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Author: tchaddad

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@@ -297,10 +297,9 @@ <h2>Information Theory Part II:</h2><h2>A Terroir of Landslides!</h2>
         <p>RA is a form of machine learning algorithm (MLA) that works exclusively with discrete data, so categorical data such as the variables in the SSURGO soils database, the geologic database (DOGAMI), and land cover (NLCD) can be used as inputs in their native format without conversion to dummy variables, as is typically done in other MLAs.</p>
         <p>For his February demo, Percy will show how these variables are used along with topographic data to model landslide susceptibility in two of the counties ranked highest by FEMA for most landslide hazards: Lincoln and Tillamook. Percy will show that there is evidence to suggest that we can map a Terroir of Landslides, using combinations of soil type, geology, elevation,and terrane group, similar to the combinations of environmental factors that are used to characterize wines, chocolate, and coffee. Results will be presented that show how these data are useful in making hazard maps. </p>
         <p>All of the extraction for analysis was done in QGIS and Python using open source GIS libraries <a href="https://pypi.org/project/shapely/" target="_blank">Shapely</a> and <a href="https://pypi.org/project/rasterio/" target="_blank">Rasterio</a>. Code will be shared and discussed!</p>
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         <p>Zoom link for remote participants: <a href="https://us02web.zoom.us/j/84354140386?pwd=g3gZCAjEZfWxWXFevoizOovOgDizuJ.1" target="_blank">https://us02web.zoom.us/j/84354140386?pwd=g3gZCAjEZfWxWXFevoizOovOgDizuJ.1</a> </p>
-        <p>Meeting ID: 843 5414 0386</p>
-        <p>Passcode: 166107</p>
+        <p>Meeting ID: 843 5414 0386 <br/>Passcode: 166107</p>
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