Implementation of a Regional Wave Measurement and Modeling System, South Shore of Long Island, New York

Grosskopf, William G.; Kraus, Nicholas C.; Militello, Adele; Bocamazo, Lynn

doi:10.1061/40604(273)63

Cited by 3 publications

(3 citation statements)

References 3 publications

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“…Sediment deposition at the inlet requires dredging to maintain sufficient depth for safe navigation. According to Grosskopf et al (2001) and Rosati et al (1999), annual average dredging volume is approximate 100 000 m 3 in Shinnecock. The US Army Corps of Engineers (USACE), New York District and the State of New York operate a regional sediment management program for the South Shore, which integrates operation cost by linking dredging, sand bypassing, breach-contingency plans, and protection of beaches vulnerable to storm erosion.…”

Section: Study Site and Data Setsmentioning

confidence: 97%

Multiple‐station neural network for modelling tidal currents across Shinnecock inlet, USA

Huang

Murray

2007

Hydrological Processes

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Abstract:This paper presents the development of a multiple-station neural network for predicting tidal currents across a coastal inlet. Unlike traditional hydrodynamic models, the neural network model does not need inputs of coastal topography and bathymetry, grids, surface and bottom frictions, and turbulent eddy viscosity. Without solving hydrodynamic equations, the neural network model applies an interconnected neural network to correlate the inputs of boundary forcing of water levels at a remote station to the outputs of tidal currents at multiple stations across a local coastal inlet. Coefficients in the neural network model are trained using a continuous dataset consisting of inputs of water levels at a remote station and outputs of tidal currents at the inlet, and verified using another independent input and output dataset. Once the neural network model has been satisfactorily trained and verified, it can be used to predict tidal currents at a coastal inlet from the inputs of water levels at a remote station. For the case study at Shinnecock Inlet in the southern shore of New York, tidal currents at nine stations across the inlet were predicted by the neural network model using water level data located from a station about 70 km away from the inlet. A continuous dataset in May 2000 was used for the training, and another dataset in July 2000 was used for the verification of the neural network model. Comparing model predictions and observations indicates correlation coefficients range from 0Ð95 to 0Ð98, and the root-mean-square error ranges from 0Ð04 to 0Ð08 m s 1 at the nine current locations across the inlet.

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Section: Study Site and Data Setsmentioning

confidence: 97%

Multiple‐station neural network for modelling tidal currents across Shinnecock inlet, USA

Huang

Murray

2007

Hydrological Processes

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“…STWAVE was applied locally to Shinnecock Inlet, New York, to simulate wave driven currents and transport (Buonaiuto, 2003;Buonaiuto and Militello, 2003). STWAVE was also used to simulate nearshore wave conditions for a regional modeling system for the south shore of Long Island (Grosskopf et al, 2000). The half plane version of STWAVE was used for this investigation, which limits the direction of wave propagation from offshore to nearshore (approximate shore-normal directions).…”

Section: Introductionmentioning

confidence: 99%

Wave Modeling of Long Island Coastal Waters

Buonaiuto

Slattery

Bokuniewicz

2011

Journal of Coastal Research

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“…In the technologically advanced countries, particularly the USA and the countries of Western Europe, the need for monitoring the parameters related to qualitative environmental characteristics and especially to the quality of water has been recognized for a long time. With this aim, several programmes of automatic measurement of qualitative characteristics and analysis of results have been installed and placed in operation (Gross Kopf et al , 2001).…”

Section: Introductionmentioning

confidence: 99%

Monitoring water quality through a telematic sensor network and a fuzzy expert system

Hatzikos¹,

Bassiliades²,

Asmanis³

et al. 2007

Expert Systems

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In this paper we present an expert system that monitors seawater quality and pollution in northern Greece through a sensor network called Andromeda. The expert system monitors sensor data collected by local monitoring stations and reasons about the current level of water suitability for various aquatic uses, such as swimming and piscicultures. The aim of the expert system is to help the authorities in the decision-making process in the battle against pollution of the aquatic environment, which is vital for public health and the economy of northern Greece. The expert system determines, using fuzzy logic, when certain environmental parameters exceed certain pollution limits, which are specified either by the authorities or by environmental scientists, and flags up appropriate alerts.

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Implementation of a Regional Wave Measurement and Modeling System, South Shore of Long Island, New York

Cited by 3 publications

References 3 publications

Multiple‐station neural network for modelling tidal currents across Shinnecock inlet, USA

Multiple‐station neural network for modelling tidal currents across Shinnecock inlet, USA

Wave Modeling of Long Island Coastal Waters

Monitoring water quality through a telematic sensor network and a fuzzy expert system

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