The CEE Department is pleased to announce Morgan Tholl's MS Thesis Defense: "Learning From Machines: Insights in Forest Transpiration Using Machine Learning Methods"
Date: Thursday, May 26th, 2022
Time: 9:30 - 11:00 AM
Location: This event will be hosted on zoom using the following link: https://pdx.zoom.us/j/84953588518
Advisor: Dr. Samantha Hartzell
Abstract: "Machine learning has been used as a tool to model transpiration for individual locations, 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 to produce (1) transpiration models that can generalize to new locations and (2) insights into the environmental variables that control each cluster. It was found that key predictors of transpiration vary by climate. High performance was achieved for clusters with enough data to define the group, but not for clusters that were too general. Water-limited climates tended to be more controlled by soil water content, whereas climates with high mean annual temperature tended to be more controlled by solar radiation and less dependent on air temperature. By defining which cluster a site fits into, these novel generalized models can be used to predict transpiration and the variables that control it. The predictions provide climate-specific insights into how forests respond to their environment."