The Great East Japan Earthquake and the Fukushima nuclear accident cause large human population movements and evacuations. Understanding and predicting these movements is critical for planning effective humanitarian relief, disaster management, and long-term societal reconstruction. In this paper, we construct a large human mobility database that stores and manages GPS records from mobile devices used by approximately 1.6 million people throughout Japan from 1 August 2010 to 31 July 2011. By mining this enormous set of Auto-GPS mobile sensor data, the short-term and longterm evacuation behaviors for individuals throughout Japan during this disaster are able to be automatically discovered. To better understand and simulate human mobility during the disasters, we develop a probabilistic model that is able to be effectively trained by the discovered evacuations via machine learning technique. Based on our training model, population mobility in various cities impacted by the disasters throughout the country is able to be automatically simulated or predicted. On the basis of the whole database, developed model, and experimental results, it is easy for us to find some new features or population mobility patterns after the recent severe earthquake, tsunami and release of radioactivity in Japan, which are likely to play a vital role in future disaster relief and management worldwide.

No abstract

Sediment movement around the river mouth is intensive and complicated, owing to the combined effects of short waves, long waves, tide currents and river discharge, which usually leads to great and frequent topography change. This study aims to describe the sediment movement in a river mouth, further to distinguish the contribution of each hydrodynamic component to sediment movement, and to get better understanding on the nonlinear effects to sediment transport and their interactions. A numerical model was developed to simulate the hydrodynamic environment and the movement of color sand tracers. Both field survey and numerical modeling were carried out around the Magome River mouth, which is located on the Enshu Coast, Shizuoka Prefecture, Japan and drains into the Pacific Ocean.

ABSTRACT:In this study, we developed a method to detect sudden population concentration on a certain day and area, that is, an "Event," all over Japan in 2012 using mass GPS data provided from mobile phone users. First, stay locations of all phone users were detected using existing methods. Second, areas and days where Events occurred were detected by aggregation of mass stay locations into 1-km-square grid polygons. Finally, the proposed method could detect Events with an especially large number of visitors in the year by removing the influences of Events that occurred continuously throughout the year. In addition, we demonstrated reasonable reliability of the proposed Event detection method by comparing the results of Event detection with light intensities obtained from the night light images from the DMSP/OLS night light images. Our method can detect not only positive events such as festivals but also negative events such as natural disasters and road accidents. These results are expected to support policy development of urban planning, disaster prevention, and transportation management.

the category-5 typhoon T0918 hit the Japan Pacific Coast and significant topographic changes occurred along the Enshu-nada coast due to the storm waves. Significant wave height was more than 10m and rapid landward migration of the sand spit of the Tenryu river mouth was observed. Based on X-band radar image analysis, spatial and temporal changes of overtopping waves and corresponding sediment flux across the sand spit were revealed. Spatial concentration of the sediment flux can be explained by the nonlinear relationship between wave height of overtopping waves and corresponding sediment fluxes. A sediment flux model was developed based on the relationship. Maximum flux was estimated by the model to be 51 m 3 /m/hour. It happened when high waves and high tide were observed simultaneously.