Journal of Environmental Treatment Techniques 2020, Volume 8, Issue 1, Pages: 18 4 - 18 4 481 A Formulation of Big Data Analytics Model in Strengthening the Disaster Risk Reduction Syamil Zayid 1 , Nur Azaliah Abu Bakar 2* , Mageshwari Valachamy 3 , Nur Shuhada Abdul Malek 4 , Suraya Yaacob 5 , Noor Hafizah Hassan 6 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 1 Introduction 1 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 Corresponding author: Nur Azaliah Abu Bakar, Razak Faculty of Technology and Informatics, Universiti Teknologi Malaysia, 54100 Kuala Lumpur, Malaysia. E-mail: azaliah@utm.my. 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]. 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 Journal web link: http://www.jett.dormaj.com J. Environ. Treat. Tech. ISSN: 2309-1185