Development of a Neural Network Model to Nowcast/forecast the Coastal Water Level Anomalies on the Entrance to Galveston Bay, Texas
Author: Young Joo Nam
Publisher:
Published: 2002
Total Pages: 156
ISBN-13:
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Author: Young Joo Nam
Publisher:
Published: 2002
Total Pages: 156
ISBN-13:
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Publisher:
Published: 2003
Total Pages: 356
ISBN-13:
DOWNLOAD EBOOK"Education, arts and social sciences, natural and technical sciences in the United States and Canada".
Author: Richard A. Schmalz
Publisher:
Published: 2000
Total Pages: 167
ISBN-13:
DOWNLOAD EBOOKAuthor: Philip H. Richardson
Publisher:
Published: 2000
Total Pages: 56
ISBN-13:
DOWNLOAD EBOOK"A set of four programs has been developed to evaluate nowcast/forecast water levels. These four programs include reform%5Fcoops.f, wl.sigma.pro, and match.event.f ... In chapter 2 descriptions of each program are provided as well as program listings. In chapter 3, program locations and control files as well as sample outputs are given. In chapter 4, recommendations for the operational use of these assessment program [sic] are presented. In addition, possible future enhancements to each program are discussed."--Page 1-2.
Author: Brunello Tirozzi
Publisher: Springer Science & Business Media
Published: 2007-10-12
Total Pages: 181
ISBN-13: 0817644598
DOWNLOAD EBOOKDevoted to the application of neural networks to the concrete problem of time series of sea data Good reference for a diverse audience of grad students, researchers, and practitioners in applied mathematics, data analysis, meteorlogy, hydraulic, civil and marine engineering Methods, models and alogrithms developed in the work are useful for the construction of sea structures, ports, and marine experiments
Author:
Publisher:
Published: 2003
Total Pages: 23
ISBN-13:
DOWNLOAD EBOOKThis paper presents the development of a Regional Neural Network for Water Level (RNN-WL) predictions, with an application to the coastal inlets along the South Shore of Long Island, New York. Long-term water level data at coastal inlets are important for studying coastal hydrodynamics sediment transport. However, it is quite common that long-term water level observations may be not available, due to the high cost of field data monitoring. Fortunately, the US National Oceanographic and Atmospheric Administration (NOAA) has a national network of water level monitoring stations distributed in regional scale that has been operating for several decades. Therefore, it is valuable and cost effective for a coastal engineering study to establish the relationship between water levels at a local station and a NOAA station in the region. Due to the changes of phase and amplitude of water levels over the regional coastal line, it is often difficult to obtain good linear regression relationship between water levels from two different stations. Using neural network offers an effective approach to correlate the nonlinear input and output of water levels by recognizing the historic patterns between them. In this study, the RNN-WL model was developed to enable coastal engineers to predict long-term water levels in a coastal inlet, based on the input of data in a remote NOAA station in the region. The RNN-WL model was developed using a feed-forwards, back-propagation neural network structure with an optimized training algorithm. The RNN-WL model can be trained and verified using two independent data sets of hourly water levels. The RNN-WL model was tested in an application to Long Island South Shore. Located about 60-100 km away from the inlets there are two permanent long-term water level stations, which have been operated by NOAA since the 1940s.
Author: Lun Yin (Ocean engineer)
Publisher:
Published: 2019
Total Pages: 228
ISBN-13: 9781392240243
DOWNLOAD EBOOKAuthor: Fi-John Chang
Publisher: MDPI
Published: 2019-02-28
Total Pages: 376
ISBN-13: 3038975486
DOWNLOAD EBOOKNowadays, the degree and scale of flood hazards has been massively increasing as a result of the changing climate, and large-scale floods jeopardize lives and properties, causing great economic losses, in the inundation-prone areas of the world. Early flood warning systems are promising countermeasures against flood hazards and losses. A collaborative assessment according to multiple disciplines, comprising hydrology, remote sensing, and meteorology, of the magnitude and impacts of flood hazards on inundation areas significantly contributes to model the integrity and precision of flood forecasting. Methodologically oriented countermeasures against flood hazards may involve the forecasting of reservoir inflows, river flows, tropical cyclone tracks, and flooding at different lead times and/or scales. Analyses of impacts, risks, uncertainty, resilience, and scenarios coupled with policy-oriented suggestions will give information for flood hazard mitigation. Emerging advances in computing technologies coupled with big-data mining have boosted data-driven applications, among which Machine Learning technology, with its flexibility and scalability in pattern extraction, has modernized not only scientific thinking but also predictive applications. This book explores recent Machine Learning advances on flood forecast and management in a timely manner and presents interdisciplinary approaches to modelling the complexity of flood hazards-related issues, with contributions to integrative solutions from a local, regional or global perspective.
Author: Itay Rosenzweig
Publisher:
Published: 2011
Total Pages: 82
ISBN-13:
DOWNLOAD EBOOKA new method for automatically integrating the results of hydrodynamic models of currents in Texas bays with the National Oceanic and Atmospheric Administration's (NOAA) in house oil spill trajectory model, the General NOAA Operational Modeling Environment (GNOME), is presented. Oil spill trajectories are predicted by inputting wind and water current forces on an initial spill in a dedicated spill trajectory model. These currents can be field measured, but in most real and meaningful cases, the current field is too spatially complex to measure with any accuracy. Instead, current fields are simulated by hydrodynamic models, whose results must then be coupled with a dedicated spill trajectory model. The newly developed automated approach based on Python scripting eliminates the present labor-intensive practice of manually coupling outputs and inputs of the separate models, which requires expert interpretation and modification of data formats and setup conditions for different models. The integrated system is demonstrated by coupling GNOME independently with TXBLEND -- a 2D depth-averaged model which is currently used by the Texas Water Development Board, and SELFE -- a newer 3D hydrodynamic model with turbulent wind mixing. A hypothetical spill in Galveston Bay is simulated under different conditions using both models, and a brief qualitative comparison of the results is used to raise questions that may be addressed in future work using the automated coupling system to determine the minimum modeling requirements for an advanced oil spill nowcast/forecast platform in Texas bays.
Author: P. Varoonchotikul
Publisher: CRC Press
Published: 2017-10-02
Total Pages:
ISBN-13: 9781138475076
DOWNLOAD EBOOKThis dissertation considers various questions with respect to the effects of salinity on nutrification: what are the main inhibiting factors causing the effects, do all salts have similar effects, what is the maximum acceptable salt level, are ammonia oxidisers or nitrite oxidizers most sensitive to salt stress, can nitrifiers adapt to long term salt stress and are some specific nitrifiers more resistant to salt stress than others? Research was carried out at laboratory scale and in full-scale plants and modelling was employed in both phases to provide a mathematical description for salt inhibition on nitrification and to facilitate the comparison. The result has led to an improved understanding of the effect of salinity on nitrification. The results can be used to improve the sustainability of the exisisting wastewater treatment plants operated under salt stress.