LPI Prediction and Map Expression Using Deep Learning Techniques

In South Korea, earthquakes of magnitude 4.0 and above continue to occur, yet there remains a lack of awareness regarding liquefaction caused by these seismic events.In this context, evaluating liquefaction during seismic events, using geotechnical information from the National Ground Information Portal System consumes considerable human and temporal resources to derive the Liquefaction Potential Index (LPI).Additionally, there are issues concerning the accuracy of the input data.This study designed an LPI prediction model to address these challenges by comparing the calculated LPI with a reference map indicating liquefaction susceptibility.The results showed that while the LPI prediction model requires some improvement in accuracy compared to the actual LPI, it demonstrated high effectiveness in rapidly selecting ground investigation locations during national disaster situations.