<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>Gbm on CRC Earth Analytics</title><link>http://www.crceanalytics.com/tags/gbm/</link><description>Recent content in Gbm on CRC Earth Analytics</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Fri, 30 Sep 2022 00:00:00 +0000</lastBuildDate><atom:link href="http://www.crceanalytics.com/tags/gbm/index.xml" rel="self" type="application/rss+xml"/><item><title>Machine Learning for Snow Hydrology - A Follow Up</title><link>http://www.crceanalytics.com/posts/machine-learning-for-snow-hydrology-a-follow-up/</link><pubDate>Fri, 30 Sep 2022 00:00:00 +0000</pubDate><guid>http://www.crceanalytics.com/posts/machine-learning-for-snow-hydrology-a-follow-up/</guid><description>&lt;h3 id="overview">Overview&lt;/h3>
&lt;p>Last winter I tried my hand at competing in a machine learning competition to predict snow water equivalent (SWE) across the Western United States. I learned a lot and created a two part blog series to document both the competition and my approach:&lt;/p>
&lt;ul>
&lt;li>&lt;a href="https://crceanalytics.com/2022/04/07/machine-learning-for-snow-hydrology-a-competition/">Machine Learning for Snow Hydrology - A Competition&lt;/a>&lt;/li>
&lt;li>&lt;a href="https://crceanalytics.com/2022/05/11/machine-learning-for-snow-hydrology-methods/">Machine Learning for Snow Hydrology - Methods&lt;/a>&lt;/li>
&lt;/ul>
&lt;p>I competed in the preliminary phase of the competition that didn’t include any prizes. The second phase involved predicting SWE in real time and included big prizes totaling $500,000. The competition ended in early summer, but the winners were just recently announced on the DrivenData Blog:&lt;/p></description></item></channel></rss>