Erick Mas 교수

The 2011 Great East Japan Earthquake/Tsunami was a magnitude 9.0 Mw event that destroyed most structural tsunami countermeasures. However, approximately 90% of the estimated population at risk from the tsunami survived due to rapid evacuation to higher ground or inland. In this paper, we introduce an evacuation model integrated with a numerical simulation of a tsunami and a casualty estimation evaluation. The model was developed in Netlogo, a multi-agent programming language and modeling environ

As confirmed by the extreme tsunami events over the last decade (the 2004 Indian Ocean, 2010 Chile and 2011 Japan tsunami events), mitigation measures and effective evacuation planning are needed to reduce disaster risks. Modeling tsunami evacuations is an alternative means to analyze evacuation plans and possible scenarios of evacuees’ behaviors. In this paper, practical applications of an agent-based tsunami evacuation model are presented to demonstrate the contributions that agent-based model

Abstract. On 27 February 2010, a megathrust earthquake of Mw = 8.8 generated a destructive tsunami in Chile. It struck not only Chilean coast but propagated all the way to Japan. After the event occurred, the post-tsunami survey team was assembled, funded by the Japan Science and Technology Agency (JST), to survey the area severely affected by the tsunami. The tsunami damaged and destroyed numerous houses, especially in the town of Dichato. In order to estimate the structural fragility against t

The 2011 Great East Japan earthquake and tsunami was a magnitude 9.0 Mw event that destroyed most structural tsunami countermeasures. However, approximately 90% of the estimated population at risk from the tsunami survived due to a rapid evacuation to higher ground or inland. Thus, tsunami evacuation is the most effective measure to reduce casualties. In this paper, we applied a new developed evacuation model integrated with the numerical simulation of tsunami for casualty estimation. This tool

Emergency responders require accurate and comprehensive data to make informed decisions. Moreover, the data should be acquired and analyzed swiftly to ensure an efficient response. One of the tasks at hand post-disaster is damage assessment within the impacted areas. In particular, building damage should be assessed to account for possible casualties, and displaced populations, to estimate long-term shelter capacities, and to assess the damage to services that depend on essential infrastructure

Abstract We developed tsunami fragility functions using three sources of damage data from the 2018 Sulawesi tsunami at Palu Bay in Indonesia obtained from (i) field survey data (FS), (ii) a visual interpretation of optical satellite images (VI), and (iii) a machine learning and remote sensing approach utilized on multisensor and multitemporal satellite images (MLRS). Tsunami fragility functions are cumulative distribution functions that express the probability of a structure reaching or exceedin

We estimated, from twelve scenarios of potential megathrust earthquakes, the tsunami impact on the Lima-Callao region in Central Peru. In addition, we conducted hazard mapping using the local envelope of the maximum inundation simulated in these scenarios. The deterministic approach is supported by the decades of geodetic measurements in this area that characterize the interseismic strain build up since historical megathrust earthquakes. The earthquake scenarios for simulation proposed in [1] in