Physical Sciences and Mathematics Commons^™

On The Use Of Machine Learning For Causal Inference In Extreme Weather Events, Yuzhe Wang

Discovery Undergraduate Interdisciplinary Research Internship

Machine learning has become a helpful tool for analyzing data, and causal Inference is a powerful method in machine learning that can be used to determine the causal relationship in data. In atmospheric and climate science, this technology can also be applied to predicting extreme weather events. One of the causal inference models is Granger causality, which is used in this project. Granger causality is a statistical test for identifying whether one time series is helpful in forecasting the other time series. In granger causality, if a variable X granger-causes Y: it means that by using all information without …

Glacier Segmentation From Remote Sensing Imagery Using Deep Learning, Bibek Aryal

Open Access Theses & Dissertations

Large-scale study of glaciers improves our understanding of global glacier change and is imperative for monitoring the ecological environment, preventing disasters, and studying the effects of global climate change. In recent years, remote sensing imagery has been preferred over riskier and resource-intensive field visits for tracking landscape level changes like glaciers. However, periodic manual labeling of glaciers over a large area is not feasible due to the considerable amount of time it requires while automatic segmentation of glaciers has its own set of challenges. Our work aims to study the challenges associated with segmentation of glaciers from remote sensing imagery …

Prediction Of Soil Water Content And Electrical Conductivity Using Random Forest Methods With Uav Multispectral And Ground-Coupled Geophysical Data, Yunyi Guan, Katherine R. Grote, Joel Schott, Kelsi Leverett

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

The volumetric water content (VWC) of soil is a critical parameter in agriculture, as VWC strongly influences crop yield, provides nutrients to plants, and maintains the microbes that are needed for the biological health of the soil. Measuring VWC is difficult, as it is spatially and tempo-rally heterogeneous, and most agricultural producers use point measurements that cannot fully capture this parameter. Electrical conductivity (EC) is another soil parameter that is useful in agricul-ture, since it can be used to indicate soil salinity, soil texture, and plant nutrient availability. Soil EC is also very heterogeneous; measuring EC using conventional soil sampling …

Application Of Machine Learning In Geophysics: Ranking Teleseismic Shear Wave Splitting Measurements And Classifying Different Types Of Earthquakes, Yanwei Zhang

Doctoral Dissertations

"During the past decades, applications of Machine Learning have been explosively developed to solve various academic and industrial problems, and over-human performance has been shown in diverse areas. In geophysical research, Machine Learning, especially Convolutional Neural Network (CNN), has been applied in numerous studies and demonstrated considerable potential. In this study, we applied CNN to solve two geophysical problems, ranking teleseismic shear splitting (SWS) measurements and classifying different types of earthquakes.

For ranking teleseismic SWS measurements, we utilized a CNN-based method to automatically select reliable SWS measurements. The CNN was trained by human-verified teleseismic SWS measurements and tested using synthetic …

The Burning Bush: Linking Lidar-Derived Shrub Architecture To Flammability, Michelle S. Bester

Graduate Theses, Dissertations, and Problem Reports

Light detection and ranging (LiDAR) and terrestrial laser scanning (TLS) sensors are powerful tools for characterizing vegetation structure and for constructing three-dimensional (3D) models of trees, also known as quantitative structural models (QSM). 3D models and structural traits derived from them provide valuable information for biodiversity conservation, forest management, and fire behavior modeling. However, vegetation studies and 3D modeling methodologies often only focus on the forest canopy, with little attention given to understory vegetation. In particular, 3D structural information of shrubs is limited or not included in fire behavior models. Yet, understory vegetation is an important component of forested ecosystems, …