ESTIMATING GREENHOUSE GAS EMISSIONS USING

COMPUTATIONAL INTELLIGENCE

Joaquim Augusto Pinto Rodrigues, Luiz Biondi Neto, Pedro Henrique Gouvêa Coelho

State University of Rio de Janeiro, FEN/DETEL, R. S. Francisco Xavier, 524/Sala 5006E, Maracanã, RJ, 20550-900, Brazil

João Carlos Correia Baptista Soares de Mello

Fluminense Federal University,Production Eng. Dep., R. Passo da Pátria, 156, S. Domingos, Niteroi, RJ, 24210-240, Brazil

Keywords: Computational Intelligence, Neuro-Fuzzy Systems, Greenhouse Gas Emissions.

Abstract: This work proposes a Neuro-Fuzzy Intelligent System – ANFIS (Adaptive Network based Fuzzy Inference

System) for the annual forecast of greenhouse gases emissions (GHG) into the atmosphere. The purpose of

this work is to apply a Neuro-Fuzzy System for annual GHG forecasting based on existing emissions data

including the last 37 years in Brazil. Such emissions concern tCO

(tons of carbon dioxide) resulting from

fossil fuels consumption for energetic purposes, as well as those related to changes in the use of land,

obtained from deforestation indexes. Economical and population growth index have been considered too.

The system modeling took into account the definition of the input parameters for the forecast of the GHG

measured in terms of tons of CO

. Three input variables have been used to estimate the total tCO

one year

ahead emissions. The ANFIS Neuro-Fuzzy Intelligent System is a hybrid system that enables learning

capability in a Fuzzy inference system to model non-linear and complex processes in a vague information

environment. The results indicate the Neural-Fuzzy System produces consistent estimates validated by

actual test data.

1 INTRODUCTION

Human activities have produced inadvertent effects

on weather and climate. Adding gases such as

carbon dioxide and methane into the atmosphere has

increased the greenhouse effect and contributed to

global warming by raising the mean temperature of

the Earth by about 0.5°C since the beginning of the

20th century.

More recently, chlorofluorocarbons (CFCs),

which are used as refrigerants and in aerosol

propellants, have been released into the atmosphere,

reducing the amount of ozone worldwide and

causing a thinning of the ozone layer over Antarctica

around October. The potential consequences of these

changes are vast. Global warming may cause sea

level to rise, and the incidence of skin cancer may

increase as a result of the reduction of ozone. In an

effort to prevent such consequences, production of

CFCs has been curtailed and many measures have

been suggested to control emission of greenhouse

gases, including the development of more efficient

engines and the use of alternative energy sources

such as solar energy and wind energy. The purpose

of this paper is to apply Neuro-Fuzzy Systems for

annual greenhouse gases emissions (GHG)

forecasting. GHG estimates in other contexts have

been considered by researchers using statistical

regression and modeling (Ghorbani et alli, 2008),

(Searchinger and Heimlich, 2007). Time series

applications using computational intelligence have

also been considered by the authors ( Biondi et. Alli,

2004). This paper is organized in four sections. The

first section is the present introduction. The second

section describes the used Neuro-Fuzzy Model.

Section three shows results and discussions and the

paper ends with section four depicting results and

future work.

2 ANFIS STRUCTURE

The ANFIS (Adaptive Network Based Fuzzy

Inference System) structure is a Fuzzy inference

248

Augusto Pinto Rodrigues J., Biondi Neto L., Henrique Gouvêa Coelho P. and Correia Baptista Soares de Mello J. (2009).

ESTIMATING GREENHOUSE GAS EMISSIONS USING COMPUTATIONAL INTELLIGENCE.

In Proceedings of the 11th International Conference on Enterprise Information Systems - Artiﬁcial Intelligence and Decision Support Systems, pages

248-250

DOI: 10.5220/0002014402480250

 SciTePress

system (Jang et alli, 1997), (Rutkowski,2004) . The

ANFI structure is depicted in Figure 1.

Camada 1 Camada 3 Camada 4 Camada 5Camada 2Camada 1 Camada 3 Camada 4 Camada 5Camada 2Camada 1 Camada 3 Camada 4 Camada 5Camada 2

Layer 1 Layer 3 Layer 4 Layer 5Layer 2

Camada 1 Camada 3 Camada 4 Camada 5Camada 2Camada 1 Camada 3 Camada 4 Camada 5Camada 2Camada 1 Camada 3 Camada 4 Camada 5Camada 2Camada 1 Camada 3 Camada 4 Camada 5Camada 2Camada 1 Camada 3 Camada 4 Camada 5Camada 2Camada 1 Camada 3 Camada 4 Camada 5Camada 2

Layer 1 Layer 3 Layer 4 Layer 5Layer 2

Figure 1: ANFI Structure.

The ANFI structure comprises five layers where

layer one and four include adaptive nodes. The first

layer nodes concern fuzzy sets related to the input

variables whose outputs are membership functions.

Layer two nodes are not adaptive and their task is to

perform a normalization process which is part of a

defuzzification procedure. Layer four leads to layer

five that evaluates the system output including the

final defuzzification in connection with layers 3 and

4. Details of the ANSI system can be found in (Jang

et alli, 1997).

Learning procedures for the ANFI structure

involve the parameters optimization of the adaptive

nodes in layers one and four. The optimization

procedure uses usually deepest descent gradient

techniques (in connection with backpropagation

techniques Jang et alli, 1997). Figure 2 depicts the

ANFI structure used in this paper where the inputs

are Var_1 : total emission at time t-1; Var_2 : GDP

(Gross Domestic Product) at time t; Var_3 :

Population at time t. The output is variable is the

total emission at time t. For validation purposes,

20% of the available data was set apart for testing. In

other words the system was trained with 80 % of the

available data.

Sistema

Neuro-Fuzzy

Var_1

Var_2

Var_3

Saída

Sistema

Neuro-Fuzzy

Var_1

Var_2

Var_3

Saída

Sistema

Neuro-Fuzzy

Var_1

Var_2

Var_3

Output

Neuro - Fuzzy

Var_1

Var_2

Var_3

ANFIS

Sistema

Neuro-Fuzzy

Var_1

Var_2

Var_3

Saída

Sistema

Neuro-Fuzzy

Var_1

Var_2

Var_3

Saída

Sistema

Neuro-Fuzzy

Var_1

Var_2

Var_3

Saída

Sistema

Neuro-Fuzzy

Var_1

Var_2

Var_3

Saída

Sistema

Neuro-Fuzzy

Var_1

Var_2

Var_3

Output

Neuro - Fuzzy

Var_1

Var_2

Var_3

ANFIS

Figure 2: ANFI Structure Configuration.

Figure 3, 4 and 5 shows Population data, GDP data,

and total emission data used in the ANSI prediction

scheme. (Rodrigues, 2008).

Population Evolution

20000000

40000000

60000000

80000000

100000000

120000000

140000000

160000000

180000000

200000000

1970

1972

1974

1976

1978

1980

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

2006

Year

People

Figure 3: Population Evolution Data.

GDP Evolution

500000

1000000

1500000

2000000

2500000

1970

1972

1974

1976

1978

1980

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

2006

Year

Figure 4: GDP Evolution Data.

Emission/R$ Evolution Data

0,00

100,00

200,00

300,00

400,00

500,00

600,00

1970

1972

1974

1976

1978

1980

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

2006

Yea r

tCO2/R$

Figure 5: Emission/R$ Evolution Data.

3 RESULTS

Figure 6 shows the results comparing the trained

data with ANFI estimated data.

ESTIMATING GREENHOUSE GAS EMISSIONS USING COMPUTATIONAL INTELLIGENCE

249

0 5 10 15 20 25 30

x 10

Real x Estimated - Training

Value

Real

Estimated

0 5 10 15 20 25 30

x 10

Real x Estimated - Training

Value

Real

Estimated

Figure 6: Trained x Estimated Data.

Figure 7 compares the ANFI estimates with the

actual data for the validation set. Remember that the

validation set is composed of data not used in the

training of the ANSI structure. One can see an

agreement concerning estimates and data.

1 2 3 4 5 6 7

5.5

6.5

7.5

8.5

9.5

x 10

Real x Estimated – Validation Set

Value

Real

Estimated

1 2 3 4 5 6 7

5.5

6.5

7.5

8.5

9.5

x 10

Real x Estimated – Validation Set

Value

Real

Estimated

Figure 7: Validation Set x Estimated Data.

The average error for the validation set was about

7%. Figure 8 shows estimates comparison with data

for all data i.e. training and validating set in order to

give a better perspective of the whole estimation

process.

Train and Validation Data x Estimated Data

0,00

100000000,00

200000000,00

300000000,00

400000000,00

500000000,00

600000000,00

700000000,00

800000000,00

900000000,00

1 3 5 7 9 1113 15 171921232527293133

Yae r

tCO2

Real Estimated

Figure 8: Trained and Validation Data x Estimated Data.

4 CONCLUSIONS

This paper applied a Neuro-Fuzzy approach for the

estimation of greenhouse gas emissions. An ANFI

structure was used whose inputs were GNP,

Population and one step back emissions. Average

errors were around 7 % for untrained data and the

estimated emissions agreed with the used trained

data. Future work concerns using data relating other

type of emissions not considered in the present

work.

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