Data Mining and ANFIS Application to Particulate Matter Air

Pollutant Prediction. A Comparative Study

Mihaela Oprea

, Marian Popescu

, Sanda Florentina Mihalache

and Elia Georgiana Dragomir

Automatic Control, Computers and Electronics Department, Petroleum-Gas University of Ploiesti, Ploiesti, Romania

Informatics, Information Technology, Mathematics and Physics Department, Petroleum-Gas University of Ploiesti,

Bd. Bucuresti, 39, Ploiesti, Romania

Keywords: Prediction Model, Data Mining, Adaptive Neuro-Fuzzy Inference System, Particulate Matter Air Pollution.

Abstract: The paper analyzes two artificial intelligence methods for particulate matter air pollutant prediction, namely

data mining and adaptive neuro-fuzzy inference system (ANFIS). Both methods provide predictive

knowledge under the form of rule base, the first method, data mining, as an explicit rule base, and ANFIS as

an internal fuzzy rule base used to perform predictions. In order to determine the optimal number of prediction

model inputs, we have perform a correlation analysis between particulate matter and other air pollutants. This

operation imposed NO

and CO concentrations as inputs of the prediction model, together with four values

of PM

concentration (from current hour to three hours ago), the output of the model being the prediction of

the next hour PM

concentration. The two prediction models are investigated through simulation in different

structures and configurations using SAS

and MATLAB

respectively. The results are compared in terms of

statistical parameters (RMSE, MAPE) and simulation time.

1 INTRODUCTION

Artificial intelligence (AI) provides very good

prediction models for a variety of applications in

domains such as engineering, environmental science,

meteorology, economy, medicine, finance, banking,

education etc. They find in shorter time, sub-optimal

solutions, being proper for use in real time systems.

Developing more accurate real time air pollution

forecasting systems is nowadays an important

interdisciplinary research topic for several academic

communities jointly working in environmental

science (air pollution), meteorology, computer

science, artificial intelligence, statistics, physics etc.

One of the harmful air pollutant in urban areas which

can cause significant health problems especially to

sensitive people (such as children, elderly) is

particulate matter (PM). As smaller is the PM

diameter size as much significant is the potential

negative effect on human health. PM

and PM

2.5

are

two types of PM that need careful analysis of their

concentration levels and accurate prediction for short-

terms (as e.g. next hours, next day) in order to reduce

the impact on human health when higher levels are

recorded. Early warning systems based on accurate

prediction of air pollution can help to improve life

quality in urban areas.

The research topic tackled in this paper focus on

the comparison of two artificial intelligence

prediction models, data mining (DM) and adaptive-

network fuzzy inference system (ANFIS) that have

the potential to increase the prediction accuracy for

PM air pollutant. The selection of the two methods

was done taking into account the recent research

results reported in the literature (primarily the

atmospheric environment science and artificial

intelligence literature) and previous research work

results reported in (Oprea et. al., 2016b). We made a

comparison between them in order to identify the best

one which can be used in the Ploiesti city from

Romania, a city that has a higher air pollution.

The purpose of our research work is to develop a

real-time PM air pollutant forecasting system which

provides next hours PM concentration level with a

higher accuracy.

The paper is organized as follows. Section 2

presents an overview on prediction models. The two

artificial intelligence prediction models, data mining

and ANFIS, are described in section 3. The

experimental results are detailed and discussed in

section 4. The final section concludes the paper and

identifies some future work.

Oprea M., Popescu M., Mihalache S. and Dragomir E.

Data Mining and ANFIS Application to Particulate Matter Air Pollutant Prediction. A Comparative Study.

DOI: 10.5220/0006196405510558

In Proceedings of the 9th International Conference on Agents and Artiﬁcial Intelligence (ICAART 2017), pages 551-558

ISBN: 978-989-758-220-2

551

2 AN OVERVIEW ON

PREDICTION MODELS

The main types of prediction models that are

currently used in forecasting environmental (air,

water, soil) pollution are: climatology models,

deterministic models, statistical models, artificial

intelligence models, hybrid models.

Computational intelligence (CI) models are the

most used artificial intelligence prediction models.

They include fuzzy inference systems (FIS), artificial

neural networks (ANNs), genetic algorithms (GAs),

swarm intelligence models (such as ant colony

optimization - ACO, particle swarm optimization -

PSO, bees colony - ABC etc) as well as combinations

of them (e.g. ANFIS which combines FIS and ANN).

Several applications were reported for time series

forecasting using CI models (see e.g. (Palit and

Popovic, 2005)).

The prediction models used for real time air

quality forecasting are (Zhang et al., 2012): simple

empirical approaches (e.g. climatology), parametric

(including statistical) models (e.g. ANNs, regression

trees, fuzzy models), advanced physically-based

approach (e.g. deterministic – CTM models).

Three prediction models were used for PM

2.5

forecasting in (Perez and Salini, 2008): a linear

model, a multilayer neural network and a hybrid

clustering algorithm. All methods proved to be good,

but the last one gave more accurate results in

detecting PM

2.5

high level concentrations. Another

research work (Elangasinghe et al., 2014) combines

ANN with k-means clustering for a better

understanding of PM

and PM

2.5

complex time series

measured in a coastal site of New Zealand. In this

case the inclusion of clustering rankings in the ANN

model improved the prediction accuracy when PM

higher concentrations are registered.

Air quality prediction using fuzzy logic and

statistical models (autoregressive) is discussed in

(Carbajal-Hernández et al., 2012). Another research

which apply fuzzy logic to time series forecasting is

presented in (Domańska and Wojtylak, 2012).

A case study of ANFIS modelling for air pollution

in Serbia is described in (Savić et al., 2014). Another

research work using ANFIS combined with a data

preprocessing technique (output-dependent data

scaling) to predict daily levels of PM

in Konya city,

Turkey, is detailed in (Polat et al., 2012). Some recent

research work that report more accurately prediction

results by using ANFIS prediction models in

environmental systems are introduced in (Mekanik et

al., 2015) - for seasonal rainfall forecasting in

southeast Australia, (Hajek and Olej, 2015) - for

common air quality index prediction in some regions

from Czech Republic, (Mishra et al., 2015) – for

2.5

forecast during haze episodes in Delhi, India.

Shahraiyni et al. (2015) proposes an identifica-

tion scheme for selecting meaningful inputs to ANFIS

model used to simulate the virtual air pollution

monitoring stations from Berlin. The hourly data sets

for particulate matter PM

are converted into daily

mean for the available data. The results show a

reduced computing time for inferring a smaller

number of ANFIS rules. The proposed ANFIS

models have good results in terms of statistical

indices. Ausati and Amanollahi (2016) studies

ANFIS method and statistical methods for daily

average PM

2.5

prediction in a polluted urban area of

Iran. The hybrid models offer the best solutions

according to statistical indices.

Prasad and al. (2016) want to predict five air

pollutants daily concentration (PM

among them)

based on ANFIS using as inputs several

meteorological parameters and previous’ day air

pollutant concentration. The datasets is for an urban

region from India. Forward selection method is used

to reduce the number of ANFIS inputs, reducing the

computational time and effort.

A hybrid method, statistical and ANFIS combined

is proposed to predict the daily PM

concentration

from an urban area from Turkey in Polat (2012). The

proposed ANFIS use four meteorological parameters

as additional inputs to the previous PM

daily

concentration.

Oprea et al. (2016a) presents a comparative study

for PM

2.5

prediction between ANN and ANFIS. The

used dataset contains data from an urban region from

Germany with hourly concentrations. The inputs of

ANFIS are based only on previous hourly PM

concentration. Mihalache et al. (2015) test the ANFIS

prediction method on three data sets from different

urban region from Romania. The next hour PM

concentration is the ANFIS output and the inputs are

based only on previous hourly concentrations.

A combination of FIS and GA for air pollution

prediction based on GIS data is presented in (Shad et

al., 2009).

Data mining models used for prediction are

reported in (Osrodka et al., 2005) – for high level air

pollution forecasting in urban industrial area from

southern Poland, (Siwek and Ossowski, 2016) –

which use GA and a linear method for feature

selection and random forest (forming an ensemble of

decision trees) and ANNs as prediction models.

Another research work on data mining models for air

quality prediction in Athens, Greece, is described in

(Riga et al., 2009).

ICAART 2017 - 9th International Conference on Agents and Artiﬁcial Intelligence

552

The research results reported so far highlighted

the potential benefits of using data mining techniques

as predictive models for air pollutants concentrations.

A combination between ANN and inductive

learning algorithms applied to prediction is discussed

in (Bae and Kim, 2011). Deep recurrent ANN is

another promising method for PM

2.5

prediction (see

e.g. a recent research work reported in (Ong et al.,

2016). The application of a predictor’s ensemble for

daily average PM

forecasting is tackled in (Siwek et

al., 2011). The use of decision trees and neural

networks proved to give also, good prediction

accuracy of air quality forecast (see e.g. (Loya et al.,

2013)).

The main conclusion of our overview is that most

of the prediction models based on artificial

intelligence techniques are chosen as a function of the

air quality monitoring area specific data, and the

hybrid models built as a combination of several

techniques, gave the best results. However, some of

the AI techniques, such as data mining, ANFIS and

ANNs need deeper investigation of their potential in

providing more accurate forecasts. Moreover, few

research papers presents comparative studies between

data mining techniques (such as decision tree) and

ANFIS. Our research work focus on the analysis of a

data mining technique - decision tree (CHAID

algorithm) and ANFIS (Takagi-Sugeno) in solving

short-term prediction of PM

air pollutant in the city

of Ploiesti, Romania. Several experiments were

performed and the main lessons that were learnt are

presented in this paper.

3 METHODS DESCRIPTION

A brief description of the two methods that were

selected in our research study, data mining – decision

trees and ANFIS is given as follows.

3.1 Data Mining – Decision Tree

Data mining can be defined as the process of

knowledge (pattern) extraction from large databases.

It comprises a set of techniques from statistics (e.g.

statistical classifiers) and artificial intelligence (e.g.

computational intelligence techniques and rule-based

systems).

Some examples of data mining techniques are:

decision tree classifiers (e.g. C4.5, CHAID, REP

Tree, Random Forest), support vector machine

(SVM), instance-based classifiers, artificial neural

networks (such as multi-layer perceptron - MLP and

radial basis function neural network – RBF-NN),

rule-based classifiers.

We have chosen to analyze decision trees as they

have important characteristics: offer fast and

computationally inexpensive prediction; they are

nonparametric; provide an intuitive representation of

extracted knowledge, being very useful for

environmental data; they generate a rule base.

The decision tree method is often used for gaining

information in a decision-making process.

A decision tree is a tree-like structure where each

branch is a possible choice and each leaf node is a

decision. This technique classifies instances by

crossing the tree from the root node to leaf nodes. It

starts by testing the root attribute, then by moving the

tree branches according to data attribute values in the

given data set. Attributes in a classification problem

are usually of two types: nominal or numerical (their

values are real numbers).

At each step there is selected a variable which is

considered “best”. Different type of decision trees use

different formula for this. One of the possibility is the

information gain (given by (1)).

)()(),(

)(

AValuesv

MEn

MEnAMIG







(1)

Parameter M is a training data set, A - an attribute,

En() - the function which calculates the entropy,

Values(v) - a data set with all possible values of A and

represents a subset of M in which A has value v.

Decision trees generate a set of rules that may be

useful in predicting a new set of data. This knowledge

can be used in anticipation of a possible air pollution

episode.

3.2 ANFIS Prediction Method

ANFIS technique can be used as a prediction method.

The available dataset impose the inputs used to

predict a specific variable that becomes the ANFIS

output. The prediction is usually on short term (next

hour, next two hours, next 6 hours). The inputs must

be relevant to the output variable due to the increase

of computational effort with additional inputs. For

example an ANFIS with 4 inputs each described by 3

membership functions generates 81 rules. Adding one

input increases the rules number to 243 rules. A

statistical method can be used to select between

relevant inputs to the selected output prediction. Then

the input-output dataset is randomly divided into

training, validating and testing datasets. The starting

FIS can have an empty rule base or an existing

number of fuzzy rules. In the first case ANFIS must

Data Mining and ANFIS Application to Particulate Matter Air Pollutant Prediction. A Comparative Study

553

generate the rules with respect to continuity,

consistency and completeness required for fuzzy rule

base. This is the case where there are no obvious if-

then rules between the inputs and outputs. In the

second case, from experience one can generate a fixed

number of fuzzy rules and then the ANFIS has to

adjust the premise parameters and consequent

parameters to match the input output datasets. For

example, if there are two rules:

If U

is μ

and U

is μ

then f

= c

+ d

(2)

If U

is μ

and U

is μ

then f

= c

+ d

(3)

where μ

and μ

are input membership functions and

, d

, and r

are the constant values for Takagi Sugeno

part, the ANN part adjust the values of the consequent

parameters (c

, d

, and r

)

and the parameters

associated with premise parameters (membership

functions μ

and μ

). ANFIS technique used as

prediction for PM usually has to model complicated

nonlinear dependencies between inputs and output,

therefore the rule base is empty and ANFIS has to

generate the rules, and adjust the FIS parameters to

match the input output datasets. The resulted FIS

architecture is evaluated with the testing data and the

prediction is compared to the testing data via

statistical indices.

The ANFIS performance can be adjust modifying

the methods and parameters associated: the method of

generating the FIS structure, the number of inputs, the

shapes of membership functions defined for the input

variables, the granularity of each input, the type of

output function, the optimization method to train FIS

and the number of training epochs.

4 EXPERIMENTAL RESULTS

4.1 Data Set

The data used in this study are from an air quality

monitoring station from Ploiești, Romania, and

contains hourly concentrations of the air pollutants

, SO

, CO, NO

, and NO

, each pollutant having

around 6000 samples.

In order to determine which pollutants have

greater influence on PM

concentration the

correlation coefficient is calculated its values being

presented in Table 1.

From Table 1 it can be observed that CO and NO

provide the highest values of the correlation

coefficient. Thus, these pollutants will be used

together with PM

as inputs in the prediction models

from this study.

Table 1: Correlation Coefficient.

Pollutant Cor. Coef.

0.048

CO 0.603

0.556

0.480

The data set consisting of PM

, CO and NO

concentrations has the following characteristics:

 PM

data have a maximum of 261.33 µg/m

, and

an average of 45.05 µg/m

;

 The maximum CO concentration is 1.8 mg/m

with an average of 0.33 mg/m

;

 NO

concentration has a maximum of 79.91

µg/m

, and an average of 27.3 µg/m

All experiments presented in the following are based

on a data set divided into 70% for training, 15% for

validation and 15% for testing.

4.2 Decision Tree Method

The software package used in the decision tree

experiments includes SAS

Enterprise Guide

and

SAS

Enterprise Miner

. SAS implementation

involves the use of a multi-way decision trees

(Schubert and Lee, 2011). It can be chosen the

splitting criteria and other options that determine the

method of tree construction. The options include the

popular features of CHAID (Chi-square automatic

interaction detection) and those described in

(Breiman et al., 1984). The evaluation criteria of

selection rules can be based either on the results of

tests to determine certain statistical parameters (such

as the F-test and Chi-Square, methods that accept an

input value p as stop criterion) or on reducing

variance, entropy or Gini parameter. The F test and

variance can be used for interval values.

The decision tree experiments test different values

for the target variable criteria analysis (TVCA),

missing values (MV), branching factor (BF) and tree

depth (TD).

The two methods that have been tested to assess

possible variables used in splitting rules are ProbF

(the p-value of the F test that is associated with the

node variance) and Variance (the reduction in the

square error from the node means).

Decision trees implemented in SAS Enterprise

Miner can handle missing values in three ways: Use

in search (UIS - using the missing values when

calculating worth a rule of splitting; this will always

produce a splitting rule that assigns the missing

values to the branch that maximizes the worth of the

split), Largest Branch (LB - assigns the observations

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554

that contain missing values to the largest branch) and

Most Correlated Branch (MCB - missing values are

assigned to the branch with the lowest residual sum

of squares calculated) (SAS Ent. Miner, 2016).

The representative tests to determine the most

appropriate process method for missing values and

target variable are summarized at the beginning of

Table 2. The branching and depth factor of decision

tree were kept at node default settings. Thus, the

smaller value for the mean square error in the

validation set (V_ASE) is 137.17 obtained by the

DT2 tree using Variance and Use in search methods.

Table 2: The most relevant decision tree experiments.

Tree TVCA MV BF TD V_ASE

DT1 ProbF UIS 2 6 156.50

DT2 Variance UIS 2 6 137.17

DT3 ProbF LB 2 6 157.74

DT4 ProbF MCB 2 6 156.50

DT5 Variance LB 2 6 138.42

DT6 Variance MCB 2 6 137.17

DT7 Variance UIS 3 6 149.75

DT11 Variance UIS 7 6 140.44

DT14 Variance UIS 2 7 136.53

DT15 Variance UIS 2 8 135.33

DT16 Variance UIS 3 4 153.92

DT17 Variance UIS 3 8 149.75

DT19 Variance UIS 5 8 162.94

The most relevant experiments made to determine

optimum values for the depth factor and decision tree

branching are presented in second part of Table 2. All

decision trees have set Variance and Use in search

methods. It is noted that DT15 has 135.33 the lowest

V_ASE value. For a branching factor greater than 2

the V_ASE begins to grow again. The DT15

identifies 8 as optimal depth value.

In Figure 1 is sketched a partial view of DT15

decision tree. The root node selected PM10_t

parameter, the amount of PM

measured at the t

moment influence the changes in PM

concentration

in the next hour. The same importance for this

parameter is considered by the nodes from level 1, 2

and partial level 3 in the tree.

The next parameter in generating the decision tree

is NO2_t, followed by CO_t. At the 5

level are being

selected the other database pollutants: PM10_t_1,

PM10_t_2, PM10_t_3. A possible explanation can be

that the current concentrations most influence the

next hour PM

value compared with other time

moments measured data.

Figure 1: Partial view of DT15 decision tree.

Table 3: The model score on distributed intervals.

Range for

Predicted

Mean

Target

Mean

Predicted

Model

Score

141.572 - 148.518 145.032 148.518 145.045

113.787 - 120.733 81.26 116.288 117.26

99.895 - 106.841 105.526 100.183 103.368

92.949 - 99.895 79.573 97.549 96.422

79.056 - 86.003 87.78 81.703 82.53

72.110 - 79.056 75.53 75.025 75.583

58.218 - 65.164 61.339 59.822 61.691

51.272 - 58.218 54.016 52.757 54.745

44.326 - 51.272 41.958 46.766 47.799

37.380 - 44.326 37.027 39.175 40.853

30.433 - 37.380 31.361 32.309 33.906

23.487 - 30.433 23.977 27.473 26.96

16.541 - 23.487 21.754 21.456 20.014

9.595 - 16.541 10.493 11.246 13.068

Table 3 presents the model score on distributed

intervals for the validation data set.

Statistical results of the leaf nodes are shown in

Table 4. There are selected only the nodes that

presented the pattern usage degree greater than 0.70.

The pattern usage degree is the ratio of the number of

observations in the branch to the number of

observations in the root node (SAS Ent. Miner, 2016).

< 92.71 or Missing

PM10_next_1

=100.18

PM10_next_1

=81.70

PM10_next_1

=132.68

PM10_next_1

=148.51

PM10_t_2

PM10_next_1

=74.46

≥ 92.71

PM10_t

PM10_next_1

=97.41

< 0.81 or Missing

CO_t

PM10_next_1

=74.97

≥ 0.81

< 84.66

PM10_t

≥ 84.66 or Missing

< 108.87 or Missing

PM10_t

≥ 108.87

< 131.81 or Missing

PM10_t

PM10_next_1

=116.28

≥ 131.81

PM10_t

PM10_next_1

=97.96

< 74.99 or Missing

NO2_t

PM10_next_1

=182.78

< 169.61 or Missing

PM10_t

≥ 169.61

< 96.05

≥ 96.05 or Missing

≥ 74.99

< 116 ≥ 116 or Missing

Data Mining and ANFIS Application to Particulate Matter Air Pollutant Prediction. A Comparative Study

555

Table 4: Leaf nodes statistical results (selection by pattern usage degree).

Node id Parent id

Node

depth

Predicted:

PM10_next_1

Validated:

PM10_next_1

RASE VRASE

Pattern usage

degree

21 10 4 46.48 41.41 8.73 13.17 1.03

36 19 5 31.13 29.66 7.25 8.48 0.98

37 19 5 35.45 32.21 8.96 5.74 0.94

43 22 5 58.57 59.82 8.24 10.09 0.90

33 17 5 21.29 21.88 4.25 6.15 0.88

35 18 5 25.21 19.00 4.09 14.94 0.88

68 37 6 36.59 32.92 7.84 5.25 0.84

60 33 6 21.68 22.33 4.04 6.13 0.81

80 43 6 59.82 61.34 8.24 10.24 0.72

97 60 7 22.21 22.32 3.86 6.33 0.72

Node 21 has as parent the node 10, is situated at

the 4

level depth, it is characterized by a value of

8.73 for the RASE training set, and 13.17 V_RASE

value in the validation set. The PM10_next_1

predicted value is 46.48 and the PM10_next_1

validated value is 41.41. Some IF-THEN rules

generated by the decision tree are shown in Table 5.

Rule 1 is specific to the node 12 and predicts an

extremely high value 74.97 for PM10_next_1 if

PM10_t is in the range [67.35, 84.66]. Rule 2 of the

node 23 identifies the value 43.53 of the target

variable if PM10_t is between 49.29 and 67.35 and if

NO2_t is greater than 57.59.

The knowledge extraction generated rules

generally capture characteristics that influences PM

concentration evolution. Thus, the forecast model

proposed has identified influence of the PM

concentrations measured in a previous moment of

time as well as other atmospheric parameters

measured locally (NO

, CO).

Table 5: Examples of knowledge extraction rules.

Rule Rule description

IF PM10_t < 84.66 AND PM10_t >= 67.35 THEN

Predicted: PM10_next_1 = 74.97

IF PM10_t < 67.35 AND PM10_t >= 49.29 AND

NO2_t >= 57.595 THEN Predicted: PM10_next_1

= 43.53

IF PM10_t <108.87 AND PM10_t >= 84.66 OR

MISSING AND CO_t < 0.815 OR MISSING

THEN Predicted: PM10_next_1 = 97.41

IF PM10_t_3 >= 50.15 AND PM10_t < 37.335

AND PM10_t >= 31.995 THEN Predicted:

PM10_next_1 = 26.4975

IF PM10_t_1 < 34.275 AND PM10_t < 41.165

AND PM10_t >= 37.335 AND NO2_t >= 23.35

THEN Predicted: PM10_next_1 = 28.37

IF PM10_t_2 < 97.21 OR MISSING AND

PM10_t < 108.87 AND PM10_t >= 96.055 OR

MISSING AND CO_t >= 0.815 THEN Predicted:

PM10_next_1 = 100.183

The statistical parameters used in this analysis

(RMSE and MAPE) present satisfactory values for

the test data set: RMSE is 6.41 µg/m

and MAPE is

7.36 %. The running time is 19.66 seconds.

4.3 ANFIS Method

The structure of the ANFIS model is presented in

Figure 2.

Figure 2: Structure of the ANFIS model.

The model has six inputs, namely: four PM

concentrations, from current hour (PM

(t)) to three

hours ago, current value of NO

concentration and

current value of CO concentration. The output of the

model is the prediction of the next hour PM

concentration – PM

(t+1). The number of inputs and

their granularity determine a rule base of 729 rules.

The specifics of used data set imposed grid partition

as FIS generating method.

This study is performed in MATLAB

where four

ANFIS architectures will be used. Architecture 1

consists of Gaussian membership functions for inputs

and backpropagation optimization algorithm for the

training of the neural network, while architecture 2

uses the same type of membership functions but the

optimization algorithm is hybrid. Similarly,

architectures 3 and 4 use triangular membership

functions for inputs and as optimization algorithms

backpropagation and hybrid respectively.

ANFIS

PREDICTION

MODEL

(t-3)

(t-2)

(t-1)

(t)

CO(t)

(t+1)

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The simulation results for each architecture, in

terms of statistical parameters (RMSE, IA and

MAPE), are presented in Table 6.

Table 6: Statistical parameters for ANFIS architectures.

ANFIS

RMSE[g/m

]

IA MAPE[%]

Arch. 1 5.4828 0.9348 11.9772

Arch. 2 7.0774 0.9148 9.8816

Arch. 3 5.0552 0.9530 9.2518

Arch. 4 5.1240 0.9508 9.2606

From Table 6 it can be observed that the best

values of the statistical parameters (smallest RMSE

and MAPE and IA closest to 1) are obtained for the

third ANFIS architecture with triangular membership

functions for inputs and backpropagation as

optimization algorithm. The testing error for this case

is illustrated in Figure 3 and a partial view of the

comparison between testing data and predicted data is

presented in Figure 4.

Figure 3: Testing error.

Figure 4: Comparison between testing and predicted data.

The training time for this model is around 24

hours due to the large number of rules from the fuzzy

inference system.

Table 7: Comparative results.

Method RMSE MAPE Running time

Data Mining 6.41 7.36

 1 min

ANFIS 5.06 9.25

 24 hours

As it can be seen in Table 7 the ANFIS method

has a small value of RMSE. However, the data mining

(decision tree) method has the advantage of a very

small running time (which includes training,

validation and testing) and a smaller value for MAPE.

5 CONCLUSIONS

Our research work investigated the potential use of

two AI based prediction models: a data mining

technique, decision trees, and the ANFIS model. Both

models build a rule base that can be used in a

knowledge base system. Decision trees generate a

rule base with the extracted knowledge, while ANFIS

has an internal fuzzy rule base on which the

prediction is performed.

There are few research studies that compare data

mining and ANFIS methods used in forecasting and

our work tries to supplement this research field. Our

comparative study revealed that ANFIS performs

better than data mining (decision tree) method in

terms of RMSE and IA, while data mining presents

very small running time and smaller MAPE. The

main drawback of the ANFIS method is the very long

time associated to the training phase (determined by

the large number of fuzzy rules, because it starts with

an empty fuzzy rule base). Due to the data specifics

from this case study, clustering method for generating

FIS structure could not be used to diminish the

number of fuzzy rules.

As future work we shall combine the two methods

by using the rule base generated with the data mining

method (decision tree) as the initial fuzzy rule base of

the FIS structure, thus decreasing the ANFIS

computational effort.

ACKNOWLEDGEMENTS

The research leading to these results has received

funding from EEA Financial Mechanism 2009-2014

under the project ROKIDAIR “Towards a better

protection of children against air pollution threats in

the urban areas of Romania” contract no.

20SEE/30.06.2014.

0 20 40 60 80 100 120 140 160 180 200

-0.2

-0.15

-0.1

-0.05

0.05

0.1

0.15

0.2

Samples

Testing erro

0 20 40 60 80 100 120 140 160 180 200

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

Samples

Data

Tes ting dat a

Forecasted data

Data Mining and ANFIS Application to Particulate Matter Air Pollutant Prediction. A Comparative Study

557

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