Tsunamis generated by undersea earthquakes can cause severe damage. It is essential to quickly assess tsunami-damaged areas to take emergency measures. In this study, I employ deep learning and develop a model using aerial photographs and road segment data. I obtained data from the aerial photographs taken after the Great East Japan Earthquake; the deep learning model used was YOLOv5. The proposed method based on YOLOv5 can determine damaged roads from aerial pictures taken after a disaster. The feature of the proposed method is to use training data from images separated by a specific range and to distinguish the presence or absence of damage related to the tsunami. The results show that the proposed method is more accurate than a comparable traditional method, which is constructed by labeling and learning the damaged areas. The highest F1 score of the traditional method was 60~78%, while the highest F1 score of the proposed method was 72~83%. The traditional method could not detect locations where it is difficult to determine the damage status from aerial photographs, such as where houses are not completely damaged. However, the proposed method was able to detect them.

Disasters can lead to bridge collapses, road flooding, and mudslide blockages, disrupting the road network and hindering victims' movement and rescue operations. After a disaster, it is crucial to quickly assess the damage and identify access routes to affected areas to transport relief supplies and conduct rescue operations. Recent advances in digital transformation (DX) technology have the potential to revolutionize disaster management planning. Uncrewed Aerial Vehicles (UAVs) are being utilized to assess damage, while GPS data from mobile phones is helping to visualize evacuation behavior. The goal of this research is to enhance community resilience, minimize damage and enable rapid recovery by improving emergency response after a disaster. We propose a method for efficiently observing road damage after a disaster using population flow data derived from UAV and GPS data. The method focuses on assessing concentrated population areas, touring from one evacuation center to another based on the road network. We employ the Traveling Salesman Problem (TSP) and route search methods to calculate distances between points. The proposed model was applied to the Mabi city, Kurashiki City, Okayama Prefecture, which suffered significant damage during the July 2018 rainstorm. Results indicate that the proposed method can effectively gather data on road blockages in areas with higher population densities than static census data. Additionally, it found that investigating the flooding situation of roads at that time through land routes, such as vehicles, proved challenging because many routes connecting densely populated areas to evacuation centers were continuously flooded.

Background: Climate change has made the relationship between disaster and sustainable development more critical. Especially in developing countries, disasters frequently devastate local communities and hinder progress. Children are at a higher risk during catastrophes; however, their knowledge of disaster risk is limited. Education for disaster risk reduction can bring the necessary changes for a safe society. School disaster prevention education can raise awareness through lectures and games, but interactive dialogues between students and teachers can be more effective. Also, GPS tracking can be a valuable tool for understanding people’s behavior during disasters. Methodology: This study proposes a method to analyze the relationship between students’ daily behavior and their experiences of flood damage and preparedness for disaster risk. It is a simple method using questionnaires and GPS loggers and can be applied in mountainous areas in developing countries where equipment is inadequate. The proposed method will be used to understand the current state of disaster risk among children and identify disaster risk reduction issues. The study area lies in Paluntar Municipality, Gorkha district, Nepal. Forty-five students from two schools in the region participated in the study. Results: The results showed that students who commute to school in areas inundated by heavy rainfall in recent years were not adequately prepared for disasters, even though they had experienced flood damage in the past and were significantly more concerned about future harm. Our field observations and interviews of teacher-student relationships showed that students were very attentive to their teachers’ instruction. Teachers understand the dangers of heavy rainfall and how to prepare for it, but students need to learn how to deal with it. In other words, the school staff’s disaster education to the students effectively reduces disasters. Early warning is needed when severe floods are expected during the rainy season.

Understanding and reducing the risk of natural disasters is crucial for maintaining the economy and society. Since Japan is a disaster-prone country and the most advanced nation in the world regarding aging and population decline, it necessitates a natural disaster risk analysis that considers both challenges. This paper represents the first study to explore the risk of natural disasters, specifically for young females exposed to four types of events: floods, tsunamis, storm surges, and sediment disasters, particularly in municipalities at risk of vanishing. We identified municipalities with a high proportion of young females exposed to these natural disasters and examined them from a geographical perspective. Utilizing a spatial regression model, we statistically analyzed the factors influencing the proportion of the exposed population. The result showed that young females exposed to flooding tend to the areas with a higher proportion of habitable land, inland regions, and locations abundant in rivers and lakes. Conversely, young females affected by tsunamis tend to be in coastal areas with few buildings. Additionally, those exposed to storm surges are often in regions characterized by features such as rice fields and other facilities. In the case of landslides, young females tend to reside in areas where habitable land is scarce, such as coastal regions. Our findings suggest that municipalities with a risk of citywide flooding or storm surge should enhance countermeasures, such as implementing building regulations in high-hazard residential areas. Additionally, it recommends that municipalities with a risk of tsunami or sediment should relocate hazardous housing.

Learning from the Great East Japan Earthquake, the Japanese government enacted the “Tsunami Disaster Prevention Community Development Law.” Following the law’s enactment, local prefectures could designate areas that required special preparation for tsunami disasters as “tsunami disaster alert areas.” However, more than 10 years after the law’s enactment, only about half of the prefectures with estimated tsunami inundation have designated disaster alert areas. One reason for this lack of progress is that it is difficult to reach a consensus owing to concerns regarding falling land prices caused by rumors. Thus, this study presents an empirical analysis of the impact of designating tsunami disaster alert areas on land prices. The difference in land prices between designated tsunami disaster alert areas and other areas is analyzed using the difference-in-difference and propensity score matching methods, using data on 26 prefectures. The eight prefectures in the “treatment group” were designated as tsunami disaster alert areas before 2018. The other prefectures are assigned to the “control group.” The results show a significant difference between designated tsunami disaster alert areas and other locations. The results are confirmed in kernel matching bandwidths of 0.06 for the entire sample, sample of only residential use, and sample of an estimated tsunami inundation depth below 2 m. Thus, this study concludes that before designating tsunami alert areas, local prefectures must carefully consider the impact of such designation on land prices.