Bruno Adriano

Recent Advances in Agent-Based Tsunami Evacuation Simulations: Case Studies in Indonesia, Thailand, Japan and Peru

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 modeling has added to tsunami evacuation simulations and tsunami mitigation efforts. A brief review of previous agent-based evacuation models in the literature is given to highlight recent progress in agent-based methods. Finally, challenges are noted for bridging gaps between geoscience and social science within the agent-based approach for modeling tsunami evacuations.

Understanding the Extreme Tsunami Inundation in Onagawa Town by the 2011 Tohoku Earthquake, Its Effects in Urban Structures and Coastal Facilities

The 2011 Tohoku Tsunami is considered to be one of the most tragic events in Japans disaster history, and represents an important milestone for the research community regarding the investigation of the characteristics of tsunami inundation. A thorough analysis of tsunami inundation was conducted using numerical modeling, and measurements from a video recorded from the rooftop of a building in Onagawa in Miyagi Prefecture. In this study, we analyze the destruction of buildings using numerical simulations and tsunami fragility functions. Numerical results for the locations at which the tsunami eyewitness video was recorded are compared with measurements. In addition, we considered the effect of the breakwater in Onagawa bay to evaluate its contribution to reducing overland tsunami inundation depths. The results of our simulations show that the maximum inundation depth due to the first incoming wave was over 16 m, and over 500 buildings were washed away with this first wave. This result is consistent with the video data. Further, we found that the breakwater, which was not originally designed against tsunami waves, reduced the maximum tsunami inundation depth at least by 2.0 m in Onagawa town.

Extraction of damaged areas due to the 2013 Haiyan Typhoon using ASTER data

In this study, the extent of the flooded areas by the Super Typhoon Haiyan in the Philippines were extracted using ASTER VNIR images taken over Tacloban city in the Visayas. In order to constraint the affected area, we employed the normalize difference vegetation and water indices (NDVI and NDWI) from the pre- and post-event images. The extension of the flooded area was determined by comparing the index characteristics before and after the event. A phase-based change detection method indices was applied to classify the affected area into three classes according to the changes between the pre- and post-images. Through NDWI the flooded areas were detected despite the moderate resolution of ASTER images. In addition, the phase-based analysis successfully detected level of change within the affected area that may be correlated to the damage observed on field surveys. The results from the phase-based analysis were verified with damage levels obtained through visual damage inspection using high resolution satellite images.

A Framework of Rapid Regional Tsunami Damage Recognition From Post-event TerraSAR-X Imagery Using Deep Neural Networks

Near real-time building damage mapping is an indispensable prerequisite for governments to make decisions for disaster relief. With high-resolution synthetic aperture radar (SAR) systems, such as TerraSAR-X, the provision of such products in a fast and effective way becomes possible. In this letter, a deep learning-based framework for rapid regional tsunami damage recognition using post-event SAR imagery is proposed. To perform such a rapid damage mapping, a series of tile-based image split analysis is employed to generate the data set. Next, a selection algorithm with the SqueezeNet network is developed to swiftly distinguish between built-up (BU) and nonbuilt-up regions. Finally, a recognition algorithm with a modified wide residual network is developed to classify the BU regions into wash away, collapsed, and slightly damaged regions. Experiments performed on the TerraSAR-X data from the 2011 Tohoku earthquake and tsunami in Japan show a BU region extraction accuracy of 80.4% and a damage-level recognition accuracy of 74.8%, respectively. Our framework takes around 2 h to train on a new region, and only several minutes for prediction.

Possible Failure Mechanism of Buildings Overturned during the 2011 Great East Japan Tsunami in the Town of Onagawa

Six buildings were overturned in the town of Onagawa during the 2011 Great East Japan tsunami. This study investigates the possible failure mechanisms of building overturning during tsunami flow. The tsunami inundation depth and flow velocity at each overturned building were recalculated by using a tsunami numerical simulation and verified using a recorded video. The overturning moment is a result of hydrodynamic and buoyancy forces, whereas the resisting moment is a result of building self-weight and pile resistance force. This study aimed to demonstrate that the building foundation design is critical for preventing buildings from overturning. The analysis results suggest that buoyancy force can generate a larger overturning moment than hydrodynamic force and the failure of a pile foundation could occur during both ground shaking and tsunami flow. For the pile foundation, pile resistance force plays a significant role due to both tension and shear capacities at the pile head and skin friction capacity between the pile and soil, which can be calculated from 18 soil boring data in Onagawa using a conventional method in the AIJ standards. In addition, soil liquefaction can reduce skin friction capacity between the pile and soil resulting in a decrease of the resisting moment from pile resistance force.

Reconstruction Process and Social Issues After the 1746 Earthquake and Tsunami in Peru: Past and Present Challenges After Tsunami Events

Tsunamis, oceanic wave events that are most often triggered by earthquakes at interplate subduction areas, result in damaged infrastructure, ecological disruption and a substantial number of deaths among coastal communities. In recent years, a key concept in the assessment of tsunami events has been resilience, which can be understood as the ability of a group to anticipate risk, limit negative impacts and recover rapidly from a catastrophic event through processes of survival, adaptability, evolution and growth. The term resilience incorporates a dynamic and durable connotation of constant preparedness, not only for the next tsunami event but also for the ensuing process of reconstruction. The reconstruction of a community devastated by a tsunami poses a multiplicity of challenges, including environmental, social, political, scientific, engineering and architectural challenges. In this paper, we first examine a 1746 tsunami event (Mw9.0) that occurred on the coast of Viceroyalty-era Peru and consider the challenges reported during the subsequent reconstruction of a devastated city and port. We contrast those challenges, reported nearly 250 years ago, with analogous challenges observed in more recent tsunami events. The paper concludes with comments on the lessons learned and suggests areas of future research.

Novel Unsupervised Classification of Collapsed Buildings Using Satellite Imagery, Hazard Scenarios and Fragility Functions

Although supervised machine learning classification techniques have been successfully applied to detect collapsed buildings, there is still a major problem that few publications have addressed. The success of supervised machine learning strongly depends on the availability of training samples. Unfortunately, in the aftermath of a large-scale disaster, training samples become available only after several weeks or even months. However, following a disaster, information on the damage situation is one of the most important necessities for rapid search-and-rescue efforts and relief distribution. In this paper, a modification of the supervised machine learning classification technique called logistic regression is presented. Here, the training samples are replaced with probabilistic information, which is calculated from the spatial distribution of the hazard under consideration and one or more fragility functions. Such damage probabilities can be collected almost in real time for specific disasters such as earthquakes and/or tsunamis. We present the application of the proposed method to the 2011 Great East Japan Earthquake and Tsunami for collapsed building detection. The results show good agreement with a field survey performed by the Ministry of Land, Infrastructure, Transport and Tourism, with an overall accuracy of over 80%. Thus, the proposed method can significantly contribute to a rapid estimation of the number and locations of collapsed buildings.

Tsunami Source Inversion Using Tide Gauge and DART Tsunami Waveforms of the 2017 Mw8.2 Mexico Earthquake

On September 8, 2017 (UTC), a normal-fault earthquake occurred 87 km off the southeast coast of Mexico. This earthquake generated a tsunami that was recorded at coastal tide gauge and offshore buoy stations. First, we conducted a numerical tsunami simulation using a single-fault model to understand the tsunami characteristics near the rupture area, focusing on the nearby tide gauge stations. Second, the tsunami source of this event was estimated from inversion of tsunami waveforms recorded at six coastal stations and three buoys located in the deep ocean. Using the aftershock distribution within 1 day following the main shock, the fault plane orientation had a northeast dip direction (strike

Damage detection due to the typhoon haiyan from high-resolution SAR images

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