Bushfire Susceptibility Mapping Using Gene Expression

Programming and Machine Learning Methods: A Case Study of

Kangaroo Island, South Australia

Maryamsadat Hosseini

and Samsung Lim

School of Civil and Environmental Engineering, University of New South Wales, High Street, Sydney, Australia

Keywords: Bushfire, Susceptibility Map, Gene Expression Programming, Machine Learning, Kangaroo Island.

Abstract: Kangaroo Island, South Australia is one of the bushfire-prone areas. A catastrophic bushfire known as the

black summer hit Kangaroo Island in 2019/2020. We chose Kangaroo Island as a case study to generate

bushfire susceptibility maps using five different methods, namely gene expression programming (GEP),

random forest (RF), support vector machine (SVM), frequency ratio (FR) and logistic regression (LR). To

generate bushfire susceptibility maps, we used eight contributing factors including: digital elevation model,

slope, aspect, normalized difference vegetation index, distance to roads, distance to streams, precipitation,

and land cover. The proposed methods were evaluated by area under the curves (AUCs) of receiver operating

characteristic. RF performed best with an AUC of 0.93, followed by SVM and GEP with AUCs equal to 0.89

and 0.88, respectively, but LR and FR performed least among the five methods with AUCs 0.85 and 0.84,

respectively. The generated bushfire susceptibility maps show that western and central areas of Kangaroo

Island are highly vulnerable to bushfire.

1 INTRODUCTION

Bushfires are dangerous and destructive to both

human and nature, which affect the ecological

balance of the environment (Leuenberger et al., 2018;

Zhang et al., 2019). Generation of a nation-wide

bushfire susceptibility map is difficult (Valdez et al.,

2017) as there are many areas with unknown or

inaccurate spatial variability in bushfire susceptibility

mapping (Valdez et al., 2017). Consequently,

different methods have been used in different studies

(Valdez et al., 2017). Statistical methods such as

weights of evidence (WOF) and frequency ratio (FR)

have been applied in many studies to generate

bushfire susceptibility (Dorji and Ongsomwang,

2017; Hong et al., 2019, 2017; Valdez et al., 2017).

In contrast, several studies showed that machine

learning techniques could have a better performance

than statistical methods do in this field (Gholamnia et

al., 2020; Tehrany et al., 2019; Valdez et al., 2017).

However, statistical methods are easy to apply,

machine learning methods are independent from

https://orcid.org/0000-0002-2858-9705

https://orcid.org/0000-0001-9838-8960

expert’s opinion (Hosseini and Lim, 2022; Jaafari et

al., 2017). Gene expression programming (GEP),

introduced by Ferreira (2001), is a population-based

algorithm similar to a genetic algorithm (GA) and

genetic programming (GP) (Ferreira, 2001). GEP is

based on a fitness function and process developed to

find the best solution for the specific problem while

minimizing the error (Ferreira, 2001). Recently,

Hosseini and Lim (2021) applied GEP, logistic

regression (LR), FR and ensemble of these methods

for bushfire susceptibility mapping. They showed that

GEP ensembled with FR had the highest AUC in

Victoria, Australia. In addition, in another study

which applied in New South Wales, Australia

(Hosseini and Lim, 2022), GEP ensembled with FR

had the highest AUC among the different machine

learning techniques such as RF and SVM.

Therefore, the goal of this study is to investigate

the application of statistical methods such as FR and

different machine learning techniques including

random forest (RF), support vector machine (SVM)

and LR, in bushfire susceptibility mapping, as an

Hosseini, M. and Lim, S.

Bushﬁre Susceptibility Mapping Using Gene Expression Programming and Machine Learning Methods: A Case Study of Kangaroo Island, South Australia.

DOI: 10.5220/0011724700003473

In Proceedings of the 9th International Conference on Geographical Information Systems Theory, Applications and Management (GISTAM 2023), pages 123-127

ISBN: 978-989-758-649-1; ISSN: 2184-500X

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

123

extension of the abovementioned studies. Finally, we

compared the maps generated by different methods

with the result of GEP in Kangaroo Island.

2 MATERIALS AND METHODS

2.1 Study Area

This study aims to generate bushfire susceptibility

maps in Kangaroo Island (Figure 1A). Kangaroo

Island with 3,890 km

area which is relatively flat,

surrounded by cold waters and has a 458-km

complicated coastline (Bourman, Murray-Wallace

and Harvey, 2016; Peace and Mills, 2012). Kangaroo

Island after Tasmania and Melville Island is the

largest Island in Australia which is located in South

Australia and is vulnerable area for regular bushfires

(Bonney et al., 2020; Bourman, Murray-Wallace and

Harvey, 2016). For example, 2,100 km

were burned

during the last bushfire known as the black summer

(2019-2020) in Kangaroo Island (Bonney et al.,

2020). The temperature in Kangaroo Island reached

on average to 24 ˚C in summer, and annual

precipitation on average is 567 mm since 1988, while

forests in the west and central parts of the Island have

the highest precipitation in the Island (Bonney et al.,

2020).

2.2 Data Collection

2.2.1 Bushfire Reference Map

Generating a bushfire reference map is the first stage

in bushfire susceptibility mapping (Hosseini and Lim,

2022). A bushfire reference map in Kangaroo Island

was generated using the MODIS burned-area

(MCD64). MCD64 with 500-m resolution is

available monthly from the website of University of

Maryland (MODIS Fire, 2020). In this study, we

collected the data for 10 years (2010 to 2020) in

November to February which is known as a fire

season in Australia (Figure 1B). The reference map

was randomly divided into two groups: 70% of the

data considered as the training set and 30% of the data

used for the testing set.

Figure 1: A) Australia map with location of Kangaroo

Island, B) Bushfire inventory map for 10 years (2010 to

2020) in Kangaroo Island.

2.2.2 Conditioning Factors

Several factors are influencing bushfires and may

change the behaviour of the bushfires. Therefore,

these factors also known as conditioning factors,

should be considered in bushfire modelling studies.

In this study, we considered topographic factors as an

important factor in bushfire occurrence including

digital elevation model, slope and aspect which

considered in similar studies (Bui, 2019; Gholamnia

et al., 2020; Tonini et al., 2020). There are many

research that took climate factors such as

precipitations and temperature into account

(Ghorbanzadeh et al., 2019; Jaafari and Pourghasemi,

2019; Razavi-Termeh, Sadeghi-Niaraki and Choi,

2020). Fuel load factors including normalized

difference vegetation index (NDVI) and land cover

also were proposed in different studies (Bui, 2019;

Hong, Jaafari and Zenner, 2019; Razavi-Termeh,

Sadeghi-Niaraki and Choi, 2020). Finally, other

factors such as distance to roads, distance to water

streams and topographic wetness index (Zhang,

Wang and Liu, 2019; Eskandari, Miesel and

Pourghasemi, 2020; Gholamnia et al., 2020) assumed

as conditioning factors in this study. However, we

eliminated two factors (temperature and topographic

wetness index) due to the high correlation of these

two variables with other variables.

2.3 GEP

GEP introduced by Ferreira (2001), similar to GA and

GP, is based on populations of individuals and

individuals had been selected by their fitness

(Ferreira, 2001). The main difference between these

three algorithms is in individuals’ characters which

create the populations (Ferreira, 2001). Individuals in

GA have fixed linear structure, while chromosomes

in GP are nonlinear structures with different sizes and

GISTAM 2023 - 9th International Conference on Geographical Information Systems Theory, Applications and Management

124

shapes (Ferreira, 2001). Individuals in GEP are linear

with a fixed length, but they can be expressed in

nonlinear structures with different sizes and shapes

(Ferreira, 2001). GA and GP have their own

limitations. Manipulation in GA is easy, but the

complexity in individuals is not available. However,

GP maintains the complexity of individuals, but the

reproduction is too challenging and difficult (Ferreira,

2001). Therefore, GEP (as a result of further

development of GP) is able to deal with a complex

phenomenon and solve the problem faster than GP

(Alkroosh and Nikraz, 2011).

The GEP, a freshly developed artificial

intelligence method, has demonstrated its

effectiveness in the engineering sector (Alkroosh and

Nikraz, 2011). GEP can generate the mathematical

formula for bushfire susceptibility which make

interpretation and explanation steps easy and simple

(Hosseini and Lim, 2022).

2.4 Machine Learning Techniques

In this study, we considered three different machine

learning techniques including LR, SVM and RF. LR

is a popular method in modelling the bushfires and

other types of natural hazards (Jaafari et al., 2019;

Zhang et al., 2016). LR has the ability to find the

spatial relationship between several independent

variables and dependent variable to find the event’s

pattern. LR also makes the results interpretation easy

and understandable (Jaafari et al., 2019; Zhang et al.,

2016). In the SVM model, a linear hyperplane

separates two different classes using statistical

learning theory and principle of risk minimization

(Jaafari and Pourghasemi, 2019). A separating

hyperplane converts the nonlinear problem to a linear

problem (Jaafari and Pourghasemi, 2019). In

addition, RF has improved classification accuracy

significantly by using an ensemble of trees while trees

are allowed to vote for the most popular label

(Breiman, 2001). While the number of trees in the

forest increased, the generalization error converges to

the small value for the forests (Breiman, 2001). In

machine learning methods, data are divided into two

groups including training data and testing data.

Firstly, the model starts to get trained by the training

data, and secondly, the model gets evaluated by using

the testing data.

3 RESULTS

We generated bushfire susceptibility maps using

different methods including GEP, RF, SVM, FR and

LR. Data have been divided randomly in two groups.

The models have been trained by using 70% of the

data and evaluated by using 30% of the data. The

natural break classification method was used to

classify bushfire susceptibility maps generated by the

GEP, RF, SVM, FR and LR. The generated maps were

categorized into five different classes including very

low, low, moderate, high and very high (Figure 2).

The bushfire susceptibility map generated by GEP

(Figure 2A), showed the central and western part of

Kangaroo Island had high to very high potential for

bushfire while the eastern part showed very low to

moderate potential for bushfire. The model generated

by GEP had AUC and accuracy equal to 0.88 and

80% respectively. We also generated a bushfire

susceptibility map using RF (Figure 2B). The

generated map by RF categorized the central and

western part of Kangaroo Island with very high

potential for bushfire while the eastern part had very

low potential for bushfire. The AUC and accuracy of

the model generated by RF was 0.93 and 85%,

respectively. The maps generated by SVM and RF

were similar to each other. The majority of the

western and central area of Kangaroo Island labelled

as very high and eastern area mainly determined as

very low and moderate potential for bushfire (Figure

2C). The SVM model had AUC equal to 0.89 and

accuracy equal to 82% in this study area. Finally, the

bushfire susceptibility maps generated by FR and LR

were similar. These two maps (Figure 2D and 2E)

represented the study area in variety of different

classes. The western part of study area covered with

very high and high potential for bushfire. The central

area covered by moderate potential, while eastern part

showed very low and moderate potential of bushfire.

The AUC of the model generated by FR was 0.84

while accuracy was 73% and the generated model by

LR had AUC and accuracy equal to 0.85 and 78%,

respectively. The model generated by LR was:

Z = -6.740 + 1.550 × A + 1.517 × E + 1.138

× L + 1.126 × N + 1.182 × P + 0.336 × DR

+ 0.307 × S - 0.582 × DS

)

where A is aspect, E is digital elevation model, L is

landcover, N is NDVI, P is precipitation, DR is

distance to roads, S is slope and DS is distance to

streams.

Based on five different maps generated by

different methods (Figure 2), bushfire susceptibility

maps generated by RF was the most successful

method to classify burned and unburned areas

followed by SVM and GEP. Therefore, the maps

generated by LR and FR were the least accurate in

this study.

Bushﬁre Susceptibility Mapping Using Gene Expression Programming and Machine Learning Methods: A Case Study of Kangaroo Island,

South Australia

125

Figure 2: Bushfire susceptibility mapping by A) GEP, B) RF, C) SVM, D) FR and E) LR. Generated maps classified in five

different classes from very low (dark green) to very high (red).

4 DISCUSSIONS

The generated maps by different methods were

classified in five categories from very low to very

high classes. The bushfire susceptibility maps

generated by different techniques showed high

potential of bushfire in central and western part of

Kangaroo Island which was dominantly covered by

forest and dense vegetation and low potential in

eastern part were mainly covered by grassland. The

maps generated by RF and SVM were very similar,

and the Island was mainly divided in two different

classes (very low and very high), however, five

different classes (very low to very high) were more

clear in the bushfire susceptibility maps generated by

GEP, FR and LR in Kangaroo Island. The maps

generated by GEP, FR and LR were so similar while

GEP had higher AUC and accuracy than FR and LR.

On the other hand, the five different generated maps

showed incorrect classification in eastern and south-

eastern parts of study area due to the land cover of

Forest and Savannas in these areas. RF had the least

incorrect classification and FR had the most incorrect

classification. The most important barrier for

applying machine learning techniques such as RF and

SVM in bushfire modelling was lack of

interpretability (Jain et al., 2020). In contrast GEP is

easy to apply and understand and can represent the

formula to have an easier interpretation and

explanation step (Hosseini and Lim, 2022).

In addition, RF has an ability to determine variable

importance using internal estimates (Breiman, 2001).

In this study, RF showed that the precipitation factor

had the highest importance followed by digital

elevation model and land cover, in bushfire

susceptibility mapping. Previous study also showed

that land cover and precipitation factors had the

highest importance values (using RF) in bushfire

susceptibility mapping in New South Wales,

Australia (Hosseini and Lim, 2022). The bushfire

susceptibility maps generated by different methods in

this study showed different accuracies and AUCs.

Amongst the five different methods, RF had the

highest values in both accuracy and AUC, followed

by SVM and GEP. Therefore, FR and LR had the

lowest values for accuracy and AUC. Similarly,

previous study showed GEP ensembled with FR

outperformed the other methods such as LR and FR

in Victoria, Australia (Hosseini and Lim, 2021).

5 CONCLUSIONS

In this study, we investigated and compared the

application of machine learning techniques and GEP

in bushfire susceptibility mapping. We applied five

different methods including GEP, RF, SVM, FR and

LR to generate bushfire susceptibility maps in

Kangaroo Island. RF had the highest AUC and

accuracy followed by SVM and GEP, however, LR

and FR had the least performance and lower AUC and

accuracy. Based on the generated maps, the western

and central part of the Island had the highest potential

of bushfire while the eastern part of Island had a low

potential of bushfire. Furthermore, GEP is an advance

and new method for generating bushfire maps and RF

had the highest AUC. Therefore, in different study

areas, we can apply different methods to generate

bushfire susceptibility maps and use them to improve

the management of bushfire and have a better

performance in bushfire prone areas.

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South Australia

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