to a large error. The present study aims to develop a less resource-intensive and more effective model in predicting air quality. ANFIS is used as a regression tool. The learning algorithm of classic ANFIS is based on the gradient descent method which is replaced with the evolutionary algorithms in the present study. The efficiency of the proposed model is validated by applying it to the data monitored at the Shadipur area of India. Shadipur is an industrial cum residential area covering all the precursors for polluting air. Hence, appropriate study area to validate the performance of model. The prominent pollutants - PM_2.5, oxides of nitrogen (NO, NO₂, NO_x) and sulphur dioxide are studied. The daily (24-h average) air pollutant concentrations are obtained from Central Pollution Control Board. PM_2.5 (μg/m³) concentrations are observed from March 2015 to June 2019 and NO (μg/m³), NO_x (ppb), SO₂ (μg/m³), NO₂ (μg/m³) are observed from January 2010 to June 2019. The model developed is only dependent on the past values of the respective air pollutant. The study is independent of other variables. The authors have not encountered such work to the best of their knowledge. The article is organized into four sections explaining the problem undertaken and the data collected, methodology, results, and the last section concludes the work carried out.

2. Methodology

2.1. Study Area and Dataset

Central Pollution Control Board (CPCB) is India’s apex body that monitors the air quality. The various stations of the organization serve almost all cities. It monitors contaminants and the parameters of the atmosphere. Delhi is one of the most polluted cities. Hence, a residential, industrial, and commercial region of Delhi covering all the human activities resulting in the formation of pollutants is considered for the study. Moreover, the subtropical climate of Delhi makes the behavior of air pollutants chaotic and contains extreme values covering the broader aspect of the applicability of the proposed models. The daily concentration (24-h) of fine particulate matter (PM_2.5), oxides of nitrogen, and sulphur dioxide from January 2010–June 2019 was collected from CPCB for the current analysis. Fig. 1 depicts the areas monitored by CPCB in Delhi, India, and the study area-Shadipur.

2.2. Wavelet Transform

In real-time data problems, pre-processing of data plays a significant role in any kind of analysis. The presence of extreme outliers, difficulty in feature extraction, and various fluctuations in data lead to errors in modeling real-time problems. The Pre-processing of data is to solve these issues. Pollutant sequence preliminary analysis indicates sudden shifts and spontaneous variations. Wavelet transform is employed to extract all the characteristics of the underline sequence. For further insights refer to Rashmi and Dimple [12]. In this study Daubechies (Db5) wavelet is considered.

2.3. Adaptive-Neuro Fuzzy Inference System

ANFIS is a fusion of neuronal networks and fuzzy systems. It is a widely used method for regression problems. It is based on on-premise and consequent parameters. In the classic ANFIS model, the parameters are tuned using a gradient descent algorithm as used by various researchers in the past [12–14]. ANFIS is based on targeted data and input. The learning algorithm builds the relationship between input and output in the Fuzzy if-then rules structure. The performance of the model is based on the learning algorithm. The parameters are tuned in the present work using various techniques of optimization-swarm intelligence, genetic algorithm, ant colony, and differential evolution, as discussed below.

2.3.1. Particle swarm optimization

In 1995, Eberhart and Kennedy developed the PSO technique [15]. Compared to other evolutionary algorithms the algorithm requires fewer parameters. The output vector y in R^D in PSO is based on position vector q and velocity vector w of the particle. For each iteration, velocities of all variables are modulated based on inertia weight (W), cognitive (d₁), and social (d₂) acceleration. For (n+1)^th epoch, velocity and position are updated as:

(1)

\begin{array}{l} w_{i D}^{n + 1} = W w_{i D}^{n} + d_{1} s_{1}^{n} (q_{i D}^{n} - y_{i D}^{n}) + d_{2} s_{2}^{n} (q_{i D}^{n} - y_{i D}^{n}) \\ y_{i D}^{n + 1} = y_{i D}^{n} + w_{i D}^{n + 1} \end{array}}

where, s₁,s₂ ∈ U(0,1) and d₁ + d₂ ≤ 4. Consider the particle’s best position as pbest. Compare the current position of the particle with that of pbest. If the current position is better than pbest, then pbest is the current position otherwise pbest is the best global position (gbest). The optimal values for tuning ANFIS parameters in this study are taken as W = 1, d₁ = 1, and d₂ = 2 using the trial and error process. The maximum iteration for all the algorithms is taken as 1000.

2.3.2. Genetic algorithm

John Holland developed GA in the 1970s [16], and applied henceforth to solve different types of problems. Using evolutionary biology the genetic algorithm is concerted. The algorithm is built based on descent, mutation, and crossover ideas. The important aspect of GA over the other evolutionary strategies is that it successfully works in the presence of more varied. This is a nature-influenced optimization algorithm. The algorithm operates on a chromosome population. The emphasis is not on one search space single point or one chromosome. Properties displayed by the population at all stages are dependent on the characteristics of the preceding stage. The cost function determines chromosomal output by the added fitness function. De et al. [17] will provide further insights into the genetic algorithm. Mutation percentage and mutation rate are taken as 0.4, 0.7, and 0.15, respectively, using the trial and error process crossover ratio.

2.3.3. Ant colony optimization

ACO is influenced by ants. ACO was developed by Dorigo and colleagues in the early 1990s [17]. From then on, the algorithm is incorporated for various optimization problems. The principle of ACO is based on the mechanism of how ant optimizes their path for food. Ants reduce their journey towards food by leaving a (chemical) pheromone trail as they walk. The chemical helps other ants for food search. The shorter path is indicated by a strong trail of pheromone. The stronger pheromone draws the attention of other ants. The ants mostly choose shorter path until all find the shortest path. The ACO used the mechanism of ants and develop artificial ants starting from the initial node. The artificial ants move to feasible neighbor nodes. Each ant builds a path using the state transition rule;

(2)

P (s, v) = {\begin{array}{l} \underset{v \in J (s)}{argmax} {{[η (s, v)]}^{α} {[η (s, v)]}^{β}}, & i f r \leq r_{0} \\ R & , o t h e r w i s e \end{array}

η(s,v) represents pheromone (desirability of (s,v)) on edge (s,v). r is the parameter that governs the relative value of desired, r₀ is initialized with 0 ≤ r₀ ≤ 1, r belongs to rand([0,1]). r₀ = 0.5 for carrying out the present study. J(r) is the set of edges available at point r of decision. S is a random variable chosen according to the probability distribution given below:

(3)

P (s, r) = {\begin{array}{l} \frac{{[η (s, v)]}^{α} {[η (s, v)]}^{β}}{\sum_{v \in J (s)} {[η (s, v)]}^{α} {[η (s, v)]}^{β}}, & i f r \in J (s) \\ 0 & , o t h e r w i s e \end{array}

Amount of pheromone modifies as η(s,v) ← (1−ρ)η(s,v)+ρη₀, 0<ρ<1

where ρ is the pheromone evaporation coefficient representing. The global modified amount when all ants arrived at the destination is given by

(4)

η (s, r) \leftarrow (1 - δ) η (s, r) + Δ η (s, r)

where,

Δ η (s, r) = {\begin{array}{l} \frac{1}{L}, & i f (s, r) \in g l o b a l b e s t t o u r \\ 0 & , o t h e r w i s e \end{array}

L denotes the global best tour length from the initial stage; the δ is the global evaporation coefficient parameter, and the last term is the increase in desirability [18].

2.3.4. Firefly algorithm

This algorithm is designed on fireflies flashing characteristics [19]. FFA structure is based on (i) Firefly (assumed to be unisex) ability to captivate other; (ii) the intensity of the luminous; and (iii) amount of light emitted by the firefly.

(5)

J = J_{0} e^{- {α d}^{2}}

(6)

v (d) = v_{0} e^{- {α d}^{2}}

where J is the light intensity and v(d) attractiveness at distance-d to the firefly. At d = 0, J = J₀ and v(0)=V₀, and α is the light absorption coefficient. d is defined as [20, 21]:

(7)

d_{j k} = ‖ y_{j} + y_{k} ‖ = \sqrt{\sum_{i = 1}^{r} (y_{j, i} - y_{k, i})}

where y_j and y_k are fireflies positions j and k. Firefly is captivated by yet brightness-based firefly. The movement of the firefly is given by

(8)

y_{j} = y_{j} + β_{0} e^{- {α d}^{2}} (y_{k} - y_{j}) + {η ε}_{j}

ηɛ_j is the random movement in case of absence of brightness, η varies between 0 and 1 and the second term in the equation is the captivation factor with coefficient as β₀. FFA is used to tune the premise parameter of ANFIS with optimal values of coefficients as α=1 and β₀=2, using trial and error process.

2.3.5. Differential evolution

The well-known evolutionary algorithm is based on mutation, crossover, and selection. DE is widely used for optimization problems in various fields [22]. It has been widely applied as opposed to other algorithms due to its simple structure and faster convergence rate. The algorithm is focused on biological processes that involve survival if environmental and genetic features are to be complied with. DE begins its operation by creating a random population, where each person in the population represents a problem-solving solution. The search parameter is initialized, as are the total number of generations. For the description of the algorithm refer to Wei et al. [23]. The optimal crossover probability is taken as 0.2 and scaling factor lower and upper bounds as 0.2 and 0.8.

2.3.6. Proposed algorithm

x(t) at time t is considered where x(t) represents daily (24 h) concentration of air pollutants on day t.
x(t) is decomposed using the wavelet transform as mentioned in section 2.3.1 using high and low filters. x(t) is the sum of a_n (approximation at level n) and d₁,d₂,. . . .,d_n (details at level n). Daubechies wavelet(db5) is considered in the present study due to its property to extract fluctuations nicely.
Instead of x(t), the smoothed decomposed series are used for further analysis. Let y(t) denote the approximation at level 5 i.e. y(t) = a₅(t).
The autocorrelation function (ACF) for y(t) is computed as described by Mohammad et al. [24] to find out the dependence of y(t) on lag values. Let the optimized lag is τ. The input set is past τ values of the series and the output value is y(τ+1).
The series is divided into training (70%) and testing (30%) datasets.
The parameters as described in section 2.3 are obtained. Using the algorithm described in sections 2.3.1–5, the premise and consequent parameters are trained.
The parameters are trained to obtain the optimized error. The simulations are carried using Matlab R2019a software.
Steps (4)–(7) are carried out for d₁,d₂,. . . .,d_n. The final output is obtained adding the trained values of approximation and details. The test dataset is simulated using the model. Further effectiveness of the method is verified using trained and tested datasets.

The parameters are trained using different algorithms and the same procedure is followed. The models based on different algorithms are denoted as are compared to obtain the best model.

2.4. Evaluation Criterion

The efficacy of the model is measured against the existing models. The observed-predicted pairs (y_O(t) and y_p(t) w.r.t time t) for training and testing datasets are considered. The determinism coefficient (R²) defines the relation between the aforementioned pairs. The parameters for determining errors are based on various values, and they primarily combine absolute and relative errors. Here are some of the error measurements:

(9)

\begin{array}{l} R^{2} = 1 - \frac{\sum_{t = 1}^{m} {(y_{o} (t) - y_{P} (t))}^{2}}{\sum_{t = 1}^{m} {(y_{o} (t) - \frac{1}{m} \sum_{t = 1}^{m} y_{o} (t))}^{2}} \\ M e a n A b s o l u t e E r r o r, M A E = \frac{\sum_{t = 1}^{m} | y_{o} (t) - y_{P} (t) |}{m} \\ M e a n A b s o l u t e P e r c e n t a g e E r r o r, M A P E = \frac{\sum_{t = 1}^{m} | \frac{y_{o} (t) - y_{P} (t)}{y_{o} (t)} |}{m} \times 100 \\ R o o t M e a n S q u a r e E r r o r, R M S E = \sqrt{\frac{1}{m} \sum_{t = 1}^{m} {(y_{o} (t) - y_{P} (t))}^{2}} \end{array}}

To forecast the air quality, the analysis is carried out on the air pollutants. The Air Quality Index (AQI) is the metric for transmitting air quality to the public whether it is dangerous or safe to go out or take precautions. AQI standard for India was introduced in the year 2014. The best model is evaluated based on equation 9. As per CPCB for India, AQI is evaluated using formulas mentioned in equations 10–12. Using the best model the pollutant is predicted and correspondingly air quality sub-index is evaluated. The prominent sub-index gives the AQI. AQI in the present study is used to validate the proposed model. AQI value is calculated and divided into categories-Good (0–50), Satisfactory (51–100), Moderately polluted (101–200), Poor (201–300), Very poor (301–400), and Severe (401–500) as per CPCB norms.

(10)

{P M}_{2.5} A i r Q u a l i t y S u b i n d e x = {\begin{array}{l} \frac{X * 50}{30}, 0 < X \leq 30 \\ 50 + \frac{(X - 30) * 50}{30}, 30 < X \leq 60 \\ 100 + \frac{(X - 60) * 100}{30}, 60 < X \leq 90 \\ 200 + \frac{(X - 90) * 100}{30}, 90 < X \leq 120 \\ 300 + \frac{(X - 120) * 100}{130}, 120 < X \leq 250 \\ 400 + \frac{(X - 250) * 50}{130}, X > 250 \end{array}

where, X represents PM_2.5 concentration in μg/m³.

(11)

{S O}_{2} A i r Q u a l i t y S u b i n d e x = {\begin{array}{l} \frac{X * 50}{40}, 0 < X \leq 40 \\ 50 + \frac{(X - 40) * 50}{40}, 40 < X \leq 80 \\ 100 + \frac{(X - 80) * 100}{300}, 80 < X \leq 380 \\ 200 + \frac{(X - 380) * 100}{420}, 380 < X \leq 800 \\ 300 + \frac{(X - 800) * 100}{800}, 800 < X \leq 1600 \\ 400 + \frac{(X - 1600) * 50}{800}, X > 1600 \end{array}

where, X represents SO₂ concentration in μg/m³.

(12)

{N O}_{x} A i r Q u a l i t y S u b i n d e x = {\begin{array}{l} \frac{X * 50}{40}, 0 < X \leq 40 \\ 50 + \frac{(X - 40) * 50}{40}, 40 < X \leq 80 \\ 100 + \frac{(X - 80) * 100}{100}, 80 < X \leq 180 \\ 200 + \frac{(X - 180) * 100}{100}, 180 < X \leq 280 \\ 300 + \frac{(X - 280) * 100}{120}, 280 < X \leq 400 \\ 400 + \frac{(X - 400) * 100}{120}, X > 400 \end{array}

where, X represents NO_x concentration in ppb(parts per billion).

3. Results and Discussions

The random behavior of air pollutants makes the task of predicting air quality complex. The study aims to build a model from which high prediction accuracy with minimal input parameters can be obtained. The complexity comes into the frame when the series is non-stationary. The extreme outliers make the problem more challenging. The studies related to model air quality has been carried out for various environmental pollutants like NO₂, PM_2.5, and SO₂ which play a significant role in determining the air quality. Some of the important work is shown in table 1. The problem was previously dealt with using ANFIS modeling but the large error between the predicted and observed value was observed (refer to Table 1). The drawback found in classic ANFIS modeling was due to the gradient descent (GD) method. It is observed that in classic ANFIS modeling, the GD algorithm gets trapped in local minima. The tuning of premise parameters was inadequate leading to inadequate predictions. Moreover, too many parameters are also one of the reasons for erroneous prediction. The present work is an attempt to minimize the parameters at the same time improving the air quality prediction. In the current scenario, oxides of nitrogen, particulate matter, and sulphur dioxide are prominent pollutants. Though the air quality index is dependent on NO_X, PM_2.5, and SO₂, NO and NO₂ are also predicted in the present study as both have a significant correlation with NO_X. The air quality index is dependent on the prominent air quality sub-index corresponding to the pollutant. The proposed hybrid models were validated for the Shadipur area of India. The hybrid model combines the decomposition filter, fuzzy inference system, algorithm for optimizing parameter, and neuronal networks.

The correlation analysis of the pollutants and meteorological parameters was studied to optimize the choice of parameters. It was found that no significant correlation existed between the weather series available (relative humidity, wind speed, and temperature) and the pollutant considered for the available data. The other attempt to find the dependent parameters of the series was to carry out a partial autocorrelation function (PACF). It is observed that in comparison to the past concentration of the pollutant no significant correlation was observed with meteorological and other pollutants. Based on the partial correlation function of pollutant, the optimized lag considered is six. Hence, the concentration of past six days is considered for predicting concentration of the seventh day. For decomposition analysis Daubechies wavelet (Db5) is used. The membership function used in the fuzzy inference system is a Gaussian function. The premise parameters are tuned using the gradient descent method and evolutionary algorithms and consequent parameters using an algorithm mentioned described in classic learning. The model performance is summarized using various statistical tools as described in section 2.4.

The performance analysis of various models in Tables 2 –6 depicts the role of wavelet decomposition in analyzing the non-stationary series. Without extracting the features i.e. without using low and high filters, a large difference is observed between predicted and observed data. ANFIS and WANFIS models are also compared for each air pollutant to signify the importance of wavelet transform. Further, the evolutionary algorithm implemented for tuning premise parameters has reduced the error between predicted and observed air pollutant concentrations to a larger extent. The bold glyphs in the tables signify the errors corresponding to the best model. It is observed that for NO, NO₂, NO_x, and PM_2.5 proposed Wavelet Transform-Adaptive Neuro-Fuzzy Inference System-Particle Swarm Optimization (WANFIS-PSO) gives better results compared to other models. The genetic algorithm is very much close to PSO. The next step for the best model was to check the computation time.

While tuning the parameters it is observed that differential evolution converges at a very fast rate. Particle Swarm Optimization and Genetic Algorithm take approximately 80 seconds to converge while FFA has a slow convergence rate compared to others. FFA gives better results than ACOR and DE. ACOR is most often used for continuous problems. It is observed that ACOR possesses random behavior in case of outliers hence very large relative error. The comparison of algorithms in the study covers almost all aspects in the present scenario to develop a good model for AQI prediction. The daily (24-h) AQI value is evaluated based on the prominent pollutant. The observed and predicted concentrations of pollutants and AQI values corresponding to the best model are very close for the tested data as depicted in Fig. 2(a)–(e) and Fig. 2(f), respectively. AQI prediction met 88.63% accuracy and remaining AQI values lie very close to the breakpoints of the categories as defined in section 2.4. The accuracy level in predicting the air quality index validates the study to be used for making policies and beforehand precautions to be marked safe from air pollution.

4. Conclusion

The atmospheric dynamics is complicated due to the extreme outliers and data unavailability making the study highly expensive. The air quality index model developed in the present study is effective and less complex in forecasting the one-step-ahead quality of the air we breathe in. The proposed model is only dependent on the lagged values of the prominent air pollutant reducing the complexity of the model as compared to the existing ones. Though the pollutant series have extreme outliers the proposed hybrid model using wavelet transform, adaptive neuro-fuzzy inference system, and particle swarm optimization has significantly optimized the error. Thus, an effective model for predicting air quality index is obtained which can be adopted by air pollution agencies and the Government for policymaking. The model developed in the present work can be efficiently used and applied to any real-time series to observe future behavior.

Acknowledgment

The authors are thankful to Guru Gobind Singh Indraprastha University, Delhi, India for providing research facilities and financial support.

Notes

Author Contributions

D.P. (Ph.D. student) conducted all the experiments and wrote the manuscript. R.B. (Professor) revised the manuscript.

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Fig. 1

Study Area- Industrial cum residential area, Shadipur, Delhi, INDIA (permission from the International Journal of Environmental Science and Technology Springer Nature).

Fig. 2

Predicted and Observed concentration for the tested dataset of pollutant (a) NO, (b) NO₂, (c) NO_X, (d) PM_2.5, (e) SO₂ and (f) AQI values.

Table 1

Comparison of the Past Relevant Studies and the Present Work

Author	Input	Study Region	Methods	Output : Correlation Coefficient
Rashmi and Dimple [12]	Embedded NO₂ Time series	Shadipur, Delhi (Commercial, Industrial and residential area	Wavelet- Artifical Neural Network Fuzzy Inference System	NO₂: R = 0.9762
Fabio et al. [25]	Meteorological data, PM₁₀, CO	Pescara valley (industrial area and Busiest Port)	MLR (multiple linear regression model), neural network model	PM_2.5 :0.86 ≤ R ≤ 0.91
Ulderico et al. [26]	SO₂, NO₂ past values	Palermo (Italy)	Recurrent Neural Network (Elman model)	SO₂: 0.89 ≤ R ≤ 0.0.96 NO₂: 0.90 ≤ R ≤ 0.98
Ian [27]	NO, NO₂, CO, O₃, temperature, wind speed and direction	Chilliwack (eastern LFV)	Artificial neural network (ANN) multiple regression models (MLR)	PM_2.5 :0.69 ≤ R ≤ 0.75
Polaiah et al. [28]	Wind Speed and direction, Temperature, Humidity, Atmospheric Pressure etc.	Visakhapatnam, Andhra Pradesh	ANN	NO₂: R = 0.955
Joaquin et al. [29]	Daily average PM_2.5, wind parameters, PM_2.5 (maximum), humidity and temperature	Ciudad Juárez and El Paso	Multilayer Perceptron Neural Network (MLP), a Square Multilayer Perceptron (SMLP) and Radial Basis Function network (RBF)	PM_2.5: 0.284 ≤ R ≤ 0.679
Marijia et al. [13]	Wind speed, wind direction, temperature, humidity, pyrometallurgical process sulfur emission	Bor, Serbia(urban area in vicinity of copper smelter)	ANFIS	SO₂: R = 0.725
Sait et al. [30]	Wind Speed, Temperature, particulate matter concentrations	Izmir, Turkey	ANN	SO₂: R = 0.97
Wei and Jingyi [31]	Average PM_2.5, PM₁₀, SO₂, CO, NO₂, O₃, minimum and maximum day temperature.	Baoding City, China (industrial and energy structure)	Principal Component Analysis and improved Least Square Support Vector Machine with Cuckoo Search Algorithm	PM_2.5 14.47 ≤ RMSE ≤ 22.89
Ignacio et al. [32]	Wind Speed, Wind Direction, Temperature, Pollutant Past Values	Campo de Gibraltar (industrial area), Spain	Multiplayer perceptron model (MLR), Autoregressive Integrated Moving Average, Persistence (PER)	SO₂ :0.26 ≤ R ≤ 0.73
Present Work	Embedded Pollutant Series	Shadipur, Delhi	Wavelet- Artifical Neural Network Fuzzy Inference System- Particle Swarm Optimization/Genetic Algorithm	PM_2.5: R = 0.994 SO₂: R = 0.991 NO₂: R = 0.994 NO: R = 0.991 NO_x: R = 0.993

Table 2

Performance Analysis of the Models for Predicting Nitrogen Oxide (NO)

MODEL		WANFIS-PSO	ANFIS-PSO	WANFIS-GA	ANFIS-GA	WANFIS-FFA	ANFIS-FFA	WANFIS-DE	ANFIS-DE	WANFIS-ACO	ANFIS-ACO
R²	Training	0.9827	0.6161	0.9802	0.5899	0.9491	0.5775	0.6631	0.5484	0.0101	0.5447
R²	Testing	0.9713	0.5075	0.978	0.5233	0.9508	0.5775	0.6316	0.5362	0.0096	0.541

RMSE	Training	5.2941	24.9204	5.6537	25.8316	9.1226	28.4114	24.9419	27.0214	191.1694	27.1222
RMSE	Testing	7.9566	33.1202	6.9655	32.4669	10.4381	28.4114	30.487	32.0249	215.7535	31.9014

MAE	Training	3.1405	14.7228	3.4886	15.2701	6.5133	20.8815	16.2929	15.7738	118.0759	15.6743
MAE	Testing	4.4595	19.754	4.506	19.8969	7.1135	20.8815	20.8836	19.5628	138.4949	19.3828

MARE	Training	22.745	112.2138	24.9821	124.5905	61.6391	230.6897	138.5161	127.6262	1.01E+03	123.3864
MARE	Testing	16.6506	70.8322	17.3578	74.5642	33.6449	230.6897	95.4234	73.9303	560.8402	72.0253

Table 3

Performance Analysis of the Models for Predicting Nitrogen Dioxide (NO₂)

MODEL		WANFIS-PSO	ANFIS-PSO	WANFIS-GA	ANFIS-GA	WANFIS-FFA	ANFIS-FFA	WANFIS-DE	ANFI-SDE	WANFIS-ACO	ANFIS-ACO
R²	Training	0.9877	0.7149	0.9872	0.7042	0.9183	0.7165	0.7812	0.6893	0.0955	0.6935
R²	Testing	0.9906	0.757	0.9902	0.7533	0.928	0.6905	0.8094	0.7592	0.0984	0.6935

RMSE	Training	4.153	20.0262	4.2437	20.4163	10.942	21.7503	20.2163	21.0403	154.115	20.7621
RMSE	Testing	2.8926	14.7287	2.9582	14.8509	8.2206	18.563	14.2791	14.692	111.8062	20.7621

MAE	Training	2.6672	12.9012	2.8512	13.2187	7.7783	16.9302	13.8941	13.4367	89.3785	13.3784
MAE	Testing	1.9186	9.7879	1.9513	9.9526	5.6154	14.5151	10.034	9.5305	61.8355	13.3784

MARE	Training	19.7852	113.1708	22.175	111.4631	62.9307	191.4781	117.6548	109.4562	579.1208	118.1893
MARE	Testing	4.5629	26.7375	4.6033	27.749	13.9168	57.1857	27.6157	25.043	158.6515	118.1893

Table 4

Performance Analysis of the Models for Predicting Nitrogen Oxides (NO_x)

MODEL		WANFIS-PSO	ANFIS-PSO	WANFIS-GA	ANFIS-GA	WANFIS-FFA	ANFIS-FFA	WANFIS-DE	ANFI-SDE	WANFIS-ACO	ANFIS-ACO
R²	Training	0.9867	0.661	0.985	0.6393	0.9291	0.6282	0.6648	0.6308	0.054	0.6276
R²	Testing	0.9595	0.5444	0.9688	0.5572	0.856	0.5311	0.632	0.5558	0.0187	0.557

RMSE	Training	5.7852	29.1889	6.1414	30.1249	13.458	34.4203	31.6257	30.481	184.2189	30.5948
RMSE	Testing	10.2779	34.5928	9.0363	34.0663	20.3379	37.5671	35.2764	34.1605	238.4032	34.0544

MAE	Training	3.762	18.0986	3.9744	18.8794	10.3557	27.5149	21.3677	18.8559	129.6917	18.8685
MAE	Testing	5.2406	21.019	5.1722	21.0462	10.5809	28.4276	23.995	20.8502	145.7377	20.8199

MARE	Training	18.2935	101.813	18.5618	108.919	63.9923	303.8288	80.1258	106.3378	1.20E+03	106.3477
MARE	Testing	11.2621	39.4309	11.5561	41.5537	25.7137	73.0377	54.9069	39.3024	367.5926	40.0871

Table 5

Performance Analysis of the Models for Predicting Particulate Matter PM_2.5

MODEL		WANFIS-PSO	ANFIS-PSO	WANFIS-GA	ANFIS-GA	WANFIS-FFA	ANFIS-FFA	WANFIS-DE	ANFI-SDE	WANFIS-ACO	ANFIS-ACO
R²	Training	0.9877	0.6912	0.9853	0.6759	0.9514	0.6914	0.7355	0.6543	0.0132	0.6535
R²	Testing	0.9853	0.6218	0.9854	0.6416	0.9043	0.6091	0.7889	0.6402	9.76E-05	0.6396

RMSE	Training	10.1658	42.5825	21.4381	52.1522	20.6936	55.1953	50.3766	45.2659	345.9455	45.107
RMSE	Testing	9.0462	46.809	17.2591	44.6346	23.9442	63.7773	36.3864	44.9461	360.0478	44.6953

MAE	Training	6.4536	19.5684	12.914	28.8247	15.0175	38.0123	34.959	21.8757	242.7545	20.7134
MAE	Testing	6.1653	30.7722	11.958	30.4063	16.6529	54.6023	27.1786	31.6624	275.1605	30.5494

MARE	Training	6.5777	19.8071	5.6767	30.8871	17.0188	56.04	41.724	25.1531	284.729	21.9384
MARE	Testing	5.9886	30.5088	5.4756	31.1767	16.1643	74.5	27.9868	34.742	293.0874	31.1902

Table 6

Performance Analysis of the Models for Predicting Sulphur Dioxide SO₂

MODEL		WANFIS-PSO	ANFIS-PSO	WANFIS-GA	ANFIS-GA	WANFIS-FFA	ANFIS-FFA	WANFIS-DE	ANFI-SDE	WANFIS-ACO	ANFIS-ACO
R²	Training	0.9815	0.6169	0.9823	0.6031	0.9534	0.4992	0.5435	0.5688	4.51E-04	0.5531
R²	Testing	0.9792	0.5076	0.9817	0.5074	0.7894	0.4383	0.4606	0.4999	9.41E-05	0.4967

RMSE	Training	1.3731	6.2461	1.3419	6.3677	2.2258	8.8814	8.601	6.6253	65.4892	6.7444
RMSE	Testing	0.9536	4.6432	0.8909	4.6311	3.2581	7.3658	6.1823	4.6738	56.0997	4.6791

MAE	Training	0.7505	3.4773	0.7672	3.516	1.4525	7.2184	5.5394	3.6001	45.1907	3.6418
MAE	Testing	0.6069	2.9789	0.583	2.9574	1.3042	6.2474	4.5481	2.9866	38.7643	2.98

MARE	Training	11.4649	51.7641	11.6339	51.372	22.2842	140.8222	82.0887	53.3888	666.2866	55.1915
MARE	Testing	6.3206	33.1995	6.0066	32.8453	14.7799	89.5434	49.3311	33.1355	411.338	34.0932