THE
WORLD IS CHANGING
...AND TECHNOLOGY NEEDS TO HELP.
Communities across the world face increased risk from climate events exacerbated by climate change. These communities are in dire need of prediction, understanding, planning, and mitigation.
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Modeling environmental systems is difficult. For example, climate events are complex in themselves, and effects of multiple events are often compounded. Cascading effects cause obvious and non-obvious damage to communities, infrastructure, economy, and environment over many years. ​
Biomatica can model all these factors with ease. It can tie wide environmental data to focused data, and even generate simulations with both.​
CASE STUDY:
RESILIENT COASTAL COMMUNITIES
U.S. Department of Homeland Security
01
THE
CHALLENGE
Every community has critical infrastructure in need of protection. To disaster-proof a community, it is important to understand how climate events will affect its infrastructure, how long will the effects last, and what will be the consequences for the community.
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We chose the vibrant city of New Orleans for our study. This beautiful city was devastated by hurricane Katrina, and did not fully recover even after many years.
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How can we stop something like this from happening again? ​What can be done to improve the resilience of coastal communities and their infrastructure?
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We knew that the solution had to be more than a canned 'single factor' study. This complex problem required a broader analysis pulling in multiple datasets. What's more, to be useful the solution had to be predictive, proactive, provide site specific granularity, and do all this in real-time.
02
THE
DIGITAL TWINS
A Digital Twin is a digital replica of a system. It captures relevant operational, geometrics, physical, or behavioral aspects of the entity it mimics.
​​We decided to create 8 kinds of Digital Twins for New Orleans.
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HAZARD Digital Twins: These Digital Twins represented climate events. Two Digital Twins were Machine Learning models - one for inundation (rainfall) and the other for ocean surges. The third Digital Twin was a FEMA flood simulation. All three models had been developed at great expense by the government and used extensively... in isolation. However, no one had been able to bring their outputs together in one holistic model.
GEO Digital Twin: We modeled the city of New Orleans, its map, and its details as a Digital Twin.
ENTITY Digital Twins: This Digital Twin provided site-specific infrastructure and other entity information. We fused multiple open and government data sources to create this Twin.
INCIDENT Digital Twins: This dynamically updated Digital Twin real-time information for the city's infrastructure. For example, construction events on roads. We fused open and government data sources to create this Twin.
PATTERNS OF LIFE Digital Twin: We had to understand how people moved in their daily lives, and how this changed during climate events. This diffuse concept was captured in a PATTERNS OF LIFE Digital Twin. This Twin was created by combining travel and social media data sources, such as Twitter.
MITIGATION DICTIONARY: This Digital Twin modeled actions that the city could take to improve resilience. For example, create a storm shelter or improve drainage on a road. The purpose of this Twin was to allow hypothesis testing in Biomatica simulations.
03
THE DIGITAL THREAD
These Digital Twins are dependent. They affect each other in obvious and non-obvious ways.
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Digital Thread captures the lifecycle, temporal, and combined effects of multiple Digital Twins. Biomatica Digital Thread allows us to follow the ’thread’ of multiple related phenomena. ​
​We started by configuring Biomatica with Risk and Resilience semantic models. These models were driven by a hierarchy of concepts. For example, Risk is a complex function of hazard, exposure, vulnerability, location, and other factors.
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Biomatica AI was primed with climate science domain knowledge. ​
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Biomatica standardized the outputs of the Digital Twins physically, semantically, and quantitatively, and pulled them into the Thread.
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Biomatica AI assimilated the knowledge in the Twins, and in-between the Twins, to create a global simulation.
04
NEW
CAPABILITIES
This installation of Biomatica produced a very powerful tool for climate researchers, city planners, and anyone who needs to protect infrastructure from weather.
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When the user interacted with Biomatica, they saw an entire, multi-layered, city. The city had rich details about multiple critical locations. Diffuse concepts like 'patterns of life' of the population were woven in.​​​​​
The entities (Digital Twins) in this city were backed by real data. So, if you clicked on a road, you could see if the municipality was working on it and if it was blocked. You can see all available specs for these entities, and know if a road has good drainage.
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​You could subject the virtual city to a variety of climate hazards. The simulation would accurately predict cascading effects. For example, you could see the combined effects of rainfall and ocean surges on flooding in the city.
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Biomatica predicted the risk and resilience for any point in time, and for any site. You could zoom in and out, and predict the best, worst, and average case scenarios.
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You could set mitigation actions, like designating the airport as a storm shelter, and see the simulation quantify the benefits. The system could calculate non-obvious downstream effects. For example, setting the Airport as a storm shelter turned out to be bad idea because it put too much strain on the surrounding roads (predicted with input from patterns of life).​​​​
