Artificial Intelligence Techniques
for Modeling Natural Processes

"Artificial intelligence" refers to a broad category of computer-based techniques that can recognize and reproduce subtle patterns in large data sets.  With the increase in automated data gathering systems (for example, the Texas Coastal Ocean Observation Network), these techniques have become increasingly popular with scientists looking to find new insights about natural processes from the flood of numbers.  Artificial intelligence methods often have biologically-derived names, such as "neural networks", "genetic algorithms", and "random forests".

A group of mathematicians and natural scientists at Texas A&M University-Corpus Christi have been using AI methods, combined with classical statistical tools, to create mathematical models of natural processes from the Texas Coast and the Gulf of Mexico.  Recent examples include improved 24 and 48 hour forecasts of water levels in ship channels up and down the Texas Coast and predicting when cold fronts will cause fish kills in the Laguna Madre.  Ongoing work includes attempts to better forecast of coastal flooding from hurricanes and predicting when fecal contamination levels of coastal waters exceed standards.

For more information, contact Philippe Tissot, Beate Zimmer, Alex Sadovski, or Blair Sterba-Boatwright.