Understanding, modeling, and reducing natural-hazard-driven disaster risks
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July 2023, DE-RISC Lab, University College London
Disaster impact metrics (DIMs) are key outputs of seismic risk models, which quantitatively summarise various estimated effects of modelled earthquake disasters (e.g., the number of collapsed buildings). DIMs provide a tangible way of communicating risks to various stakeholders and end users (such as disaster planning authorities) and help to inform disaster risk reduction policies. However, the full decision-support potential of DIMs is currently hindered by some limitations. Existing DIMs typically capture only (direct) physical damage and economic losses, neglecting the well-being implications of earthquake disasters and the unique challenges that different social groups may face due to such events. Furthermore, they are usually designed for application to developed countries and may not be relevant for Global South contexts. In addition, conventional DIMs focus primarily on the immediate aftermath of a disaster, crucially overlooking the recovery phase that may last years or even decades. In general, these shortcomings can be attributed to a lack of research effort on - and therefore understanding of - the bespoke and dynamic requirements of different stakeholders concerning disaster impact assessment. Towards addressing this challenge, we propose a toolbox for characterising context-specific DIMs that meet relevant stakeholder needs. The toolbox includes 1) a general taxonomy and a pool of representative DIMs developed from a comprehensive literature review of disaster impacts across various contexts, capturing holistic effects far beyond physical damage and economic losses; and 2) a framework to systematically select DIMs from this pool or guide the design of novel DIMs, based on stakeholder feedback obtained through targeted questionnaires. We showcase the proposed toolbox using Kathmandu Valley, Nepal, as a case study. This work contributes to advancing the utility of seismic risk assessments in critical decision-making efforts, e.g., policy design. Collaborators: Fabrizio Nocera, Gemma Cremen, Carmine Galasso (UCL), and Vibek Manandhar (NSET Nepal)
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Feb 2023, DE-RISC Lab, University College London
Earthquake events can cause affected households to relocate. Post-earthquake relocation disrupts displaced households’ social ties as well as their access to affordable services. Simulation models that capture post-earthquake relocation decision-making can be useful tools for supporting the development of related disaster risk reduction policies that aim at mitigating disaster-induced relocation. Yet, existing versions of these models focus particularly on housing-related factors (e.g., housing repair costs), which are not the sole driver of post-earthquake relocation. In this paper, we integrate data-driven approaches and local perspectives into an existing simulation-based framework to holistically capture various context-specific factors perceived as being important to household relocation decision-making. The enhanced framework is used to quantitatively assess the effectiveness of various disaster risk reduction policies - both 'soft' (e.g., post-earthquake livelihood assistance funds) and 'hard' (e.g., upgrading existing infrastructure facilities to higher building codes) - in reducing post-earthquake household relocation, with an explicit focus on low-income households. We demonstrate it using a possible future (50-year) projection of “Tomorrowville”, a synthetic expanding urban extent that imitates a Global South setting. Our analyses suggest that livelihood assistance funds are more successful and pro-poor when it comes to mitigating positive post-earthquake relocation decision-making than hard policies focused on strengthening buildings (at least in the context of the examined case study). |
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June 2022, Tomorrow's Cities
Recent major earthquake disasters have highlighted the effectiveness of financial soft policies (e.g., earthquake insurance) in transferring seismic risk away from those directly impacted and complementing 'hard' disaster risk mitigation measures. However, the benefits of existing financial soft policies are often not guaranteed. This may be attributed to: (1) their low penetration rate (e.g., in the case of earthquake insurance); (2) the fact that they typically neglect the explicit needs of low-income sectors in modern societies, who are often disproportionately impacted by natural-hazard driven disasters; and/or (3) their failure to consider the time-dependent nature of urban exposure. We contribute towards addressing these shortcomings by proposing a flexible framework for designing and assessing bespoke, people-centered, household-level, compulsory financial soft policies (including conventional earthquake insurance, disaster relief fund schemes, income-based tax relief scheme, or a combination of those) across cities under rapid urban expansion. |
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January 2022, Stanford Urban Resilience Initiative
Residential damage from major disasters often displaces local residents out of their homes and into temporary housing. Out-of-town contractors assisting in post-disaster housing reconstruction also need housing, creating additional pressure on the local housing stock. Communities should thus prepare for a surge in temporary housing demand to minimize the impact on the local residents and to expedite housing recovery efforts. This paper introduces an agent-based simulation framework to estimate the workforce demand and the joint temporary housing needs of contractors and displaced households. The framework can be used to evaluate the resulting challenges and benefits of interventions aimed at attracting out-of-town contractors to expedite housing recovery. We present a case study on the housing recovery of the city of San Francisco after hypothetical M6.5, M7.2, and M7.9 earthquakes. Collaborators: Rodrigo Costa and Jack Baker (Stanford University)
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December 2020, Stanford Urban Resilience Initiative
This study investigates the effect of accounting for both physical damage to infrastructure and household disaster preparedness in estimates of potable water needs after earthquakes. A case study is presented involving the water supply system to the city of San Francisco after an M7.9 earthquake. Accounting for household preparedness helps identify regions in the city where water supply is interrupted, and many people may not have personal resources to access alternative sources of water. Considering both infrastructure disruption and household characteristics may inform decisions to allocate emergency water resources across the city during emergency response. Collaborators: Rodrigo Costa and Jack Baker (Stanford University)
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March 2021, Course Project of CEE286 Stanford University
This study proposes a privacy-friendly, reliable, economical, and flexible smart home security monitoring system that consists of non-intrusive and low-cost vibration sensors, a stranger detection algorithm based on the two-sample t-test, and an occupant identification algorithm based on a support vector machine model. Unlike a traditional home security system that extensively deploys cameras, the proposed system uses the building structure to sense the rich information embodied in human footsteps indirectly and passes the information to vibration sensors. The proposed home security system is highly flexible in its definition of occupants and strangers, allowing the end-user to customize the system to satisfy various needs under different application scenarios. Previous studies have utilized footstep-induced floor vibrations for occupant detection and estimating the left-right walking gait of humans. However, studies focusing on stranger detection are scant. This study aims to address the urgent need for a privacy-friendly home security monitoring system using footstep-induced floor vibrations. Instructor: Haeyoung Noh (Stanford University)
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November 2020, Course Project of CEE254 Stanford University
This study proposes an ensemble learning model with dynamic weighting scheme to predict the PM2.5 concentration at locations of interest. The model can perform short-term and long-term prediction, and interpolation tasks. The ensemble method is an amalgamation of six individual models of different levels of flexibility. Through bootstrapping, 100 samples are drawn from 28 days of data collected by both mobile and static sensors in Tianjin and 7 days of static and mobile data collected in Foshan. Six individual models are evaluated on the 100 samples to quantify uncertainties associated with each model. Weights of individual models consist of two parts: preliminary weights and dynamic weights. The preliminary weights are preassigned according to model performance on the 100 bootstrapped samples. The remaining weights are dynamically decided by either Cross-Validation error or error on the 20% held-out data during the learning process. The proposed ensemble learning model is expected to perform well on all three tasks for which it was designed. We won Best Project Award for Robust Prediction Model in the class competition. Collaborator: Ben Flanagan
Instructor: Haeyoung Noh (Stanford University) |
