Background: Climate change is a significant factor shaping the planet and changing the pattern of disasters which leads to direct and indirect consequences. The result is a huge amount of affected people who rely on humanitarian aid. The satisfaction of this need is the responsibility of disaster management. Only little research about the relation of disaster management and climate change was done so far but would be of utmost importance as climate change is one main obstacle for efficient humanitarian work and disaster management design, in return, affects the resilience and vulnerability of disaster-prone areas. Purpose: This thesis paper investigates the interconnectedness of climate change and disaster management. It has the purpose to explore how humanitarian actors in the scientific and operational sector of disaster management experience the impact of climate change and which preventive adaptation strategies they identify to cope with climate-related challenges. Method: The methodology is based on a relativistic ontology and follows social constructionism as epistemology. A multiple case study within the scope of a qualitative inductive approach was conducted by contrasting scientific and operational experts’ opinions about the role of climate change in the disaster management context. Primary data were gathered in the form of semi-structured interviews by applying the typical case sampling. The selected method of data analysis is the content analysis approach. Conclusion: The results show that climate change consequences can be determined as a highly relevant factor shaping disaster management by intensifying general disaster management challenges. To adjust to this development, adaptation strategies have to be established and should follow a holistic approach. The main adaptation strategies identified are localization, forecast-based financing and superior data analysis in combination with enhanced information management showing major effects if applied within prevention and preparedness. Restricting factors in adaptation are lacking resources, coordination and communication problems and an insufficient flexibility level of systems and tools. Technology application, data analysis and forecasting, as well as lessons learnt instead can be seen as facilitating factors to overcome the challenges and barriers.

In February 2022 the Intergovernmental panel on climate change (IPCC) published a report indicating that wildfires may become more frequent as climate changes keeps progressing. The last few years we have witnessed devastating consequences due to wildfires around the world. In 2014 Sweden was affected by one of the biggest wildfires in modern history when a total of 15 000 hectares burned down. This study investigates how municipalities in Sweden work with crisis management against wildfires. Data was collected in a questionnaire sent out to municipalities as a web-survey. The questionnaire included questions about the municipality´s ability to handle a higher frequency of wildfires and if their risk and vulnerability analysis includes wildfires. The overall response rate was 52 % (151/290) and more than half of the municipalities answered that wildfires are included in their risk and vulnerability analysis, and they think they can manage a higher frequency of wildfires. It was not possible to identify a difference in crisis management between small (<16 000) and large (>16 000) municipalities, and municipalities located in the southern and northern parts of Sweden, respectively. What can be seen as worrying is that there are municipalities that do not have the capacity to handle an increase in wildfires. Economics are named one of the main reasons municipalities do not prioritize these issues. Due to the development with rising temperatures and more frequent wildfires in the world, forest fires will probably be a topical research area onwards.

Recently, disaster scene description and indexing challenges attract the attention of researchers. In this dissertation, we solve a disaster-related multi-labeling task using a newly developed Low Altitude Disaster Imagery dataset. In the first task, we realize video content by selecting a set of summary key frames to represent the video sequence. Through inter-frame differences, the key frames are generated. The key frame extraction of disaster-related video clips is a powerful tool that can efficiently convert video data into image-level data, reduce the requirements for the extraction environment and improve the applicable environment. In the second, we propose a novel application of using deep learning methods on low altitude disaster video feature recognition. Supervised learning-based deep-learning approaches are effective in disaster-related features recognition via foreground object detection and background classification. Performed dataset validation, our model generalized well and improved performance by optimizing the YOLOv3 model and combining it with Resnet50. The comprehensive models showed more efficient and effective than those in prior published works. In the third task, we optimize the whole scene labeling classification by pruning the lightweight model MobileNetV3, which shows superior generalizability and can disaster features recognition from a disaster-related dataset be accomplished efficiently to assist disaster recovery.

Doctor Educationis / President Cyril Ramaphosa first addressed the nation regarding the Covid-19 pandemic on the 15th of March 2020, when he announced the national state of disaster in terms of the Disaster Management Act (DMA) to prevent the rise of Covid-19 infections.1 On the 23rd of March 2020 in his second address, the President announced a national Lockdown, initially for 21 days, and outlined more stringent interventions to limit the transmission of the coronavirus and to mitigate its economic and social impact.2 Since then, there have been at least four sets of directions issued to the provinces and municipalities in terms of the Disaster Management Act, with the first set of directions to provinces and municipalities taking effect on the 25th of March 2020. The purpose of the directions issued to municipalities were to direct municipalities in respect of the following matters in response to Covid-19 namely, the provision of water and sanitation services, hygiene education, communication, and awareness. For the purposes of this research, the word coronavirus will be used interchangeably with Covid-19. The first set of directions titled ‘COGTA COVID-19 Disaster Response Directions 2020’ took effect on the 25th of March 2020.3 The directions stipulated that no council meetings outside the District Command Centre meetings may be undertaken during the initial 21 Day Lockdown period, or any other extended period that may be declared. Since then, there have been amendments to these directions. The directions which commenced on the 3rd of July 2020, as far as local governance is concerned, stated that municipalities and municipal entities could convene council meetings.

Law enforcement needs simulation practice tailored to de-escalation skills. Law enforcement officers spend most of their training time practicing how to use force appropriately and very little time practicing how to avoid using force. There is little research into the best ways for law enforcement to reduce their use of force and the best ways to provide training to individuals to coach them to avoid using force. There are many training programs promoting de-escalation techniques, but there is little research into the effectiveness of these programs beyond the ability of individuals to perform the skills that are trained. There is little to show that using these skills reduces crime, reduces the need for incarceration, reduces the level of violence, or improves the communities where they are used. The scope of this project addresses a small part of this gap by examining different ways simulators can be used to provide practice in the skills that are taught. This dissertation contributes to the field of simulation by demonstrating how virtual reality can address deficits in law enforcement training. It does so by studying which techniques are most appropriate in some scenarios and how to better train officers to use them. This project looks at different ways of allowing police officers to practice de-escalation skills to see if these have any bearing on an officer's approach to de-escalation and if the officer responds positively to the practice. This research does not attempt to take the next step of measuring the use of these skills outside the training environment. The results indicate active-duty officers have a positive response to any attempt to practice or promote de-escalation and are especially positive about the potential for training in realistic, situationally appropriate virtual environments.

The objective of study is to identify and evaluate the role of the ecotourism business in building community resilience in disaster-vulnerable areas. This dissertation analyzed how an ecotourism business is being managed and examined what contributions the ecotourism business has made to community resiliency in disaster vulnerable areas. In the process of these examination, policies in disaster management, tourism (general policies) and ecotourism in the Philippines has been reviewed. Furthermore, case studies have been examined, a community and a government managed ecotourism business. The cases were analyzed using the "Resilience Indicators for Vulnerable Communities Engaging in Ecotourism." / 博士(グローバル社会研究) / Doctor of Philosophy in Global Society Studies / 同志社大学 / Doshisha University

The primary objective of this dissertation was to seek an answer to the question of how agricultural insurance can potentially be an effective and efficient coping mechanism so that the poorest of the poor in isolated rural areas can avoid falling into the poverty trap amid rising global natural disasters in the most exposed region of East Asia and the Pacific. Observations in the field were conducted to investigate the disaster experiences and characterization, coping strategies, but the main focus was on farmer experience on agricultural insurance in two types of elevation (lowland and upland) and program implementation of the insurance providers in the developed country of Japan and the Philippines as a representation of the developing world. / 博士(現代アジア研究) / Doctor of Philosophy in Contemporary Asian Studies / 同志社大学 / Doshisha University

Computer vision technologies are rapidly improving and becoming more important in disaster response. The majority of disaster description techniques now focus either on identify objects or categorize disasters. In this study, we trained multiple deep neural networks on low-altitude imagery with highly imbalanced and noisy labels. We utilize labeled images from the LADI dataset to formulate a solution for general problem in disaster classification and object detection. Our research integrated and developed multiple deep learning models that does the object detection task as well as the disaster scene classification task. Our solution is competitive in the TRECVID Disaster Scene Description and Indexing (DSDI) task, demonstrating that it is comparable to other suggested approaches in retrieving disaster-related video clips.

Yes / Social media is a platform to express one’s view in real time. This real time nature of social media makes it an attractive tool for disaster management, as both victims and officials can put their problems and solutions at the same place in real time. We investigate the Twitter post in a flood related disaster and propose an algorithm to identify victims asking for help. The developed system takes tweets as inputs and categorizes them into high or low priority tweets. User location of high priority tweets with no location information is predicted based on historical locations of the users using the Markov model. The system is working well, with its classification accuracy of 81%, and location prediction accuracy of 87%. The present system can be extended for use in other natural disaster situations, such as earthquake, tsunami, etc., as well as man-made disasters such as riots, terrorist attacks etc. The present system is first of its kind, aimed at helping victims during disasters based on their tweets.

Yes / The purpose of this research is to establish the necessary and sufficient conditions for food safety and security during pandemic outbreaks, focusing on the case of COVID-19 to ensure resilience of the food supply chain. The study emphasises on the complexity theory of fuzzy set Qualitative Comparative Analysis (fsQCA), to establish a result-driven definition of Industry 5.0 (I5.0) during and post pandemics. The results of this study revealed that a combination of conditions derived from pandemic policy related reforms and I5.0 enablers will assist manufacturers and suppliers in establishing food safety and security during and post the COVID-19 era in a developing economy. Food safety and security being the goal, based on a survey of 140 food companies, this study provides insights to manufacturers and policymakers to enable selective implementation of I5.0 enabling technologies and pandemic policies.