Added application to machinelearning.html

machinelearning.html

@@ -96,8 +96,37 @@ <h2>Advantages over traditional statistical approaches</h2>
 										a larger percentage of the relationships found in the noise than the traditional models, especially for highly complex problems like climate models. All 
 										of these traits combined mean that 'black box' machine learning technique could offer a more rapid means of understanding environmental problems without 
 										the need to have a complete theoretical grasp of the systems (Lary et al., 2016). That is, it could make better predictions because it can discern the 
-										unknown relationships from the data.  
-</p>
+										unknown relationships from the data. </p>
+
+									<h2>A need in polar systems</h2>
+									<p>In glaciology, modeling outlet glacier dynamical responses to contemporary environmental changes has proven to be a challenge using traditional numerical 
+										methods, but data-driven machine learning techniques could enhance that learning curve. Glacier systems are complex, highly variable, and require longer 
+										time series than are sometimes available to determine the physics behind some of the more rapid changes taking place in the last two decades. Often there 
+										are no direct observations of critical processes because these systems exist below a kilometer or two of ice in hard to reach and remote locations of the 
+										world. To model these systems using traditional methods requires complex and computationally expensive numerical methods. For these reasons, our knowledge 
+										of the physics is still incomplete and physics-based models have not yet been able to adequately reproduce <i>in situ</i>i> observations (Joughin et al., 2012). 
+										These models have been advancing rapidly over the last two decades, but have not yet been incorporated into the complex climate models that are 
+										traditionally used to predict major climate changes in the foreseeable future making an accurate estimation of sea level change challenging. Estimates 
+										from sea level contributions from the ice sheets vary widely, causing sea level rise predictions to also vary widely from 0.3 to 2.5 m by 2010 (Jevrejeva et al., 2014; 
+										DeConto and Pollard, 2016). Adequate coastal planning cannot be achieved with that scope of uncertainty. 
+
+										<b>Where can machine learning help?</b> While complex climate models that include ice sheet variability within them may be several years out, we may be able to 
+										create climate forecasts that are more accurate, with a fraction of the number of lines of code, in a very short amount of time, and with limited knowledge 
+										of the systems. Machine learning often performs better for forecasting highly complex systems, no matter how much of a theoretical understanding of the 
+										system was captured in the traditional model; that complex of a system is impossible to model perfectly via numerical methods because of its scope. 
+										Alternatively, machine learning can be used as emulators to improve the performance of physics-based models. Emulators are simplified models that mimic the behavior  
+										of more complex climate models. They can be used to emulate a piece of a physics-based model to speed up and simplify the overall model.</p>
+
+									<h2>Our work: Image classification in polar regions</h2>
+									<p>When we use a satellite image, we are often only interested in making measurements from certain kinds of surfaces. For example, I am often interested in measuring sea 
+										surface temperatures, which requires that I accurately know where the ocean exists in an image and that I exclude temperatures over ice or cloud. Classifying 
+										satellite images of polar regions is a particularly challenging problem, though, because different kinds of surfaces can look really similar. Ocean can be hard to  
+										distinguish from bare land in the polar regions because both are dark and can have relatively warm temperatures compared to ice. Even more problematic is the identification 
+										of clouds because they can be thin and nearly transparent, therefore looking like the surface they cover, or can be white or gray like ice. 
+
+										Appearing to be similar to our eye means that in an image, they may have similar spectral properties and therefore be hard to mathamatically tell apart using a 
+										computer and traditional statitistical methods. That is, in the electromagnetic spectrum, they may look the same in different wavelengths that represent the 
+										visible spectrum (i.e. the colors of the rainbow that we can see by eye), near infrared, shortwave infrared, or thermal infrared (heat). Still under construction....
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 										<h4>Heading with a Subtitle</h4>