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Articles 1 - 7 of 7
Full-Text Articles in Physical Sciences and Mathematics
Invasive Buckthorn Mapping: A Uav-Based Approach Utilizing Machine Learning, Gis, And Remote Sensing Techniques In The Upper Peninsula Of Michigan, Vikranth Madeppa
Invasive Buckthorn Mapping: A Uav-Based Approach Utilizing Machine Learning, Gis, And Remote Sensing Techniques In The Upper Peninsula Of Michigan, Vikranth Madeppa
Dissertations, Master's Theses and Master's Reports
An Invasive species is a species that is alien or non-native to the ecosystem which causes harm to economic, environmental, or human health (E.O. 13112 of Feb 3, 1999). Invasive species have posed a serious threat to ecosystems across the globe. These invasive species have impacts on the biodiversity and productivity of invaded forests. Remotely sensed data is a valuable resource for understanding and addressing issues related to invasive species. This study presents a novel approach for mapping the distribution of two invasive plant species, Common and Glossy Buckthorn, using unmanned aerial vehicles (UAVs), machine learning algorithms, geographic information systems …
Comprehensive Analysis Of Seismic Signals From Pacaya Volcano Using Deep Learning Event Detection, Jessica L. Devlieg
Comprehensive Analysis Of Seismic Signals From Pacaya Volcano Using Deep Learning Event Detection, Jessica L. Devlieg
Dissertations, Master's Theses and Master's Reports
Pacaya volcano located 30 km SW of Guatemala City, Guatemala, has been erupting intermittently since 1961. Monitoring of seismicity is crucial to understanding current activity levels within Pacaya. Traditional methods of picking these small earthquakes in this noisy environment are imprecise. Pacaya produces many small events that can easily blend in with the background noise. A possible solution for this problem is a machine learning program to pick first arrivals for these earthquakes. We tested a deep learning algorithm (Mousavi et al., 2020) for fast and reliable seismic signal detection within a volcanic system. Data from multiple deployments were used, …
A Surrogate Model Of Molecular Dynamics Simulations For Polar Fluids: Supervised Learning Methods For Molecular Polarization And Unsupervised Methods For Phase Classification, Zackerie W. Hjorth
A Surrogate Model Of Molecular Dynamics Simulations For Polar Fluids: Supervised Learning Methods For Molecular Polarization And Unsupervised Methods For Phase Classification, Zackerie W. Hjorth
Dissertations, Master's Theses and Master's Reports
Molecular Dynamic (MD) simulation is a standard computational tool in soft matter physics. While very powerful, it is computationally expensive, leading to some simulations taking days or even weeks to complete depending on the size of your computer cluster. Finding computationally cheap surrogate models which can learn the output features of MD simulation is therefore highly motivated. In this report I explore the use of deep neural network ensembles as well as support vector machine regressors as surrogate models for MD simulation. From the output of the surrogate models, we can then employ unsupervised learning methods to get insight into …
Fine Scale Mapping Of Laurentian Mixed Forest Natural Habitat Communities Using Multispectral Naip And Uav Datasets Combined With Machine Learning Methods, Parth P. Bhatt
Dissertations, Master's Theses and Master's Reports
Natural habitat communities are an important element of any forest ecosystem. Mapping and monitoring Laurentian Mixed Forest natural communities using high spatial resolution imagery is vital for management and conservation purposes. This study developed integrated spatial, spectral and Machine Learning (ML) approaches for mapping complex vegetation communities. The study utilized ultra-high and high spatial resolution National Agriculture Imagery Program (NAIP) and Unmanned Aerial Vehicle (UAV) datasets, and Digital Elevation Model (DEM). Complex natural vegetation community habitats in the Laurentian Mixed Forest of the Upper Midwest. A detailed workflow is presented to effectively process UAV imageries in a dense forest environment …
Machine Learning-Driven Surrogate Models For Electrolytes, Tong Gao
Machine Learning-Driven Surrogate Models For Electrolytes, Tong Gao
Dissertations, Master's Theses and Master's Reports
We have developed a lattice Monte Carlo (MC) simulation based on the diffusion-limited aggregation model that accounts for the effect of the physical properties of ionic liquids (ILs) on lithium dendrite growth. Our simulations show that the size asymmetry between the cation and anion, the dielectric constant, and the volume fraction of ILs are critical factors to significantly suppress the dendrite growth, primarily due to substantial changes in electric-field screening. Specifically, the volume fraction of ILs has the optimal value for dendrite suppression. The present simulation method indicates potential challenges for the model extension to macroscopic systems. Therefore, we also …
Using Text Mining And Machine Learning Classifiers To Analyze Stack Overflow, Taylor Morris
Using Text Mining And Machine Learning Classifiers To Analyze Stack Overflow, Taylor Morris
Dissertations, Master's Theses and Master's Reports
StackOverflow is an extensively used platform for programming questions. In this report, text mining and machine learning classifiers such as decision trees and Naive Bayes are used to evaluate whether a given question posted on StackOverflow will be closed or answered. While multiple models were used in the analysis, the performance for the models was no better than the majority classifier. Future work to develop better performing classifiers to understand why a question is closed or answered will require additional natural language processing or methods to address the imbalanced data.
Volcan De Fuego: A Machine Learning Approach In Understanding The Eruptive Cycles Using Precursory Tilt Signals, Kay Sivaraj
Volcan De Fuego: A Machine Learning Approach In Understanding The Eruptive Cycles Using Precursory Tilt Signals, Kay Sivaraj
Dissertations, Master's Theses and Master's Reports
Volcan de Fuego is an active stratovolcano located in the Central Guatemalan segment of the 1100 m long Central America Volcanic Arc System (CAVAS). Fuego-Acatenango massif consists of at least four major vents of which the Fuego summit vent is the most active and the youngest member. The volcano exhibits primarily Strombolian and Vulcanian behavior along with occasional paroxysms and pyroclastic flows. Historically, Fuego has produced basaltic-andesitic rocks with more recent eruptions progressively trending towards maficity. Several studies have used short-term deployments of broadband seismometers, infrasound, and long-term remote sensing techniques to characterize the mechanism of Fuego. In our study, …