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.

Rapid and accurate hazard forecasting is important for prompt evacuations and reducing casualties during natural disasters. In the decade since the 2011 Tohoku tsunami, various tsunami forecasting methods using real-time data have been proposed. However, rapid and accurate tsunami inundation forecasting in coastal areas remains challenging. Here, we propose a tsunami forecasting approach using convolutional neural networks (CNNs) for early warning. Numerical tsunami forecasting experiments for Tohoku demonstrated excellent performance with average maximum tsunami amplitude and tsunami arrival time forecasting errors of ~0.4 m and ~48 s, respectively, for 1,000 unknown synthetic tsunami scenarios. Our forecasting approach required only 0.004 s on average using a single CPU node. Moreover, the CNN trained on only synthetic tsunami scenarios provided reasonable inundation forecasts using actual observation data from the 2011 event, even with noisy inputs. These results verify the feasibility of AI-enabled tsunami forecasting for providing rapid and accurate early warnings.

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The evacuation behavior observed in Kesennuma City, Tohoku, Japan during the Great East Japan Earthquake and Tsunami and a corresponding evacuation simulation are presented. We first organized the damage and evacuation behaviors observed in the target area using the results of previous studies and official surveys. In addition, we compiled all available multimedia sources recording evacuation behavior during the 2011 event to identify the precise temporal and spatial details of the evacuation behavior. Then, a tsunami evacuation simulation was developed based on all compiled evacuation data, considering the tendencies regarding the use of main roads for evacuation, residents' shelter preferences, and pedestrian-car interactions. The developed simulation was validated against the compiled evacuation behaviors by inputting the estimated initial conditions of the 2011 event. The traffic scenarios calculated in the simulation closely reproduced the actual traffic flow as observed from the evacuation data. The evacuee populations at several shelters in the simulation also quantitatively reproduced the trend of the real numbers of evacuees reported by Kesennuma City. The results of the simulation exhibited a better capability 1640023-1 F. Makinoshima, F. Imamura f3 Y. Abe to estimate the actual evacuation behavior during the 2011 event than that achieved in previous studies.