A Formulation of Big Data Analytics Model in Strengthening the Disaster Risk Reduction

Journal of Environmental Treatment Techniques

2020, Volume 8, Issue 1, Pages: 418-41ꢀ

J. Environ. Treat. Tech.

ISSN: 2309-1185

Journal web link: http://www.jett.dormaj.com

A Formulation of Big Data Analytics Model in

Strengthening the Disaster Risk Reduction

Syamil Zayid , Nur Azaliah Abu Bakar , Mageshwari Valachamy , Nur Shuhada Abdul

Malek , Suraya Yaacob , Noor Hafizah Hassan

Razak Faculty of Technology and Informatics, Universiti Teknologi Malaysia, 54100 Kuala Lumpur, Malaysia

Received: 13/08/2019 Accepted: 18/01/2020 Published: 20/02/2020

Abstract

A natural disaster is a serious event that contributes to the damage of infrastructures and property losses, the demand of

budgetary allocation, disruption of economic and social activities, damages to the environment, and threat to human life. In disaster

management, one of the aims is to reduce the impact of natural disaster through disaster risk management. However, the traditional

data risk management mechanism to store and analyse huge disasters has become a challenge for relevant organizations due to its

massive datasets, especially when it deals with big data and analytics. Therefore, the aim of this paper is to formulate a big data

analytics model to strengthen the disaster risk reduction for Selangor State, Malaysia, comprehending both traditional datasets

(geospatial data) and big data analytics (nonspatial data). To this end, 59 factors and available datasets were classified into six

categories: ecology, economic, environment, organisation, social, and technology. These factors were derived from existing studies

and then validated in a focus group discussion with 54 government agencies involved disaster risk management in Selangor State,

Malaysia. The final output of this paper is Big Data Analytics Model for Disaster Risk Reduction, which will be useful to all

stakeholders related to disaster risk management and disaster risk reduction initiatives.

Keywords: Disaster risk management; Disaster risk reduction; Big data analytics; Selangor state

Introduction¹

supports and plan for achieving DRR identified goals, but

these two terms are used interchangeably and have some

overlap that provides very similar meaning in practice.

In this modern age, decision-makers have started to

focus on applying enormous data to their decision-making

processes. Enormous data, which is also referred to as Big

Data, is a huge dataset that is high in volume, variety, and

velocity; in this regard, a challenging issue is how to manage

it using traditional techniques and tools[3]. Thus, to satisfy

these managing requirements and extract the value and

knowledge from huge datasets that are growing every

second, a modern solution need to be developed. In addition,

solution provided might be beneficial to decision-makers for

their valuable insights into such diverse and rapidly

changing data (involving structured, semi-structures, and

unstructured data) ranging from daily transactions to

customer interactions and social network data. Big data

analytics can help to produce the value of big data that later

can be harvested [4].

Disaster is an event that occurs around the globe; as a

key challenge, it contributes to serious disruptions to human

life, economy, and sustainable development. Several factors

have most significant contribution to disaster: hazard

inherent from the nature, the extent to which people and their

belongings are exposed to it, vulnerability of affected human

and assets, and their ability to minimize or manage with the

possible harm[1]. Briefly, disaster definition reflects the

losses and the ability to cope with the impact. The United

Nations International Strategy for Disaster Reduction

(UNISDR) referred disaster as a serious disruption towards

the society or a functional community, involving economic

or environmental impacts and also loss of human life and

properties, which is beyond the ability of the affected

community and society to survive using its own supplies and

resources [2].

Disaster Risk Reduction (DRR) is clearly accepted as the

development and implementation of policies, strategies, and

practices to reduce vulnerabilities and disaster risks across

society. Often used in the same context, the term 'Disaster

Risk Management' (DRM) refers to a systematic approach to

identifying, assessing, and reducing risks of any disaster.

DRM is known to be more focused on implementing

No doubt that good governance and fast decision making

process influence the impact of disaster to community as

suggested by previous studies by Sukowati and Nelwan [5]

and Waheed and Ali [6]. In addition, the ability of big data

in visualising, analysing, and predicting disasters has been

Corresponding author: Nur Azaliah Abu Bakar, Razak Faculty of Technology and Informatics, Universiti Teknologi Malaysia,

4100 Kuala Lumpur, Malaysia. E-mail: azaliah@utm.my.

Journal of Environmental Treatment Techniques

2020, Volume 8, Issue 1, Pages: 418-41ꢀ

focused on much work done by researchers to help to

manage disasters [7, 8]. Furthermore, big data analytics

provide a value that is able to support crisis management and

enhance humanitarian operations. However, the challenges

with the big data are how to manage regarding issues such as

data mining (storing, interlinking, and processing), which is

due to the characteristics of the volume and velocity of the

data itself [9, 10]. In addition, disaster-related data consist

of both spatial and nonspatial data, which makes the data

analysis process more complex [8, 11]. From the recent

studies, there is still a gap in integrating the big data analytics

with DRR practices. Such integration would help to reduce

complexity in traditional data management mechanisms and

enable new kind of disaster preparedness and prevention

services. Therefore, the aim of this paper is to formulate a

big data analytics model applicable to disaster risk reduction.

preparedness. DRR must be incorporated to ensure that the

goals of sustainable development can be achieved. Another

initiative from the United Nations called Sustainable

Development Goals (SDGs) was also introduced (SDGs,

2018; SDSN, 2015). The achievement of the stated SDGs

requires major transformation in the ecosystem of major

areas such as urban planning, energy use, healthcare,

educational system, land use, and technologies deployment.

Yet, few challenges have arisen when incorporating the

SDGs 2030 perspective into budget planning, audits,

procurement policies, human resource management,

regulations, and related public policy needed.

2.1 Big Data Role in Disaster Risk Reduction

Emerging technology in big data is known as innovative

technology that provides new ways to extract value from the

tsunami of available information. The interpretation of big

data may differ in the defined concept and term as there is

no commonly-accepted definition for the Big Data term. In

the context of big data for DRR, data must be diverse in

Variety, high in Velocity, and huge in Volume, which is

known as the 3Vs big data model [15]. Researchers have

extended the Vs up to 9Vs, adding terms like Visualization,

Veracity, Value, and Viability.

A data-driven solution in reducing disaster risks have

long been recognized for their cross-cutting linkage among

key stakeholders such as scientists, policymakers,

emergency responders, and practitioners, and their major

influential role. This is explained well through the reviews

done by Arslan, Roxin [10] and Akter and Wamba [16].

There are a growing number of studies of Big Data Analytics

Related Work

In 2015, the United Nations Office for Disaster Risk

Reduction (UNISDR) reported that globally, disasters

contributed to over 1.3 trillion USD economic losses over a

0-year period [2]. More importantly, 144 million people

were displaced, over 1.4 million injured, and roughly 23

million left homeless. Asia, as home to 60% of the world’s

population, is considered one of the largest disaster-prone

areas.

Based on the international disaster database

OFDA/CRED 1990-2015, Malaysia has reported an average

annual loss of more than 1.3 billion USD from multi-hazards

disasters [12].

In Malaysia, the December 2014 floods in East Coast

Peninsular affected the life of more than half a million people

and inflicted damages up to RM 2.85 billion on public

infrastructure [13]. In June 2015, the Sabah earthquake, with

more than 200 aftershocks, caused 18 human casualties,

tormenting the local community in the Borneo’s most

popular tourist area and its UNESCO World Heritage sites.

While in the Cameron Highlands, Malaysia, the 2013-2014

disasters caused direct socioeconomic impacts, with tourism

numbers falling 20%. These impacts remind us there is still

much to be done to strengthen the nation’s resilience to

disasters and sustainable development.

(

BDA) in the area of disaster, among them are Zobel and

Khansa [17] in characterizing multi-event disaster resilience;

Emmanouil and Nikolaos [18] on how big data analytics is

utilized in prevention, preparedness, response, and recovery

in crisis and disaster management; Papadopoulos,

Gunasekaran [19] on big data role for disaster resilience; and

Masood, So [20] focusing on BDA for supply chain during

disaster. In Malaysia, Abdullah, Ibrahim [12] proposed a

Big Data Analytics Framework for Natural Disaster

Management in their studies.

One of the most important benefits of applying BDA to

DRR is that value of information resulting from the analysis

of Big Data can assist to do advanced prediction of disaster

or, at least, minimize the disaster-induced risks affecting the

ecosystem [18]. Researchers found that the use of big data

in disaster analysis results in disaster preparedness with

suggested proactive deployment of required resources for

coping with an impeding type of disaster in disaster

management [12, 18]. The application of real time big data

analysis is able to alert the risk administration or person

involved in the area about the need for most urgent attention

together with directive approach of recovery procedure

including coordination of volunteer and related logistics

needed during the event of disaster [21].

No doubt, DRM is crucial in reducing the impacts and

losses due to unexpected disasters.

DRM involves

conceptual practices together with an organized strategy to

deal and mitigate the risks through a systematic approach to

understanding, analysing, monitoring, predicting, and

managing the factors and the occurrences of disaster. In

general, disaster management consists of the combination of

many interrelated processes of continual, dynamic

management, and plan for responding to emergency events

14]. Disaster management may not be able to eliminate the

risk; however, it is able to give prediction for early warning,

which can help to minimize the threats towards humanity.

[

.1 Disaster Risk Reduction

Establishing an effective DRR is a global challenge. It

has become an essential factor in promoting big data

analytics towards the capabilities of strengthening and

improving the area of DRR to save lives, prevent and reduce

losses, and strengthen the resilience of cities [9].

Application of big data technology requires involvement of

To reduce the damage caused by a natural disaster, an

initiative by the United Nations called Disaster Risk

Reduction (DRR) was introduced. The aim is to minimize

the affected damage caused by natural hazards through an

ethic of prevention[2]. DRR includes disciplines like

disaster management, disaster mitigation, and disaster

Journal of Environmental Treatment Techniques

2020, Volume 8, Issue 1, Pages: 418-41ꢀ

different parties, which needs having experts from various

backgrounds such as environmental science, social science,

statistics, meteorology, and computer science. Identifying

the different sets of data of different types of disaster from

various agencies and the ability to integrate it has become

one of the key challenges in this field. This integration may

be able to provide valuable insight for the policymakers,

civil society organisations, research centres, business, and

related stakeholders to make informed decisions.

agencies involved in DRM/DRR. The aim of FGD was to

verify the DRR dimension and key elements and to confirm

the related datasets of each key elements.

During FGD, all participants were given a set of

questionnaire and interview questions to validate the

proposed dimensions and key elements. Meanwhile for the

datasets information, a guided interview session was

conducted during the FGD. The results obtained from the

FGD session confirmed all the 6 dimensions, 15 key

elements, and the identification of new 60 datasets.

Methodology

The study started with a review of DRM and DRR

4 Results and Discussion

models proposed already in literature with aim of identifying

the dimensions and key elements of disaster. This study

analysed four models and proposed six dimensions, which

are ecology, economic, environment, organisation, social,

and technology. There are also 15 key elements identified

related to these dimensions. Then, it was followed by a focus

group discussion (FGD) with Selangor State Disaster

Management Unit (SDMU) and 54 Malaysian government

This section presents the results and discussion in regard

to the topic of the research that started by the review of the

existing models, and followed by the discussion on FGD

results.

Table 1: Comparison of Disaster Management frameworks and models

Code Model Name

Dimensions

Theory

Key elements



Disaster emergency management

Technology,

Organisation,



Technology

Organisation

Social

 Training

 Leadership experience

 Community/social vulnerability

 Information system management

Social, Economy

TOSE)

TOSE

(

Economy

Framework [22]



Economic process and activity



Population and

 The functionality of population and demographics

 Community/Social vulnerability

 Cultural values

 The ability of the ecological system to return to or near

its pre-event state

 Disaster emergency management

 Information system management

 Facilities (housing, commercial facilities, and cultural

facilities)

 Lifeline (food supply, health care, utilities,

transportation, and communication networks)

 Economic process and activity

 Community and social support

 Resource allocation

Demographics (Social)

Environmental/Ecosystem

Services (Environment)

Organized Governmental

Services (Organisation)

Physical Infrastructure

(Technical)

Lifestyle and Community

Competence (Social)

Economic Development

The PEOPLES

Framework [23]

N/A

(

Economic)

Social-Cultural Capital

Social)

(



Infrastructure environment

Economic process and activity

Community and social support

Training

Leadership experience

Disaster Press &

Release Model



Social

Economy

Political

Community/social vulnerability

The functionality of population and demographics

Facilities (housing, commercial facilities, and cultural

facilities)

Crunch Model

[

24]



Political influence

Resource allocation

Disaster emergency management



Social

 The functionality of population and demographics

 Community/social vulnerability

 Disaster emergency management

 Political influence

Complex

adaptive system

Complex

Adaptive

System (CAS)

theory

Economic

Political,

Physical,

Ecological

(

[

CAS) theory

25]

 Economic process and activity

Journal of Environmental Treatment Techniques

2020, Volume 8, Issue 1, Pages: 418-41ꢀ

.1 Review of Existing Related Models

Current literature comprises a limited number of models

of the FGD participants from 54 agencies. Table 3 presents

the list of datasets categorized by the dimensions determined

earlier.

of study. A review of literature was done searching with five

keywords: ‘disaster risk reduction’, ‘disaster management’,

4.3 BDA DRR Model Formulation

‘

disaster risk management’, ‘big data’, and ‘big data

The next step is the formulation of BDA for the DRR

model. The dimensions, key elements, and datasets were

verified through the FGD sessions. The underpinning theory

for this model is Technology, Organisation, Social,

Environment, Economic, and Ecology (TOSEEE) suggested

by Vugrin [22]. The chosen model has been previously used

in disaster resilience measurement, and this study extended

its scope into DRR context. The model dimension is

originated from TOSEEE, while the key components and

datasets are derived from the study scope, which is DRR in

Selangor State, Malaysia. Figure 1 shows the completed

model of this study. To validate the model, all FGD

respondents were given a set of questionnaires to rank the

importance and relevancy of the dimensions, key elements,

and datasets. A total of 57 respondents participated during

the session. In the survey, each respondent was asked to

select their answers based on review score from 1 to 5, which

represent: Respondent Score (RS) 1: Very Low Importance,

RS 2: Low Importance, RS 3: Medium Importance, RS 4:

High Importance, and RS 5: Very High Importance.

analytics’. Based on extensive review and analysis of the

relevant literature, four related models were found, as

follow:

M1: Technology, Organisation, Social, Economy (TOSE)

Framework (Vugrin et al., 2010) [22]

M2: The PEOPLES Framework (Renschler et al., 2010) [23]

M3: Disaster Press & Release Model (Hai & Smyth, 2012)

[24]

M4: Complex Adaptive System (CAS) theory (Coetzee, Van

Niekerk, & Raju, 2016) [25]

Table 1 highlights the findings of recent studies, which

consist of dimensions (how the study view the DRM/DRR

solution), the theory used in the study, and key elements

(what are the associated factors/items). Based

the

comparison of the models and the theories and frameworks

presented, two gaps were identified. Firstly, each model,

framework, and theory has a different dimension in

measuring DRM and DRR. Secondly, the key elements

extracted from all models have no consistency. Therefore,

this study captures all the identified key elements and

dimensions that contribute to DRR and expands them by

identifying the related datasets in order to formulate the big

data analytics solution. Table 2 presents the proposed BDA

in DRR dimensions and key elements.

ECOLOGY

[EY1-EY3]

TECHNOLOGY

TY1-TY2]

ECONOMIC

[EC1-EC4]

[

BIG DATA

ANALYTICS

MODEL FOR

DISASTER RISK

REDUCTION

Table 2: The identified dimensions and key elements for

Big Data Analytics in Disaster Risk Reduction

Category

Key Element(s)

Technology

Information system management

SOCIAL

SL1-SL4]

ENVIRONMENT

[ET1-ET9]

[

Organisation Disaster emergency management

Training

Leadership experience

Facilities (housing, commercial facilities, and

ORGANISATION

[ON1-ON6]

cultural facilities)

Lifeline (food supply, health care, utilities,

transportation, and communication networks)

Political influence

Figure 1: Big Data Analytics Model for Disaster Risk Reduction

Social

Community/social vulnerability

The functionality of population and

demographics

It was found out that all key elements and datasets were

rated as important; with value starting from 0.80. Based on

the mean scores of all six dimensions, Organisation has

received the highest score and first ranking with the average

RII score of 0.895; thus, it was considered as the most

important component in this model. Meanwhile the second

most important component was Social with the RII score of

Cultural values

Community and social support

The ability of the ecological system to return

to or near its pre-event state

Economic process and activity

Resource allocation

Ecology

Economic

.882. Both Technology and Ecology components received

a score in-between range of mean value RII 0.87. The lowest

mean RII score was for Environment (RII=0.853) and

Economic (RII=0.837). The summary of all six dimensions

ranking, components, and influence for the big data model is

presented in Table 4.

Infrastructure environment

.2 Focus Group Discussion Results

Findings from FGD resulted in 59 datasets related to

DRM/DRR. These findings are results of the consolidation

Journal of Environmental Treatment Techniques

2020, Volume 8, Issue 1, Pages: 418-41ꢀ

Table 3: The identified datasets for Big Data Analytics in Disaster Risk Reduction

Dimension

Ecology

Dataset code

Dataset Description

EY-1

Ecological system to return to or near its pre-event state

Ecology

EY-2

EY-3

Relocation and Evacuation centre

Zoning Map

Economic

EC-1

EC-2

Infrastructure Asset Value (Building, etc.)

Land Production Value

Economic

EC-3

EC-4

ET-1

Land Use (Economic process and activity)

Resource allocation for Disaster Risk Management Activity

Aerial imagery

Environment

ET-2

ET-3

ET-4

ET-5

ET-6

ET-7

ET-8

ET-9

Air pollution index

Bathymetry

10.

11.

12.

13.

14.

15.

16.

17.

18.

19.

20.

21.

22.

23.

24.

25.

26.

27.

28.

29.

30.

31.

32.

33.

34.

35.

36.

37.

38.

39.

40.

41.

42.

43.

44.

Coordinates of Rainfall (RF) & Water Level (WL) Stations

Cross Section Data

Dam and water supply monitoring

Flood Extent Map due to sea level rise

Flood Extent Map for river

Flood Hazard Map

Flood protection measures

ET-10

ET-11

ET-12

ET-13

ET-14

ET-15

ET-16

ET-17

ET-18

ET-19

ET-20

ET-21

ET-22

ET-23

ET-24

ET-25

ET-26

ET-27

ET-28

ET-29

ET-30

ET-31

ET-32

ET-33

ET-34

ET-35

ET-36

ET-37

Flood, river level, and rainfall monitoring

High and low tide forecasting

Historical Flood Report

Historical records of hazard events

Hydrological gauge data

Inundation Map

Mean wind & max wind in 3 areas: KLIA, Subang, and Petaling

Meteorological gauge data

Nearshore tsunami wave height

Peta Bahaya dan Risiko Cerun (PBRC)

Radar & Weather Monitoring

Rainfall (RF) Data daily

Rainfall at Dam Data

Regional haze and hotspots

Report of Port Klang Sea Level Rise Research

River Basin Map in Shapefiles

Road access

Satellite – Flood Monitoring

Satellite image of hazard location (hazard map)

Slope data

Soil Type

Storm surge gauge data

Stream Flow Data

Topography Map (LiDAR, IFSAR)

Water Level (WL) Data daily

Watershed boundaries

Wind rose summary at KLIA, Subang, and Petaling

Disaster emergency management (management of disaster preparedness, response,

Organisation

ON-1

ON-2

mitigation, and recovery process)

6. Disaster Management Leadership Experience (the disaster management experience and

skill obtained by the organisation’s leaders/top management)

Organisation

ON-3

ON-4

47.

48.

Disaster Management Training (training in disaster management)

Facilities organisation (housing, commercial facilities, and cultural facilities)

Lifeline cycle (food supply, health care, utilities, transportation, and communication

networks)

Organisation

ON-5

ON-6

Political influence (Trust in politicians and satisfaction with the Government in

managing the disaster)

Social

Technology

SL-1

SL-2

SL-3

SL-4

TY-1

TY-2

TY-3

TY-4

TY-5

51.

52.

53.

54.

55.

56.

57.

58.

59.

Community and social support (citizen trust, cooperation)

Community and social risk acceptance (citizen level of risk resilience)

Disaster Relocation and Evacuation Centre

Population and demographics of the disaster area

Tech Technology Capacity (durability, the efficiency of machine processing)

Technology Infrastructure (no machines, specifications, storage)

Big Data Analytics Solution for Disaster Management

Disaster Management Metamodel and Metadata

Disaster Management, Knowledge Management, and Data Exchange Platform

Journal of Environmental Treatment Techniques

2020, Volume 8, Issue 1, Pages: 418-41ꢀ

Table 4: Mean value and ranking of dimension in big data

analytics model for disaster risk reduction

References

Mayner L, Arbon P. Defining disaster: The need for

harmonisation of terminology. Australasian Journal of Disaster

Key

Average of Relative

Ranking

Component

Importance Index (0 ≤ RII 1)

Trauma Studies. 2015;19.

Organisation

Social

Technology

Ecology

Environment

Economic

0.895

0.882

0.876

0.874

0.853

0.837

2. UNISDR U, editor Sendai framework for disaster risk reduction

2015–2030. Proceedings of the 3rd United Nations World

Conference on DRR, Sendai, Japan; 2015.

3. Vassakis K, Petrakis E, Kopanakis I. Big data analytics:

applications, prospects and challenges. Mobile Big Data:

Springer; 2018. p. 3-20.

Pappas IO, Mikalef P, Giannakos MN, Krogstie J, Lekakos G.

Big data and business analytics ecosystems: paving the way

towards digital transformation and sustainable societies.

Springer; 2018.

Conclusion

The study successfully described the formulation of

BDA for DRR model within the scope of Selangor State,

Malaysia. From an extensive literature analysis and an FGD

session with all the relevant agencies that responded with the

DRM/DRR, it is believed that this model is able to assist

further work in development of data analytics solution. The

model covers almost all angles of disaster-related aspects

such as ecology, economic, environment, organization,

social, and technology. It also goes in-depth by identifying

the related datasets both in spatial and nonspatial form. The

next step will be the evaluation of the model functionality in

the disaster-related case study environment. Apart from the

aim to formulate a big data analytics model for DRR, this

discovery also will be useful in developing strategies and

plans for DRR for Selangor State and the agencies itself.

Enormous growth of data has the ability and potential to

improve understanding of the historical information to shape

a better-informed future in the context of visualising the

story, identifying related cost incurred, together with new

connections and opportunities for DRR. The incorporation

of Big Data into DRR contributes to a more sustainably

developed society, economy, and nation.

5. Sukowati P, Nelwan V. Role of the Regional Bureaucracy of

East Java Province in Natural Disaster Management Policy

Integrative Based on Community. Journal of Environmental

Treatment Techniques. 2019;7(4):730-6.

Waheed AB, Ali A. Disaster Response and Potentials of Social

Capital. Journal of Environmental Treatment Techniques.

017;5(4):114-7.

Sarvari PA, Nozari M, Khadraoui D. The Potential of Data

Analytics in Disaster Management. Industrial Engineering in

the Big Data Era: Springer; 2019. p. 335-48.

8. Cumbane SP, Gidófalvi G. Review of Big Data and Processing

Frameworks for Disaster Response Applications. ISPRS

International Journal of Geo-Information. 2019;8(9):387.

Boakye J, Gardoni P, Murphy C. Using opportunities in big data

analytics to more accurately predict societal consequences of

natural disasters. Civil Engineering and Environmental

Systems. 2019:1-15.

10. Arslan M, Roxin A-M, Cruz C, Ginhac D, editors. A Review on

Applications of Big Data for Disaster Management. 2017 13th

International Conference on Signal-Image Technology

Internet-Based Systems (SITIS); 2017: IEEE.

1. Li W, Song M, Zhou B, Cao K, Gao S. Performance

improvement techniques for geospatial web services in a

cyberinfrastructure environment–A case study with a disaster

management portal. Computers, Environment and Urban

Systems. 2015;54:314-25.

Acknowledgement

Hereby, we declare a highest appreciation to the

Selangor State Disaster Management Unit and all Malaysian

Public Sector agencies that involved in this research. The

authors also thank following reviewers to review this article.

The research is financially supported by Universiti

Teknologi Malaysia under Q.K130000.3556.06G26.

12. Abdullah MF, Ibrahim M, Zulkifli H, editors. Big Data

Analytics Framework for Natural Disaster Management in

Malaysia. International Conference on Internet of Things, Big

Data and Security; 2017: SCITEPRESS.

3. Reba M, Roslan N, Syafiuddin A, Hashim M, editors.

Evaluation of empirical radar rainfall model during the massive

flood in Malaysia. 2016 IEEE International Geoscience and

Remote Sensing Symposium (IGARSS); 2016: IEEE.

4. Usman A, editor Integrated disaster risk management in Indian

environment: Prediction, prevention and preparedness. 2017

IEEE Global Humanitarian Technology Conference (GHTC);

Ethical issue

Authors are aware of, and comply with, best practice in

publication ethics specifically with regard to authorship

017: IEEE.

(avoidance of guest authorship), dual submission,

5. Fernández A, Carmona CJ, del Jesus MJ, Herrera F. A view on

fuzzy systems for big data: progress and opportunities.

International Journal of Computational Intelligence Systems.

2016;9(sup1):69-80.

manipulation of figures, competing interests and compliance

with policies on research ethics. Authors adhere to

publication requirements that submitted work is original and

has not been published elsewhere in any language.

6. Akter S, Wamba SF. Big data and disaster management: a

systematic review and agenda for future research. Annals of

Operations Research. 2017:1-21.

7. Zobel CW, Khansa L. Characterizing multi-event disaster

resilience. Computers & Operations Research. 2014;42:83-94.

Competing interests

The authors declare that there is no conflict of interest

that would prejudice the impartiality of this scientific work.

18. Emmanouil D, Nikolaos D, editors. Big data analytics in

prevention, preparedness, response and recovery in crisis and

disaster management. The 18th International Conference on

Circuits, Systems, Communications and Computers (CSCC

Authors’ contribution

All authors of this study have a complete contribution

for data collection, data analyses and manuscript writing

015), Recent Advances in Computer Engineering Series; 2015.

Journal of Environmental Treatment Techniques

2020, Volume 8, Issue 1, Pages: 418-41ꢀ

9. Papadopoulos T, Gunasekaran A, Dubey R, Altay N, Childe SJ,

Fosso-Wamba S. The role of Big Data in explaining disaster

resilience in supply chains for sustainability. Journal of Cleaner

Production. 2017;142:1108-18.

0. Masood T, So E, McFarlane D, editors. Disaster Management

Operations–Big Data Analytics to Resilient Supply Networks.

Proceedings of the 24th EurOMA Conference; 2017.

1. Shah SA, Seker DZ, Rathore MM, Hameed S, Yahia SB,

Draheim D. Towards Disaster Resilient Smart Cities: Can

Internet of Things and Big Data Analytics Be the Game

Changers? IEEE Access. 2019;7:91885-903.

2. Vugrin ED, Warren DE, Ehlen MA, Camphouse RC. A

framework for assessing the resilience of infrastructure and

economic systems.

Sustainable and resilient critical

infrastructure systems: Springer; 2010. p. 77-116.

3. Renschler CS, Frazier AE, Arendt LA, Cimellaro GP, Reinhorn

AM, Bruneau M. A framework for defining and measuring

resilience at the community scale: The PEOPLES resilience

framework: MCEER Buffalo; 2010.

4. Smyth I, Hai VM. The disaster crunch model: guidelines for a

gendered approach. 2012.

5. Coetzee C, Van Niekerk D, Raju E. Disaster resilience and

complex adaptive systems theory: Finding common grounds for

risk reduction. Disaster Prevention and Management.

016;25(2):196-211.