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Use Of Machine Learning And Natural Language Processing To Enhance Traffic Safety Analysis, Md Abu Sayed
Use Of Machine Learning And Natural Language Processing To Enhance Traffic Safety Analysis, Md Abu Sayed
Theses and Dissertations
Despite significant advances in vehicle technologies, safety data collection and analysis, and engineering advancements, tens of thousands of Americans die every year in motor vehicle crashes. Alarmingly, the trend of fatal and serious injury crashes appears to be heading in the wrong direction. In 2021, the actual rate of fatalities exceeded the predicted rate. This worrisome trend prompts and necessitates the development of advanced and holistic approaches to determining the causes of a crash (particularly fatal and major injuries). These approaches range from analyzing problems from multiple perspectives, utilizing available data sources, and employing the most suitable tools and technologies …
Forecasting Salinity In The Laguna Madre Using Deep Learning, Martin J. Flores Jr.
Forecasting Salinity In The Laguna Madre Using Deep Learning, Martin J. Flores Jr.
Theses and Dissertations
Salinity is an important metric in the Laguna Madre for establishing the long term health of the local ecological population. By utilizing Deep Learning (DL) techniques, the predicted and forecasted salinity in the Laguna Madre is generated from data provided by the Moderate Resolution Imaging Spectroradiometer (MODIS)-Aqua satellite.
Currently, only one other DL model has been used to forecast Sea Surface Salinity (SSS), being a Recurrent Neural Network (RNN). However, the RNN model requires the prediction of a full area of salinity to function.
As such, several model architectures were tested, with the best one, being a Multi-input MPNN, utilized …
Machine Learning Based Surrogate Model For Hurricane Storm Surge Forecasting In The Laguna Madre, Cesar E. Davila Hernandez
Machine Learning Based Surrogate Model For Hurricane Storm Surge Forecasting In The Laguna Madre, Cesar E. Davila Hernandez
Theses and Dissertations
Texas coastal communities are at constant risk of hurricane impacts every storm season. It is especially important to model and predict storm surge variations during hurricane and storm events. Traditionally, hurricane storm surge predictions have been the result of numerical hydrodynamics based simulations. This type of simulations often requires high amounts of computational resources and complex ocean modelling efforts. Recently, machine learning techniques are being explored and are gaining popularity in hydrologic and ocean engineering modelling fields based on their performance to model nonlinear relationships and low computational requirements for prediction. Advances in machine learning and artificial intelligence (A.I.) demand …