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Risk Assessment for Landslides Using Bayesian Networks and Remote Sensing

September 07, 2017
3:00 pm - 4:00 pm

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Patricia Varela Gonzalez
Department of Civil Engineering, Texas A&M University

Graduate Student


The use of land-­based data processing capabilities and analysis has improved due to an increase of publicly available datasets with more spatial coverage, finer resolution, and better accuracy. LiDAR derived information such as a Digital Terrain Model (DTM) and a Canopy Height Model (CHM) from a selected area of the Oregon Coast Range was used to develop a set of hazard and risk index maps. The manipulation of these models resulted in three maps named ‘Physical Model’, ‘Vegetation Density’ and “Wetness Index” that were combined with an existing landslide susceptibility map known as ‘SLIDO’. These maps served as input to a Bayesian Network capable of assessing the state of risk of slope failure in “Prognosis” and determining required conditions to achieve a prescribed risk condition in “Diagnosis”. In this work is discussed that the combination of Bayesian networks, GIS and risk assessment allows stakeholders to perform improved, informed and systematic decision making.


Patricia Varela is a Geological Engineer and a research assistant from the Geotechnical Engineering Division at Texas A&M University. She is a PhD. candidate and member of the Stochas5c Geomechanics Laboratory whose primary mission is the use of probabilis5c modeling for risk assessment and informed decision-­‐making. Patricia has conducted research on the integra5on of GIS and Bayesian Networks as a tool for environmental, social and economic risk assessment. Other interests include integrated geosciences and engineering, remote sensing, and geosta5s5cs for improved policy-­‐making.


Dr. Michael Bishop: michael.bishop@tamu.edu


September 7, 2017
3:00 pm - 4:00 pm


Eller O&M Bldg. Rm 807