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<span style="font-size:14pt"></span><span style="font-size:13pt"><b>Mapping Landslide Susceptibility Over Large Regions With Sparse Data - A Reality Check</b></span><br>
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<span style="font-size:12pt"><b>Speaker: </b>Jacob Woodard</span><span style="font-size:14pt"><b><br>
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<span style="font-size:12pt"> USGS GHSC</span></div>
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<span style="font-size:12pt"><b>Location:</b> online/virtual only</span></div>
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<span style="font-size:12pt"><b>Date/Time: </b>Tuesday, Sept 13, 2022 10-11am MDT</span></div>
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<span style="font-size:12pt"><b>Abstract: </b>Landslide susceptibility maps indicate the spatial distribution of future and existing landslide likelihood. Modeling landslide susceptibility over large and/or diverse terrains remains a challenge due to the sparsity
of data and the variability in triggering conditions. Several approaches are used to mitigate these challenges including extending statistical models developed in data-rich regions to data-poor regions, training models on sparse but well-distributed landslide-inventory
data, and restricting model training and application to regions with similar environmental attributes. However, to our knowledge, no study has attempted to systematically evaluate the effectiveness of these methods. Here we introduce a new statistical framework
that facilitates comparisons between different statistical susceptibility models. We use this framework to evaluate a set of models trained on landslide inventories collected at sub-regional scales over diverse terrains across the United States. Results show
significant variations in predictor effects (i.e., how a predictor value influences the susceptibility output) depending on the inventory used to train the models. The inconsistent predictor effects cause low accuracies when testing models on inventories outside
the domain of the training data. Grouping test and training sets according to physiographic and ecological characteristics does not mitigate these challenges. We also show that using training data distributed across of the entire modeling domain can effectively
characterize susceptibility. This study highlights limitations of present landslide inventories, and their spatial coverage.</span></div>
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<span style="font-size:12pt"><b>Bio: <span style="font-weight:400; font-size:12pt; margin:0px; background-color:rgb(255,255,255)"><span style="margin:0px">Jacob Woodard is a Mendenhall fellow working on methods for improving national-scale landslide susceptibility
maps for the United States. Before coming to the USGS he received his M.S. and Ph.D. from the University of Wisconsin-Madison where he studied glaciology. He also received his B.S. in Geology from Brigham Young University. His research interests lay in applying
statistical, numerical and analytical models to better understand landslide and glacial processes.</span></span><br class="x_x_Apple-interchange-newline">
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<span style="font-size:12pt"><b><span style="font-weight:400; margin:0px; text-align:left; background-color:rgb(255,255,255)"><span style="font-size:10pt; font-family:Calibri,sans-serif; margin:0px; color:rgb(32,31,30)">GHSC Seminar Committee: Andrea Llenos
<allenos@usgs.gov>, Benjamin Murphy <bmurphy@usgs.gov>, Lauren Schaefer <lschaefer@usgs.gov></span></span><br class="x_x_x_Apple-interchange-newline">
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