海角社区

The 海角社区-developed tool to predict storm surge and flooding during severe weather events鈥攖he Coastal Emergency Risks Assessment, or CERA, website鈥攈as become an essential resource for thousands of emergency managers and first responders in Louisiana and the nation鈥檚 coastal states to help protect people and infrastructure. Now, the tool will become even smarter and faster, thanks to artificial intelligence, or AI, and support from the U.S. Department of Energy, which is looking to secure key energy assets, especially along the Gulf Coast, from stronger and more frequent storms.

Although coastal parishes and counties make up only one-fourth of the nation鈥檚 land area, they鈥檙e home to more than half of the U.S. population and generate most of the nation鈥檚 income. In Louisiana, where energy is the number-one industry, lives and livelihoods depend on how much of the nation鈥檚 critical energy infrastructure鈥攍ocated on or near the state鈥檚 coast鈥攊s at risk from coastal hazards, such as hurricanes and flooding.

The fate of Louisiana鈥檚 coastline and the future of its economy are closely linked, as evidenced by the U.S. Department of Energy鈥檚 recent investment in 海角社区 research and a new interdisciplinary collaboration to improve storm and flood preparedness and mitigation, which is of urgent operational interest to multiple state and national agencies, including the Coastal Protection and Restoration Authority, or CPRA, U.S. Army Corps of Engineers, the National Oceanic and Atmospheric Administration, or NOAA, the U.S. Coast Guard, the Federal Emergency Management Agency and the Department of Homeland Security and Emergency Preparedness鈥攁ll professional users of .

Scientists make predictions about future events, including severe weather events, either through direct observation or by building models, which are increasingly computational, that mimic nature in significant鈥攜et incomplete鈥攚ays, and then running simulations in those models. 海角社区鈥檚 CERA tool uses several models and supercomputers to help emergency managers and first responders across the nation visualize where storm water will go and how high it will rise during hurricanes and severe weather events. These models include the Advanced Circulation, or ADCIRC, surge guidance system, collaboratively developed by several universities; Global ESTOFS, or Extratropical Surge and Tide Operational Forecast System, from NOAA; and the more local CFIGS, the Compound Flood Inundation Guidance System by CE Hydro, which adds rainfall in the watersheds around Lake Pontchartrain, north of New Orleans and east of Baton Rouge.

A big challenge in computational coastal modeling is the inherent complexity and interdependence of constantly changing parameters. As seas rise and land either sinks, due to subsidence and erosion, or builds, due to sediment transport primarily by rivers, such as the Mississippi River, coastal models must be continuously adjusted using real-world measurements. In Louisiana and in the context of CERA, many of those measurements come from CPRA, which recently renewed its contract with 海角社区 ahead of the current hurricane season. For close to 15 years, CPRA has increasingly relied on CERA in its decisions to open or close flood gates to protect Louisiana residents and assets.

鈥満＝巧缜�鈥檚 CERA is the tool we use to operate a multi-billion-dollar flood protection system,鈥� said CPRA Operations Chief Ignacio Harrouch. 鈥淚t鈥檚 an invaluable tool, and everybody across the coast that operates a flood protection system, everybody wants to see CERA results to know when gates have to be closed.鈥�

鈥満＝巧缜�鈥檚 CERA is the tool we use to operate a multi-billion-dollar flood protection system...It鈥檚 an invaluable tool, and everybody across the coast that operates a flood protection system, everybody wants to see CERA results to know when gates have to be closed.鈥�

Ignacio Harrouch, Coastal Protection and Restoration Authority operations chief

鈥淓very year, it gets better,鈥� Harrouch continued. 鈥淚鈥檝e been with the agency since 2014, and I will tell you, CERA has come a long way in the last two years. The accuracy of the model has been excellent. Also, in this business, there is no room for error. We have to be right, and AI may make our flood prediction even more accurate.鈥�

On the local level, coastal emergency managers from Plaquemines to Cameron Parish use CERA to see and understand the potential impact of extreme wind and water, which helps inform road closings, evacuations as well as search and rescue operations.

鈥淲hen you鈥檙e telling people to evacuate or open or close flood gates, you鈥檇 better be right,鈥� said Windell Curole, general manager of the South Lafourche Levee District, in a recent story about CERA, which helps guide his community鈥檚 emergency management decisions.

Computational coastal modeling is about accurately representing water, wind, soil and sediment鈥攁ll tangible yet dynamic鈥攁s ones and zeroes. It鈥檚 a considerable feat. An additional challenge for CERA is that it currently can display predictions by various models, but not all models at the same time. Because of differences in scale, it is difficult to translate and infer information from one model to another. Meanwhile, predictions would be much more detailed and accurate if all storm surge and flooding models could be improved, merged and cross-checked. This is why 海角社区 is part of an ongoing project with University of Texas at Austin, University of Notre Dame and Pacific Northwest National Laboratory to couple coastal physics-based models using AI, a technology particularly adept at finding connection points and relationships between vast datasets. The three-year project, funded by the U.S. Department of Energy, or DOE, will couple ADCIRC, the high-resolution storm-surge-guidance model, with the DOE鈥檚 Energy Exascale Earth System Model, or E3SM, and involve both coastal and computational scientists.

On the 海角社区 side, the lead researchers are Carola Kaiser and Hartmut Kaiser with the 海角社区 Center for Computation & Technology (CCT). Carola Kaiser leads the software and web development of CERA while Hartmut Kaiser leads the 海角社区 STEllAR research group, which works to improve the speed and efficiency of high-performance computing through the design and implementation of HPX, a C++ runtime system for distributed and parallel computing of which the STEllAR group is the lead developer. Through a previous project, called Phylanx, funded by the U.S. Department of Defense, Hartmut Kaiser worked to create new computational infrastructure that improves the performance of AI and machine learning in supercomputers and distributed systems鈥攌nowledge he now will use in the current DOE project, which will leverage the newly state-funded, $12.5 million 海角社区 AI supercomputer for Louisiana.

鈥淲hen people start talking about artificial intelligence and machine learning, they often talk about the concrete problems they鈥檙e using these technologies to solve,鈥� Hartmut Kaiser said. 鈥淔ew actually worry about the computational infrastructure itself and what we need to solve these really large problems鈥攈ow to train neural networks faster and make sure we make excellent use of these big and powerful machines.鈥�

The new DOE-funded project, called Multiphysics Simulations and Knowledge discovery through AI/ML, or MuSiKAL, will use the already-established infrastructures of HPX and CERA to improve the existing models and come up with a new, AI-driven model that will source from, and connect, the others. The 海角社区 researchers鈥� goal is to produce even more accurate and detailed storm surge and flood predictions for Louisiana.

鈥淐ERA, as a visualization tool and user interface, allows us to easily organize and access this very complex data and quickly validate and improve the models,鈥� Hartmut Kaiser said. 鈥淵ou can think of it as a feedback loop, where we use real historic storm data to train the AI and then have it run simulations on real events. We can then compare the simulated data with real data. This way, we can quickly verify the quality of the models and improve their performance, perhaps by adding additional parameters.鈥�

For major storms, CERA relies on National Hurricane Center data to create both forecasts, or  theoretical predictions, and hindcasts, which show what actually happened.

鈥淔or us, this project is an opportunity to bring all of these pieces together,鈥� Carola Kaiser said. 鈥淐ERA can help build trust in the models, because you need to have a tool that people can look at and say, 鈥極kay, this looks reasonable,鈥� or, 鈥楴o, we need to put in more work.鈥� MuSiKAL will add to CERA鈥檚 existing capabilities and make it easier for us to plug in additional models and deliver results to the end users who depend, down to the hour and minute ahead of dangerous storms, on the best possible flood hazard guidance we can provide to protect our coastal communities.鈥�

CERA has always relied on supercomputers to run complex models. Historically, this forecasting has been an iterative process of solving systems of differential equations that describe what happens in nature. The data is so complex that a single run can take several hours, even on a supercomputer. Hartmut Kaiser, Carola Kaiser, and their collaborators now hope to significantly speed up this process, thanks to AI. Instead of spending time solving differential equations as new data comes out from the National Hurricane Center, the researchers will spend time training their AI with data from previous storms. After sufficient 鈥渆ducation,鈥� the AI should be able to provide near-instant forecasts.

鈥淥ur hope, essentially, is to trick the laws of nature by redistributing time,鈥� Hartmut Kaiser said. 鈥淏y investing our time in training the AI, we won鈥檛 have to invest so much time into each simulation during a storm event. That鈥檚 the power of AI.鈥�

 

Read more:

海角社区 Partnerships Improve Hurricane Storm Surge Forecasts for Louisiana, Nation (海角社区 Office of Research & Economic Development)

海角社区 EE&CS Professor Uses AI/ML to Predict Coastal Flooding (海角社区 College of Engineering)