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The purpose of this paper is to use a theory-based approach to develop a new classification model for disasters that reflects their logistics implications, and to contextualise the findings by applying the model to a particular disaster situation.

A widespread literature review was conducted in order to conceptualise the proposed disaster classification model and a case study (the 2011-2012 Somali food crisis) was used to provide a practical illustration and an initial validation of the conceptual approach.

The new classification model proposes a set of four categories of disasters based on two generic dimensions, whilst simultaneously integrating five situational factors that reflect the impact of the external environment on the logistics operations. The case study confirms that this systemic approach is necessary since, from a logistics perspective, a disaster should be considered in its entirety and within its contextual environment.

Further research is needed to establish the operational characteristics of each disaster type in order to determine the applicability of business logistics practices to each scenario. In addition, this paper highlights the opportunity to validate or refine the model by using a more varied range of case studies.

This paper proposes a new classification model for disasters based on their logistics implications and, by integrating the key environmental factors, it moves beyond the traditional 2×2 model found in the literature.

The purpose of this paper is to propose a methodology for a priori classification of natural disasters that occur in Sri Lanka, through the development of a set of weighted parameters based on the product of the disaster impact and the affected area, in order to prepare mitigation plans.

Experts' opinions were used for developing the parameters. Through a facilitated workshop, the weights of the disasters were obtained from experts involved in disaster mitigation at the local, regional and national levels in Sri Lanka. A correlation analysis was used to determine the most appropriate independent measures of disaster impact and affected area, the product of which was used to rank the identified disasters for further action.

For the pre‐selection of major disasters, the study showcases four weighted parameters, one of which is identified as the best. In total, five disasters have been singled out for further consideration in Sri Lanka. The product of the affected area factor, based on administrative area classification, and the impact factor, out of the two considered, that places a higher weight on minor disasters, is shown to be the best criterion.

The geographical distribution of the participants (experts) does influence the results, and those available for the workshop were not fully representative of all Sri Lanka's provinces.

The paper emphasizes the importance of the consideration of the area impacted rather than the classification, which is based solely on the severity of the impact. The categorization of disasters based on experts' opinions and the related analysis revealed a priority order for planning for certain identified disasters.

The purpose of this study is to provide the location of natural disasters that are poured into maps by extracting Twitter data. The Twitter text is extracted by using named entity recognition (NER) with six classes hierarchy location in Indonesia. Moreover, the tweet then is classified into eight classes of natural disasters using the support vector machine (SVM). Overall, the system is able to classify tweet and mapping the position of the content tweet.

This research builds a model to map the geolocation of tweet data using NER. This research uses six classes of NER which is based on region Indonesia. This data is then classified into eight classes of natural disasters using the SVM.

Experiment results demonstrate that the proposed NER with six special classes based on the regional level in Indonesia is able to map the location of the disaster based on data Twitter. The results also show good performance in geocoding such as match rate, match score and match type. Moreover, with SVM, this study can also classify tweet into eight classes of types of natural disasters specifically for the Indonesian region, which originate from the tweets collected.

This study implements in Indonesia region.

(a)NER with six classes is used to create a location classification model with StanfordNER and ArcGIS tools. The use of six location classes is based on the Indonesia regional which has the large area. Hence, it has many levels in its regional location, such as province, district/city, sub-district, village, road and place names. (b) SVM is used to classify natural disasters. Classification of types of natural disasters is divided into eight: floods, earthquakes, landslides, tsunamis, hurricanes, forest fires, droughts and volcanic eruptions.

The purpose of this paper is to identify the present structure of the Korean disaster-safety industry and propose ways to promote it.

The background of the emergence of the disaster and safety industry in Korea is reviewed, and business networks of 91 companies belonging to the disaster and safety industry in Korea are identified and analysed through a network analysis.

Korean Government is struggling to nurture the disaster and safety industry as it has been on last few decades. This paper finds that the current industry has an ambiguous market structure, and the industry tends to depend highly on the demand of public institutions.

The concept of the disaster and safety industry is not well established in other countries except for Korea. If Korea successfully fosters its disaster and safety industry through its proper understanding, it would bring both disaster risk reduction and economic benefits.

This paper analyses the market structure of the disaster and safety industry, which is uniquely emerging in Korea and rarely designated as an industry in other countries.

The purpose of this paper is to study and review some major impacts of the disasters during the past 105 years and develop a new theoretical classification of disasters.

A detailed study of disasters in the world during the period (1900‐2005) has been obtained from the recent published sources. In that period more than 40 lessons have been reported based on statistical data analysis of disasters. Furthermore, a two‐dimensional probability density function is developed to categorize the different types of disasters. This paper studies and reviews some major impacts of disasters during the past 105 years and summarizes some major lessons for the future. Furthermore, a new scaling system is presented to determine the actual damage of disasters to human life.

There is no doubt that the impacts of future disasters will not be the same as previous ones but lessons from the past can be very helpful for improving one's knowledge about disasters and providing better response programs for local and international organizations. A new scaling system will also be a useful guide for the development and evaluation of national and international disaster planning, mitigation, and hazard reduction efforts.

Important lessons have been reported based on statistical data analysis of disasters. Moreover, a new classification of disasters is presented to relate the vulnerability factors of a society to the magnitude of the natural disasters.

This paper aims to design a vulnerability assessment model considering the multidimensional and systematic approach to disaster risk and vulnerability. This model serves to both risk mitigation and disaster preparedness phases of humanitarian logistics.

A survey of 27,218 households in Pueblo Rico and Dosquebradas was conducted to obtain information about disaster risk for landslides, floods and collapses. We adopted a cross entropy-based approach for the measure of disaster vulnerability (Kullback–Leibler divergence), and a maximum-entropy estimation for the reconstruction of risk a priori categorization (logistic regression). The capabilities approach of Sen supported theoretically our multidimensional assessment of disaster vulnerability.

Disaster vulnerability is shaped by economic, such as physical attributes of households, and health indicators, which are in specific morbidity indicators that seem to affect vulnerability outputs. Vulnerability is heterogeneous between communities/districts according to formal comparisons of Kullback–Leibler divergence. Nor social dimension, neither chronic illness indicators seem to shape vulnerability, at least for Pueblo Rico and Dosquebradas.

The results need a qualitative or case study validation at the community/district level.

We discuss how risk mitigation policies and disaster preparedness strategies can be driven by empirical results. For example, the type of stock to preposition can vary according to the disaster or the kind of alternative policies that can be formulated on the basis of the strong relationship between morbidity and disaster risk.

Entropy-based metrics are not widely used in humanitarian logistics literature, as well as empirical data-driven techniques.

The purpose of this paper is to construct a typology of a disaster that informs humanitarian-relief supply chain (HRSC) design across the stages of disaster relief.

In addition to an interdisciplinary review of pertinent literature, this paper utilises a typology construction method to propose theoretically and methodologically sound dimensions of disasters.

Whilst semantic arguments surrounding the concept of a “disaster” are ongoing, the authors propose three typologies based upon six dimensions that serve as interdependent variables informing resultant HRSC design considerations. These are speed of onset, time horizon, spatial considerations, affected population needs, perceived probability of occurrence and perceived magnitude of consequence. These combinational and independent relationships of the variables offer insight into key HRSC design-making considerations.

The study improves conceptual knowledge of disasters, distilling the concept to only the dimensions applicable to HRSC design, omitting other applications. The typologies provide empirical cell types based on extant literature, but do not apply the models towards new or future phenomena.

This paper provides HRSC practitioners with normative guidance through a more targeted approach to disaster relief, with a focus on the impacted system and resulting interactions’ correspondence to HRSC design.

This paper provides three typological models of disasters uniquely constructed for HRSC design across the various stages of disaster relief.

The purpose of this paper is to give an up‐to‐date and structured insight into the most recent literature on humanitarian logistics, and suggest trends for future research based on the gaps identified through structured content analysis.

The authors use a quantitative and qualitative content analysis process to analyse the characteristics of the existing literature, identifying the most studied topics in six structural dimensions, and presenting gaps and recommendations for further research.

It was found that existing humanitarian logistics research shows too little interest in continuous humanitarian aid operations, in slow onset disasters and man‐made catastrophes. While several papers address different phases of disasters, very few focus particularly on the reconstruction following a disaster. Empirical research is underrepresented in the existing literature as well.

While five of the authors’ structural dimensions are inspired by previous reviews, the sixth dimension (situational factors) is derived from a theoretical framework which the authors developed and which has never been tested before. The validity of the study could therefore be increased by testing this framework.

The authors analyse the broadest set of papers (174) ever covered in previous literature reviews on humanitarian logistics. A quantitative analysis of the papers was conducted in order to analyse the situational factors which have mostly been studied so far in literature. This paper is also the first in humanitarian logistics to use content analysis as the main methodology to analyse literature in a structured way, which is of particular value to the academic community as well as practitioners.

The term “causes” of disasters is used interchangeably very often with other terms, such as “types” of disasters. Also, causes of disasters are usually explained in the literature in an individualistic fashion relating every single disaster with its own causes only. This limits the ability to identify the real causes of disasters. Second, it reduces the ability to create any kind of grouping for the causes of disasters. The purpose of this paper is to identify and discuss causes of disasters in a more categorical manner.

An extensive review of the existing literature has been made in order to identify, introduce and discuss theories related to the causes of disasters. Overall, the approach taken is based on theoretical reasoning informed by the literature.

Disasters, even those which seem to be composite and complex, are not mysterious incidents that cannot be explained or analyzed. Literally, almost all disasters can be diagnosed and the causes of disasters can be identified in a systematic and reasonable manner.

This study provides a better conceptualization and understanding of the causes of disasters. It is believed that this study will improve the decision-making process accompanied with the disaster risk reduction processes by understanding the exact causes of disasters. The study also clarifies the differences between the causes, sources and types of disasters which is extremely significant in disaster lifecycle modeling.

To the author’s knowledge, very few attempts have been made in the literature to capture causes of disasters in a categorical and systematic manner. Almost all disaster occurrences have been discussed in the literature in an individualistic fashion relating every single disaster with its own causes only.

Disasters are classified as natural and man‐made disasters. Man‐made disaster, which occurs at the major hazard installation, is a technological disaster. For an incident to be classified as technological disaster criteria are required to be met. Several criteria have been proposed by researchers and agencies defining the disasters in terms of casualties, economic loss and environmental impact. Few models have been drawn describing the sequence of development of the technological disaster. This paper reviews in detail the proposed criteria, which define the disaster. The paper also summarizes the models which describe the disaster stages. Finally the paper summarizes a proposed model of the technological disaster precondition phase for major accidents.