The 2011 Great East Japan Tsunami exposed many hidden weaknesses in Japan's tsunami countermeasures. Since then, many improvements have been made in both structural measures (numerical simulations, coastal defense structures, building damage assessment and control forests) and nonstructural measures (warning/observation and evacuation). This review summarizes the lessons and improvements in the five-year time period after the 2011 event. After five years, most of the lessons from the 2011 tsunami have been applied, including more realistic tsunami simulations using very fine grids, methods to strengthen coastal defense structures, building evacuations and coastal forests, improved warning content and key points to improve evacuation measures. Nevertheless, large future challenges remain, such as an advanced simulation technique and system for real-time hazard and risk prediction, implementation of coastal defense structures/multilayer countermeasures and encouraging evacuation. In addition, among papers presented at the coastal engineering conference in Japan, the proportion of tsunami-related research in Japan increased from 15% to 35% because of the 2011 tsunami, and approximately 65-70% of tsunami-related studies involve numerical simulation, coastal structures and building damage. These results show the impact of the 2011 tsunami on coastal engineering related to academic institutions and consulting industries in Japan as well as the interest in each tsunami countermeasure.
Based on the classification provided by the Ministry of Land, Infrastructure, Transport and Tourism (MLIT), the damage level of buildings impacted by the 2011 Great East Japan tsunami can be separated into six levels (from minor damage to washed away). The objective of this paper is to identify the significant predictor variables and the direction of their potential relationship to the damage level in order to create a predicting formula for damage level. This study used the detailed data of damaged buildings in Ishinomaki city, Miyagi prefecture, Japan, collected by MLIT. The explanatory variables tested included the inundation depth, number of floors, structural material, and function of the building. Ordinal regression was applied to model the relationship between the ordinal outcome variable (damage level) and the predictors. The findings indicated that inundation depth, structural material, and function of building were significantly associated with the damage level. In addition to this new type of model, this research provides a valuable insight into the relative influence of different factors on building damage and suggestions that may help to revise the classification of current standards. This study can contribute to academic tsunami research by assessing the contribution of different variables to the observed damage using new approaches based on statistical analysis and regression. Moreover,
In the year 2020, SARS-CoV-2, the virus behind the coronavirus disease (COVID-19) pandemic, affected many lives and businesses worldwide. COVID-19, which originated in Wuhan City, China, at the end of December 2019, spread over the entire world in approximately four months. By October 2020, approximately 20 million people were infected and millions had died from this disease. Many health organizations such as the World Health Organization and Centers for Disease Control and Prevention made COVID-19 their primary focus. Many industries, especially, the tourism industry, were affected by the pandemic as many flight and hotel reservations were canceled. Thailand, a country considered one of the world’s most popular tourist destinations, suffered much losses because of this pandemic. Many events and travel bookings were canceled and/or postponed. Many people expressed their views and emotions related to this situation over social media, which is considered a powerful media for spreading news and information. In this research, the views of people who were planning to travel to Bangkok, the capital city and most popular destination in Thailand, were retrieved from Twitter for the dates between April 3 and 30, 2020, the period during which the country underwent nationwide lockdown. Sentiment analysis was performed using the support vector machine algorithm. The results showed 71.03% classification accuracy based on three sentiment classifications: positive, negative, and neutral. This study could thus provide an insight into travelers’ opinions and sentiments related to the tourism business. Based on the significant terms in each sentiment extracted, strengths and weaknesses of each tourism issue could be obtained, which could be used for making recommendations to the related tourism organizations.
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