Event‐based methods are commonly used to assess the risk to distributed infrastructure systems. Stochastic event‐based methods consider all hazard scenarios that could adversely impact the infrastructure and their associated rates of occurrence. However, in many cases, such a comprehensive consideration of the spectrum of possible events requires high computational effort. This study presents an active learning method for selecting a subset of hazard scenarios for infrastructure risk assessment. Active learning enables the efficient training of a Gaussian process predictive model by choosing the data from which it learns. The method is illustrated with a case study of the Napa water distribution system where a risk‐based assessment of the post‐earthquake functional loss and recovery is performed. A subset of earthquake scenarios is sequentially selected using a variance reduction stopping criterion. The full probability distribution and annual exceedance curves of the network performance metrics are shown to be reasonably estimated.
Commission VI, WG VI/2 KEY WORDS: Crowdsourcing, Capacity Building, Disaster Management, MANU, Mobile Application, Uttarakhand
ABSTRACT:Uttarakhand State of India suffered a widespread devastation in June 2013 due to floods caused by excessive rain in the upper reaches of the Himalaya, glacial lake outburst flood (GLOF) and landslides. Restoration process in this mountainous State calls for scientifically sound planning so that the vulnerabilities and risks to such natural hazards are minimised and developmental processes are sustainable in long run. Towards this, an understanding of the patterns and major controls of damage of the recent disaster is a key requirement which can be achieved only if the primary data on locations and types of damage along with other local site conditions are available. Considering widespread damage, tough nature of terrain and the need for collecting the primary data on damage in shortest possible time, crowdsourcing approach was considered to be the most viable solution. Accordingly, a multiinstitutional initiative called 'Map the Neighbourhood in Uttarakhand' (MANU) was conceptualised with the main objective of collecting primary data on damage through participation of local people (mainly students) using state-of-art tools and technologies of data collection and a mechanism to integrate the same with Bhuvan geo-portal (www.bhuvan.nrsc.gov.in) in near real-time. Geospatial analysis of crowd-sourced points with different themes has been carried out subsequently for providing inputs to restoration planning and for future developmental activities. The present paper highlights the capacity building aspect in enabling the data collection process using crowdsourcing technology.
A framework for dynamically updating post-earthquake functional recovery forecasts is presented to reduce the epistemic uncertainty in the predictive model. A Bayesian Network (BN) model is used to provide estimates of the total recovery time, and a process-based discrete event simulation (PBDES) model generates forecasts of the complete recovery trajectory. Both models rely on component damage and duration-based input parameters that are dynamically updated using Bayes’ theorem, as information becomes available throughout the recovery process. The effectiveness of the proposed framework is demonstrated through an application to the pipe network of the City of Napa water distribution system. More specifically, pipe damage and repair data from the 2014 earthquake are used as a point of comparison for the dynamic forecasts. It is shown that, over time, the mean value of the total recovery duration generated by the BN-based model converges to the observed value and the dispersion is reduced. Also, despite a crude initial estimate, the median trajectory generated by the PBDES model provides a reasonable approximation of the observed recovery within 30 days following the earthquake. The proposed framework can be used by emergency managers to investigate the efficacy of post-event mitigation measures (e.g. crew allocation, resource prioritization) utilizing the most current data and knowledge.
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