As part of our continuous journey to refine our resilience modeling capabilities, One Concern regularly meets with our Technical Working Group to review our latest research and models. Comprised of independent leaders in the fields of disaster science, data science, engineering, and hydrology, the Technical Working Group helps to guide One Concern as we develop our hazard and climate resilience modeling capabilities by providing insights and feedback informed by the most up-to-date knowledge from peer-reviewed academic research.
Flood Models for Japan
Nearly half of the world’s population lives on or near coastlines, and their livelihoods often depend on the water and their industries reliant on coastal infrastructure. Because of this, flood modeling has long been a priority for One Concern. Our riverine flood models are driven with radar rainfall data and validated against stream gauge data taken from sources around Japan. We utilize tidal harmonics, hindcast pressure, and wind fields for historical typhoon events to measure potential flooding from the coast. We draw from the same rainfall data source as our riverine model for urban flooding, but we focus specifically on measurements around populated and developed locations.
For each of our flood models, the Technical Working Group found the data we generated aligned with ground-truth peak flood levels and noted that our flood model pipeline is “a sophisticated and seamless serial combination of widely covered multiple state-of-the-art and well-accepted models.”Additionally, the Technical Working Group noted that our machine learning methods to generate data to fill gaps where no ground-truth data was available is “a cutting-edge approach for dealing with data scarcity.” In the past, other industry flood models would ignore levees where data was absent.
Our next modeling update will focus on addressing:
· Underestimation of levee crest height due to elevation to represent levees in the inundation model and floodplain conveyance and storage capacity due to the rectangular shape of river channels in the riverine model.
· Overestimation of peak flood flows due to the kinematic wave routing in the riverine model; storm surge due to the use of increased wind speeds to account for breaking-wave-radiation-stress-induced water level setup in the coastal model; rainfall accumulation due to neglect of the storage and conveyance capacities of stormwater drains and deep tunnels, in the urban model; ponding due to negligence of pumping stations in the inundation model; and flood extent due to neglect of land-use-dependent surface roughness in the inundation model.
· Errors in baseflow due to the neglect of snowmelt in the riverine model and dam operation. We have improved from the time of receiving feedback to include the effects of snowmelt.
Earthquake Damage Models
Earthquake damage has historically been challenging to model due to the wide variance of building characteristics, such as their material makeup (e.g., wood, brick, metal, etc.), use (e.g., industrial, commercial, residential, etc.), age, and limited data for calibration and validation of the models. In response to our modeling, the Technical Working Group found:
“In contrast to other software for regional damage and loss analyses, such as HAZUS, One Concern’s cloud-based platform offers a promising new approach that leverages statistical machine learning and artificial intelligence (ML/AI) techniques to integrate observed and simulated data to characterize earthquake ground shaking, detailed inventories, structure performance, and recovery times. This approach permits the latest advances in hazard and damage estimation to be augmented with rapidly developing improvements in ML/AI techniques as developed across a broad range of fields.”
Our validation and calibration efforts for earthquake damage were found to be “in reasonably good agreement with observed data from the Northridge and Kumamoto earthquakes and those calculated for a potential future earthquake on the Hayward fault.” Since 2020, we have continued to expand, validate, calibrate, and train the ML model using observed data from additional events, such as the 1995 Kobe earthquake and simulations. Together with current ground motion maps based on Japan’s national state-of-the-art Kik-Net and K-Net seismic-observation networks. In addition, the model has been expanded to incorporate exposure data developed using state-of-the-art imputation procedures for situations with limited data.
Our next modeling update is focusing on improving ML/AI damage estimation models in Japan.
Earthquake Building Inventory Model
In the past year, we have developed and validated models to create a high-resolution building inventory database for the United States by combining and imputing data from various sources. To further our work, we will continue to train and evaluate One Concern’s inventory, damage, and loss components of the ML/AI model using inventory data and damage/loss data from regions at risk from earthquakes.
Earthquake Time Recovery Model
Another area that we are working on is recovery-time models, which are needed for resilience planning. Recovery-time is a measure of resilience that models the capabilities of a community to bounce back from a natural disaster. Our world is highly complex and interconnected, and there are numerous layers of underlying dependencies that can impact recovery time. For example, a building may not see direct damage from a natural disaster, but vital infrastructure such as water and power utilities may have suffered damage. Operations effectively grind to a halt if a seaport or a manufacturing facility loses power or employees cannot safely travel to work due to damaged roads and bridges. In this regard, the Technical Working Group noted:
“Given the increasing recognition of recovery-time as a critical measure of indirect losses and community resilience, we appreciate One Concern’s efforts to incorporate recovery time (in addition to damage and direct economic loss, i.e., repair costs) into their earthquake model. To the extent that recovery modeling has not received as much attention from the research community, One Concern’s modeling platform can provide a vital resource to test and evaluate alternative building recovery models, including ones that scale with the earthquake magnitude and include resource availability and lifeline systems (e.g., water and electric power) in the recovery assessment.”
We are also exploring expanding the recovery models for container ports to include liquid-storage and liquid-transfer ports, which are critical for recovering disrupted oil and gas supply chains for transportation and electric power plants. Finally, we will continue to develop state-of-the-art models for resilience planning that define, adopt, and communicate clear metrics for characterizing “performance functionality” for various infrastructure systems.