Open Access. Powered by Scholars. Published by Universities.®
- Keyword
-
- Water quality (2)
- Aerosol (1)
- Air pollution (1)
- Artificial Neural Network (1)
- Artificial Neural Networks (1)
-
- Atchafalaya River (1)
- Bottom-up Cracking (1)
- Cement-Treated Reflective Cracking (1)
- Chemical transport models. (1)
- Coastal deltaic floodplains (1)
- Convolutional Neural Network (1)
- Damage detection (1)
- Element sizing (1)
- FE model updating (1)
- Hydrodynamic modeling (1)
- Hydrology (1)
- Hydroperiod (1)
- Image processing (1)
- Machine learning (1)
- Meteorology (1)
- Mississippi River (1)
- Numerical modeling (1)
- Old River Control Structure (1)
- Pattern recognition (1)
- Physics guidance (1)
- Prediction Model (1)
- Random Forests (1)
- Remote Sensing (1)
- River Deltas (1)
- River Diversions (1)
Articles 1 - 7 of 7
Full-Text Articles in Engineering
Development Of Artificial Intelligence Approach To Nowcasting And Forecasting Vibrio Prevalence In Coastal Waters, Peyman Hosseinzadeh Namadi
Development Of Artificial Intelligence Approach To Nowcasting And Forecasting Vibrio Prevalence In Coastal Waters, Peyman Hosseinzadeh Namadi
LSU Doctoral Dissertations
Vibrio parahaemolyticus (V.p) is an epidemiologically significant pathogen that poses high risks to the human health and shellfish industry, calling for predictive models for management interventions. This study presents an Artificial Intelligence(AI)-based approach to predicting and reducing the risks. The AI-based approach involves the identification of environmental indicators and their optimum variation ranges favoring V.p prevalence, the development of nowcasting and forecasting models for predicting V.p prevalence, and the creation of remote sensing algorithms for mapping concentrations of V.p and its environmental indicators by synergistically combining the Deep Neural Network (DNN) modeling technique, Genetic Programming (GP) method, R …
Data-Driven And Model-Based Methods With Physics-Guided Machine Learning For Damage Identification, Zhiming Zhang
Data-Driven And Model-Based Methods With Physics-Guided Machine Learning For Damage Identification, Zhiming Zhang
LSU Doctoral Dissertations
Structural health monitoring (SHM) has been widely used for structural damage diagnosis and prognosis of a wide range of civil, mechanical, and aerospace structures. SHM methods are generally divided into two categories: (1) model-based methods; (2) data-driven methods. Compared with data-driven SHM, model-based methods provide an updated physics-based numerical model that can be used for damage prognosis when long-term data is available. However, the performance of model-based methods is susceptible to modeling error in establishing the numerical model, which is usually unavoidable due to model simplification and omission. The major challenge of data-driven SHM methods lies in data insufficiency, e.g., …
Identification Of Top-Down, Bottom-Up, And Cement-Treated Reflective Cracks Using Convolutional Neural Network And Artificial Neural Network, Nirmal Dhakal
LSU Doctoral Dissertations
The objective of this study was to formulate a Convolutional Neural Networks (CNN) model and to develop a decision-making tool using Artificial Neural Networks (ANN) to identify top-down, bottom-up, and cement treated (CT) reflective cracking in in-service flexible pavements. The CNN’s architecture consisted of five convolutional layers with three max-pooling layers and three fully connected layers. Input variables for the ANN model were pavement age, asphalt concrete (AC) thickness, annual average daily traffic (AADT), type of base, crack orientation, and crack location. The ANN network architecture consisted of an input layer of six neurons, a hidden layer of ten neurons, …
Understanding Air Pollutants And Meteorology Interactions Using Chemical Transport Models, Pengfei Wang
Understanding Air Pollutants And Meteorology Interactions Using Chemical Transport Models, Pengfei Wang
LSU Doctoral Dissertations
Air pollution is a worldwide threat to human health and ecosystems, especially in developing countries. After being emitted to the atmosphere, air pollutant concentrations are determined by chemical and physical processes including transport, transformation, and deposition, which are largely affected by meteorological variations. In turn, pollutants such as fine particulate matter (PM2.5) affect meteorology by impacting solar radiation and cloud condensation processes. Thus, it is important and necessary to understand the interactions between air pollutants and meteorology for better designing effective air pollution control strategies and forecasting weather. In this study, two chemical transport models (CTMs) are applied …
Physical And Biological Factors Controlling The Fate Of Nitrate In A Louisiana Coastal Deltaic Floodplain, Alexandra Christensen
Physical And Biological Factors Controlling The Fate Of Nitrate In A Louisiana Coastal Deltaic Floodplain, Alexandra Christensen
LSU Doctoral Dissertations
The Mississippi River Delta is threatened by a growing pressure to support large human populations in the United States both with food production, navigation systems, and urban development in the Mississippi River Basin. Nitrate-nitrogen load in the Mississippi River, up to 100 Tg N yr-1 from agricultural and urban runoff, leads to phytoplankton blooms and hypoxia across the Louisiana continental shelf, creating dead zones of low dissolved oxygen threatening a significant commercial fishery. Along the coast and river corridors, floodplain ecosystems have the capacity to retain and remove nitrate. This dissertation explores the role of productive, actively growing coastal …
The Long-Term Outlook Of The Mississippi-Atchafalaya Bifurcation: A Convergence Of Engineering, Economics, And Deltaic Evolution, Thomas Mitchell Andrus
The Long-Term Outlook Of The Mississippi-Atchafalaya Bifurcation: A Convergence Of Engineering, Economics, And Deltaic Evolution, Thomas Mitchell Andrus
LSU Doctoral Dissertations
The most recent and currently active delta lobe of the Mississippi River (MR) is the Atchafalaya-Wax Lake lobe, which was initiated approximately 400 years ago as a result of MR stream capture by the Atchafalaya River (AR). This capture process accelerated in the early to mid-1900s but further progress was prevented by construction and operation of the Old River Control Structure (ORCS) Complex. Many recent studies indicate that MR system below the ORCS is on a retreating geologic trajectory due to contributing factors such as sea level rise, subsidence, faulting, and declining hydraulic stream power. Diversions along the Lower MR …
Automatic Shoreline Digitization And Mesh Element Sizing For Hydrodynamic Modeling, Henok Kefelegn
Automatic Shoreline Digitization And Mesh Element Sizing For Hydrodynamic Modeling, Henok Kefelegn
LSU Doctoral Dissertations
The first and most critical step in any coastal hydrodynamics and transport process modeling is identifying land-water boundaries. In a coastal wetland, this has always been a challenge due to the complexity of the wetland and lack of efficient methods, calling for efficient and effective methods to extract and digitize the shorelines. While coastline feature extraction has been increasingly researched, its application in hydrodynamic and environmental modeling, without morphological adjustment, remains limited and suboptimal. Further, there has been a paucity of cost-effective, contextually adaptive and high-quality methods to generate meshes, especially for coastal hydrodynamic modeling. This study has developed and …