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Full-Text Articles in Engineering
Learning From Machines: Insights In Forest Transpiration Using Machine Learning Methods, Morgan Tholl
Learning From Machines: Insights In Forest Transpiration Using Machine Learning Methods, Morgan Tholl
Dissertations and Theses
Machine learning has been used as a tool to model transpiration for individual sites, but few models are capable of generalizing to new locations without calibration to site data. Using the global SAPFLUXNET database, 95 tree sap flow data sites were grouped using three clustering strategies: by biome, by tree functional type, and through use of a k-means unsupervised clustering algorithm. Two supervised machine learning algorithms, a random forest algorithm and a neural network algorithm, were used to build machine learning models that predicted transpiration for each cluster. The performance and feature importance in each model were analyzed and compared …
Understanding Ch4 Emissions From Compostables: An Exploration Of Local Ch4 Emissions From Landfilled Compostables And The Efficacy Of Emission Mitigation Via Anaerobic Biogas Digestion, Jordan-Yoosuf Aljbour
Understanding Ch4 Emissions From Compostables: An Exploration Of Local Ch4 Emissions From Landfilled Compostables And The Efficacy Of Emission Mitigation Via Anaerobic Biogas Digestion, Jordan-Yoosuf Aljbour
University Honors Theses
Methane (CH4) is the second most abundant anthropogenic greenhouse gas within the atmosphere, comprising ~16% of the total anthropogenic greenhouse gas composition on Earth. It has an ~12-year lifetime relative to its eventual oxidation via reaction with tropospheric hydroxyl radicals (OH), and has a 100-year indirect global warming potential (GWP) approximately ranging between 28-36 [Environmental Protection Agency, 2021]. In recent years, the observed average global concentration of atmospheric CH4 has increased by ~11.0% from 2020 (~15.3 ppb) to 2021 (~17.0 ppb) [Dlugokencky et al., 1994; National Oceanic and Atmospheric Administration, 2022]. With …