The coastal areas of the Tohoku region were severely damaged by the Great East Japan Earthquake and tsunami, resulting in a large-scale project to relocate residential areas to higher ground. However, this project required a large amount of human resources. At the same time, other projects of the same scale were being planned in other areas. This resulted in a shortage of human resources, and concerns about the prolonged construction period and the resulting population exodus. Under these circumstances, the earthquake restoration project in Onagawa Town, Miyagi Prefecture, was completed earlier than in other areas. However, there has been no research on the decision-making process of construction workers, which was the cause of this success. In this study, we conducted an interview survey construction workers involved in the restoration work and classified the survey results into categories based on an inductive approach. The structure of the decision-making process was clarified as a tree structure, and a new decision-making support model was developed.

 Among the various forms of accidents that occur in construction work, crashes and falls account for the largest percentage. In this study, we attempted to identify the balance state during walking from the measured plantar pressure data that were measured the changes in plantar pressure during balanced and unbalanced walking condition using plantar pressure sensors. Specifically, we proposed a method to convert plantar pressure data during linear walking under three different walking conditions into a feature vector during a walking cycle. Furthermore, we proposed a method to identify three types of walking conditions using a k nearest neighbor model on the transformed feature vectors. The identification results using our proposed method showed that balanced and unbalanced walking condition could be recognized with high accuracy.

 In recent years, the impacts and damages caused by localized heavy rainfall associated with climate change have been reported, highlighting the importance of appropriate weather information sharing. Addressing these issues is an urgent task for national resilience, requiring suitable measures in road traffic and business operations. Meanwhile, the promotion of digital transformation (DX) using digital technologies such as virtual spaces is anticipated. This study identifies the challenges in disseminating weather information to vehicle drivers to ensure safe and smooth traffic flow using digital technology. Specifically, it examines the understanding of rainfall intensity defined by the Japan Meteorological Agency through questionnaire interviews with participants. Furthermore, the study investigates the impact of different environmental conditions, such as rainfall, on vehicle drivers using driving simulations with BIM/CIM models constructed during road design. It also considers future speed regulations during rainfall. The results using BIM/CIM suggest the importance of setting speed regulations that take into account differences in rainfall and other driving environmental conditions.

 Manufacturing in Japan is largely dependent on small and medium-sized factories located in urban areas. However, in recent years, many small and medium-sized factories have been closed due to the reason such as the ageing of their owners. In some cases, former factory sites are used for housing, mainly condominiums for single people, and as a result, a mixed residential-industrial problem has emerged. It is significance to investigete the urban development considering the resistant to flood disasters and capable of sustainable development, while maintaining the regional characteristics of manufacturing. In this study, we proposed a method of solving the problem by land readjustment projects based on an urban planning method, taking into consideration the revitalisation of small and medium-sized factories, the mixed residential-industrial problem, and measures to inundation due to flood. The method was applied to the Ota Ward area of Tokyo and its validity was examined.

 In the 20th century, management on the production site has been fragmented in the order of productivity, quality, safety and reliability. As a result, today, safety alone is no longer sufficient to protect safety, and integrated management is necessary. Hollnagel, who believed that unified management was necessary, proposed Synesis as a way to create safety rather than analyze it.The authors have proposed the use of FRAM for safety management, but in recent years, accidents in the construction industry have occurred that cannot be prevented by safety management alone. It is necessary to maintain reliability in quality, cost, delivery and safety, thereby ensuring productivity and safety, and to manage them in an integrated manner. This study illustrates risk assessment using quality, cost, process, and safety management to understand the effects of variability in FRAM. This approach makes it easier to identify indirect risks related to safety.

 During the "Heavy Rain in July 2020," large-scale flooding struck Hitoyoshi City in Kumamoto Prefecture, causing damage to approximately 19.2% of all households. Due to concerns about the spread of COVID-19 in evacuation shelters, it was decided to use accommodation facilities as alternative evacuation sites. However, many accommodation facilities in Hitoyoshi City were also damaged, leaving only two facilities capable of accepting evacuees. In response, Kumamoto Prefecture and Hitoyoshi City funded emergency repairs for several damaged accommodation facilities, allowing them to be used as evacuation shelters. This study aims to provide an overview of these emergency repair efforts, focusing on the perspectives of government agencies, accommodation facility operators, and evacuees. The findings reveal that while the actual number of evacuees using the accommodation facilities was lower than expected, these facilities nonetheless provided a favorable evacuation environment. Moreover, accommodation facilities that underwent emergency repairs and accepted evacuees benefited from secondary effects such as early business recovery and operational continuity. The prompt recovery and continued operation of these facilities contribute to the long-term revitalization of the region. Therefore, raising awareness and promoting the use of accommodation facilities as evacuation shelters in disaster scenarios is recommended.

 The purpose of this study is to summarize the points to be considered when formulating a child-care continuity plan for certified child-care centers, using a certified child center in Toride City, Ibaraki Prefecture, which was damaged by a heavy rain caused by a rainy front and Typhoon No. 2 in 2023, as a case study. Interviews were conducted with the affected certified child center, the local government division in charge of childcare, and three kindergarten and daycare centers that had accepted the children. The affected preschools had secured alternative facilities and dispersed their childcare services among the three locations. When childcare was resumed, priority was given to children with high childcare needs, and even within the same certified child center, some children were able to resume attending certified child center early while others were not, depending on the working status of their parents. In addition, securing an alternative facility to accommodate many children is an issue, and a cooperative system within the community is needed to enable the acceptance of children.

 In this study, we predicted river embankment damage due to liquefaction on the Kuzuryu River and Hino River, based on the assumed results of a large scale earthquake caused by the eastern margin active fault zone of the Fukui Plain. In addition, the impact of the river embankment damage on flood inundation was investigated. As a result of calculating the amount of embankment subsidence at boring points around the river embankment, the maximum amount of subsidence was 3.68 m. Furthermore, we conducted a flood analysis before and after the embankment subsidence, and compared the differences in the time series changes in the inundation area and inundation depth.

 The purpose of this study was to clarify the influence of disaster traditions on local residents’ disaster awareness and preparedness. The Okoshi and Asahi districts in Ichinomiya City, Aichi Prefecture, which are in the Kiso River area, were the subject areas. A questionnaire survey revealed a significant difference in that the lessons from past disasters were more easily transmitted and the disaster consciousness was also higher in the Okoshi district, where a natural disaster lore monument related to disaster lore remained, than in the Asahi district where only the lore remained. On the other hand, it cannot be said that the disaster preparations of the local inhabitants were sufficient regardless of the existence of a natural disaster tradition monument. It was also shown that local residents have a high expectation for disaster prevention education in schools using the method of passing on disaster lore. Disaster lore is effective as a motivation to learn the history of local disasters, and it needs to be utilized in combination with disaster mechanisms and countermeasures.

 In recent years, damage caused by heavy snow has been seen in various places. Various plans for road maintenance work such as snow removal work are currently being formulated based on Disaster Prevention Weather Information announced by the Meteorological Agency and based on the rules of thumb of experienced engineers. However, Disaster Prevention Weather Information covers a wide area and is strictly a forecast. Even with the empirical rules of experienced engineers, it is difficult to estimate the condition of the road surface at key monitoring points. If the road surface temperature can be predicted with a high degree of accuracy, the optimum antifreeze spray time will become clear, leading to the prevention of slip accidents. For the purpose of optimizing the application timing of road deicing agents, data obtained from IoT sensors on roads that are intensively monitored by road administrators and Disaster Prevention Weather Information Data published daily are combined and used for predicting road surface temperature up to 26 hours ahead. And we aim to improve the accuracy of the learning model. We construct Feed-forward Neural Network and Recurrent Neural Network to confirm the prediction accuracy of the road surface temperature. And we also confirm the effect of ensemble learning. As a result, it was found that the air temperature and wind speed have an effect on the prediction of the road surface temperature. In the Feed-forward Neural Network, the combination of road surface temperature, air temperature, wind speed, and Disaster Prevention Weather Information Data resulted in the prediction with the smallest error. In the Recurrent Neural Network, the prediction with the smallest error was obtained when using GRU. It was found that the prediction accuracy can be improved by taking the average of both Neural Network models.

 The purpose of this study is to propose a flood risk assessment method that takes into account the likelihood of flooding and to apply for the Kamanashi River flowing south in western Yamanashi Prefecture. For this purpose, each inundation category's number of inundation events was calculated for each mesh based on the data from the inundation simulation for each breach point, which is used to create a map of the expected flood inundation area. A risk assessment method was also developed using the expected value of damage to buildings caused by flooding as an indicator, and a flood damage risk assessment was conducted for the target area. Finally, using the developed risk assessment method, we analyzed the changes in flood risk over time in the flood-prone areas of the Kamanashi River.

 As a result of the analysis, it was confirmed that differences in inundation frequency in the target area can be visualized, and differences in risk due to differences in inundation frequency can be expressed by using the concept of expected value. The analysis also revealed the possibility that development may have occurred on land with high inundation frequency between 2000 and 2005 in the subject area.

 Amid fears of more severe disasters due to climate change, wide-area evacuation for people to leave the city in the event of river flooding is being discussed. In this study, through a wide-area evacuation workshop conducted for residents of Settsu City, Osaka Prefecture, where the necessity of wide-area evacuation has been pointed out, we analyzed factors effective in changing residents' awareness toward wide-area evacuation. In the workshop, we examined appropriate wide-area evacuation for the participants by using the “wide-area evacuation drill,” which consists of three steps: (1) prior awareness （before information confirmation）, (2) risk information （confirmation of risk information）, and (3) post awareness (after information confirmation). From the results of the wide-area evacuation drill and the questionnaire survey, it was confirmed that the process of confirming and comparing one's own prior awareness and correct risk information through the wide-area evacuation chart fosters awareness of early wide-area evacuation, and that the time factor is effective in changing residents' awareness of wide-area evacuation.

 Local Normalization Plan system was established in 2014. In late years the plan and the need of the cooperation of the disaster prevention are advocated. Particularly, heavy damage occurs by the tsunami disaster. And development of Pre-Disaster Recovery Plans advances recently in the local government in Wakayama prefecture. And the restoration after the damage and an approach for recovery advance. However, it can protect life and the property of inhabitants if it takes measures of the residence move before a damage. Furthermore, it can push forward restoration and the revival after the damage quickly, too. Therefore, in this study, it clarifies a method of resizing of urbanized area which can secure security and the population density of inhabitants depending on a tsunami inundation risk. And it gives tsunami measures of Pre-Disaster Recovery Plan and Local Normalization Plan a suggestion.

 In the context of pedestrian evacuation on sidewalks in the central areas of large cities, we estimated the risk levels of crowd accidents under different crowd flow patterns and environmental conditions using simulations. By proposing a new index called the Risk Level Index, which converts the discrete risk levels into continuous values, we were able to compare the risk levels of crowds under different flow patterns and environmental conditions. We found that counterflows, crossflows, and mixed flows, compared to unidirectional flows, can lead to dangerous situations where crowd movement suddenly halts even under conditions with lower Risk Level Index values.

 The Ministry of Land, Infrastructure, Transport and Tourism has determined that there is a risk of damage to many residents on steep slopes with a slope of 30 degrees or more that are likely to collapse, and that there is a need for certain action restrictions to prevent the risk of facilitating or inducing collapse. Areas of land with a steep slope are designated as steep slope warning areas.Since it takes a huge amount of labor and cost to conduct a basic survey for designation, efficiency and automation are expected.In this research, cost reduction is expected. The purpose of this study is to extract steep slope warning areas using deep learning techniques for the purpose of improving work efficiency. A dropout layer was added to the model to prevent overfitting. For data divided into arbitrary image sizes, standardization preprocessing was added, and the accuracy improved to 80%.

 Water supply is a lifeline essential to our lives, and it is essential to ensure the sustainability of a stable supply of safe water. For this reason, early recovery from disasters is necessary, and the promotion of advance measures such as BCP (business continuity plan) is essential. In this study, we focused on the water supply in Hyogo Prefecture, particularly on water purification plants that supply safe water every day, and examined the impact and challenges of future natural disasters on water purification plants, the necessity of BCP for water supply, and the risk of damage from natural disasters based on the results of a questionnaire survey and spatial information analysis using GIS. The questionnaire survey revealed that countermeasures have not been implemented in some of these facilities. It was revealed that the main bottlenecks preventing the progress of countermeasures are a shortage of staff and securing a budget for costs. In addition, the results of spatial information analysis revealed that there are several water purification plants that are at risk of being affected by various natural disasters.