Building safer cities with AI: Machine learning model enhances urban resilience against liquefaction

In earthquake-prone areas like Japan, there is a need for better prediction of soil stability to mitigate liquefaction risks. Towards this end, researchers have used machine learning models, including artificial neural networks and bagging techniques, to create accurate 3D maps of bearing layers using data from 433 locations in Setagaya, Tokyo. This approach can identify stable construction sites, enhance disaster planning, and contribute to safer urban development, making cities more resilient to liquefaction risks.