Leveraging Big Data And AI For Disaster Resilience And Recovery

By Alyson Chapman, Texas A&M University College of Engineering June 13, 2023

In a world where natural hazards can strike at any time with devastating consequences, reducing their impacts in advance may seem impossible.

The unpredictability of these events and their effects makes it challenging to anticipate and respond appropriately, leaving individuals and communities vulnerable to their destructive effects.

In 2017, Hurricane Harvey made landfall along the Texas coast as a category four hurricane and slowed to nearly 5 mph as it moved inland. Palacios experienced a storm surge exceeding 8 feet. The storm dumped 56 inches of rain in the Friendswood area and more than 60 inches near Nederland and Groves. The National Hurricane Center reported $125 million in damage because of Hurricane Harvey.

Now, researchers from the Zachry Department of Civil and Environmental Engineering at Texas A&M University have created models using big data and artificial intelligence (AI) to help communities prepare for future natural disasters, assess the impacts and monitor the recovery in near real time. They used data from Harvey to test these AI-centric solutions.

Led by Dr. Ali Mostafavi, Zachry Career Development Associate Professor, the Urban Resilience.AI Lab is leveraging AI and big data for predictive risk analysis, predictive infrastructure failure assessment, situational awareness during the event, monitoring of recovery and rapid assessment of the impacts of disasters.

“When facing hazards, there are certain capabilities that AI and data science approaches provide or enhance that can improve the resiliency of communities to these disasters,” Mostafavi said. “Our vision over the past four or five years has been to improve disaster resilience by developing different classes of AI-based models that could provide foresights and insights critical for mitigation, preparedness, response and recovery.”

The growth of data from different types of sensors — from physical, social and other sensing technologies — has given researchers tremendous information to work with in creating these models.

“These days, cities and communities are essentially data factories. You can evaluate sensor data related to the condition of the community facing hazards from the traffic network cameras, people’s cell phone activities, power outage data, flood gauge data, satellite images and many other sources of technology that harness the heartbeat of the community,” Mostafavi said. “As our cities and communities become smarter with information and communication technologies, the amount of data generated grows.”

Mostafavi and his team in the Urban Resilience.AI Lab are taking the lead in harnessing community-scale big data to develop AI-based models with the potential to impact communities before, during and after a natural disaster or crisis.

Road Inundation Prediction

Roads are essential in urban cities, allowing goods, information and people to move from place to place. But during times of disaster, such as floods, road networks can be damaged or blocked, which impacts access to services such as hospitals, shelters and grocery stores. During floods in urban areas, vehicle accidents resulting from driving on flooded roads have been identified as a leading cause of fatalities, highlighting the failures of road networks.

Researchers have developed a deep-learning framework to effectively predict near-future flooding of roads. They tested the framework using three models, and the results showed that it can accurately predict the flooding status of roads with 98% precision and recall values of 96%. Researchers validated the models using the 2017 Hurricane Harvey flooding.

Knowing the flooding status of roads can help affected communities avoid flooded streets and aid emergency management agencies in planning evacuations and delivery of resources.

To read the rest of this article, visit https://today.tamu.edu/2023/06/13/leveraging-big-data-and-ai-for-disaster-resilience-and-recovery/