Using Artificial Intelligence to Optimize Wireless Sensor Network Deployments for Sub-alpine Biogeochemical Process Studies

Researchers in the discipline of biogeochemistry face an enormous challenge as they perform carbon cycle studies related to global climate change. These include quantifying energy and element flows through the earth system and coupling these flows to the dynamic climate system with the goal of devising models that can be used to predict how these flows might change in the future. In facing this challenge, researchers must accommodate spatial and temporal heterogeneity at unprecedented scales and confront non-linearities and intermittency of gas transport that renders many earth system processes intractable for existing approaches. As researchers have embraced these challenges, one reality has emerged clearly: satisfactory sampling of complex biogeochemical systems lies beyond the research community’s current observational capabilities (Levin 1992).