HR: 0800h
AN: H41G-0845    [Abstracts]
TI: A New Approach to Liquefaction Potential Mapping Using Remote Sensing and Machine Learning
AU: * OOMMEN, T
EM: thomas.oommen@tufts.edu
AF: 1. Department of Civil and Environmental Engineering, Tufts University, 113 Anderson Hall, Medford, MA 02155, United States
AU: Baise, L G
EM: laurie.baise@tufts.edu
AF: 1. Department of Civil and Environmental Engineering, Tufts University, 113 Anderson Hall, Medford, MA 02155, United States
AB: In order to help communities better plan and mitigate the effects of seismic hazards, it is important to use innovations in science and technology to improve our techniques for mapping the spatial extents of seismic hazards. Earthquake induced ground shaking in areas with saturated sandy soils pose a major threat to communities as a result of the soil liquefaction. Liquefaction is the process of changing a saturated cohesionless soil from a solid to liquid state due to increased pore pressure. Many major earthquakes, especially those in coastal regions, result in liquefaction related ground failures that can lead to infrastructure damage or slope stability issues. Currently liquefaction potential is assessed on two scales: regionally based on surficial geologic unit or locally based on geotechnical sample data. Regional liquefaction potential maps fail to capture the variability of liquefaction potential on the local scale. On the other hand, collection of geotechnical data on the local scale is costly and only done for specific engineering projects and therefore not generally available for regional mapping. Today, the advent of advanced remote sensing products from air and space borne sensors allow us to explore the land surface parameters (geology, moisture content, temperature) at different spatial scales (remote sensor footprint). In this study, we explore the use of satellite based remote sensing data (Landsat 7 ETM+), together with digital elevation model, ground water table, land cover classification, geology, water index and normalized difference vegetation index (NDVI) to characterize the liquefaction potential of northern Monterey and southern Santa Cruz counties in California. A supervised classification of the data into seven classes based on the liquefaction potential map developed by Dupre and Tinsley 1980 was done using Support Vector Machine (SVM). SVM is a machine learning/artificial intelligence algorithm that has the ability to simulate the learning capabilities of a human brain and make appropriate predictions that involve intuitive judgments and a high degree of nonlinearity. The accuracy of the developed liquefaction potential map was tested using independent testing data that was not used for the model development. The results show that the developed liquefaction potential map has an overall classification accuracy of 84%, indicating that the combination of remote sensing data and other relevant spatial data together with machine learning can be a promising approach for liquefaction potential mapping.
DE: 0468 Natural hazards
DE: 0480 Remote sensing
DE: 0555 Neural networks, fuzzy logic, machine learning
DE: 1242 Seismic cycle related deformations (6924, 7209, 7223, 7230)
DE: 1855 Remote sensing (1640)
SC: Hydrology [H]
MN: 2007 Fall Meeting