Development of an Atlantic Canadian Coastal Water Level Neural Network Model

2008 ◽  
Vol 25 (11) ◽  
pp. 2117-2132 ◽  
Author(s):  
Guoqi Han ◽  
Yu Shi
Keyword(s):  
Neural Network ◽  
Water Level ◽  
Sea Level ◽  
Coastal Water ◽  
Water Levels ◽  
Atlantic Canada ◽  
Level Data ◽  
Coastal Water Level ◽  
Term Data

Abstract Coastal water-level information is essential for coastal zone management, navigation, and oceanographic research. However, long-term water-level observations are usually only available at a limited number of locations. This study discusses a complementary and simple neural network (NN) approach, to predict water levels at a specified coastal site from the data gathered at other nearby or remote permanent stations. A simple three-layer, feed-forward, back-propagation network and a neural network ensemble, named Atlantic Canadian Coastal Water Level Neural Network (ACCSLENNT) models, was developed to correlate the nonlinear relationship of sea level data among stations by learning from their historical characteristics. Instantaneous hourly observations of water level from five stations along the coast of Atlantic Canada—Argentia, Belledune, Halifax, North Sydney, and St. John’s—are used to formulate and validate the ACCSLENNT models. Qualitative and quantitative comparisons of the network output with target observations showed that despite significant changes in sea level amplitudes and phases in the study area, appropriately trained NN models could provide accurate and robust long-term predictions of both tidal and nontidal (tide subtracted) water levels when only short-term data are available. The robust results indicate that the NN models in conjunction with limited permanent stations are able to supplement long-term historical water-level data along the Atlantic Canadian coast. Because field data collection is usually expensive, the ACCSLENNT models provide a cost-effective alternative to obtain long-term data along Atlantic Canada.

scholarly journals Evolution of extreme Total Water Levels along the northern coast of the Iberian Peninsula

2011 ◽  
Vol 11 (2) ◽  
pp. 613-625 ◽  
Author(s):  
D. F. Rasilla Álvarez ◽  
J. C. García Codron
Keyword(s):  
Iberian Peninsula ◽  
Water Level ◽  
Sea Level ◽  
Tide Gauge ◽  
Water Levels ◽  
Northern Coast ◽  
Atmospheric Conditions ◽  
Total Water ◽  
Total Water Level

Abstract. This paper assesses the evolution of storminess along the northern coast of the Iberian Peninsula through the calculation of extreme (1%) Total Water Levels (eTWL) on both observed (tide gauge and buoy data) and hindcasted (SIMAR-44) data. Those events were first identified and then characterized in terms of oceanographic parameters and atmospheric circulation features. Additionally, an analysis of the long-term trends in both types of data was performed. Most of the events correspond to a rough wave climate and moderate storm surges, linked to extratropical disturbances following a northern track. While local atmospheric conditions seem to be evolving towards lesser storminess, their impact has been balanced by the favorable exposure of the northern coast of the Iberian Peninsula to the increasing frequency and strength of distant disturbances crossing the North Atlantic. This evolution is also correctly reproduced by the simulated long-term evolution of the forcing component (meteorological sea level residuals and wave run up) of the Total Water Level values calculated from the SIMAR 44 database, since sea level residuals have been experiencing a reduction while waves are arriving with longer periods. Finally, the addition of the rate of relative sea level trend to the temporal evolution of the atmospheric forcing component of the Total Water Level values is enough to simulate more frequent and persistent eTWL.

scholarly journals Fluctuating water levels influence access to critical habitats for threatened Cowichan Lake lamprey

FACETS ◽  
2020 ◽  
Vol 5 (1) ◽  
pp. 488-502
Author(s):  
Chiranjib Chaudhuri ◽  
Joy Wade ◽  
Colin Robertson
Keyword(s):  
Water Level ◽  
Water Levels ◽  
Spawning Period ◽  
Habitat Area ◽  
Management Actions ◽  
Bear Lake ◽  
Aerial Vehicle ◽  
Key Drivers ◽  
Term Data

Cowichan Lake lamprey ( Entosphenus macrostomus) is a threatened species resident to Mesachie Lake, Cowichan Lake, and adjoining Bear Lake and their major tributaries in British Columbia. Decreases in trapping success have created concerns that the population is declining. Some potential threats include water use, climate change, and management actions. Owing to the absence of long-term data on population trends, little information is available to estimate habitat quality and factors that influence it. We sought to fill this gap by examining associations between habitat area and variables representing suspected key drivers of habitat availability. Critical habitat areas were imaged using an unmanned aerial vehicle over a period of three years at three sites at Cowichan Lake and a subsequent habitat area was classified. Meteorological and anthropogenic controls on habitat area were investigated through automatic relevance detection regression models. The major driver of habitat area during the critical spawning period was water level during the storage season, which also depends on the meteorological variables and anthropogenic control. It is recommended that regulation of the weir should aim to ensure that the water level remains above the 1 m mark, which roughly equates to the 67% coverage of water on the habitat area used for spawning.

scholarly journals Application of artificial neural network for predicting water levels in Hooghly estuary, India

2020 ◽  
Vol 3 (1) ◽  
pp. 401-415 ◽  
Author(s):  
Kalyan Kumar Bhar ◽  
Susmita Bakshi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Water Level ◽  
Tidal Cycle ◽  
Water Levels ◽  
Hooghly Estuary ◽  
Tidal Water ◽  
Level Data ◽  
Water Level Data ◽  
Artificial Neural

Abstract Hydrodynamic models for morphodynamic studies in estuaries require continuous tidal water level data as boundary conditions. However, for the Hooghly estuary in India, measurement of continuous tidal water elevation data at the most downstream point is a very difficult task because of the remote location and the confluence with the deep sea. The tidal water level data at this station are measured for a half tidal cycle which is not useful for hydrodynamic modeling. However, at other upstream stations, tide water level data are measured continuously. Accordingly, in this study, an attempt is made to generate continuous tidal water level data at the remote station, using the data of the neighboring stations as input to an artificial neural network (ANN) model. A three-layered feed-forward backpropagation (FFBP) network with two hidden layers is selected and five different combinations of input vectors are used. Simulated water level data obtained from each model are compared with the observed data graphically as well as by estimating the standard error parameters. The best model suitable for prediction of continuous tidal elevation during any time of the tidal cycle and applicable throughout the year is then identified. It is found that tidal data from the nearest neighboring station are more suitable for training.

Development of a regional neural network for coastal water level predictions

2003 ◽  
Vol 30 (17) ◽  
pp. 2275-2295 ◽  
Author(s):  
Wenrui Huang ◽  
Catherine Murray ◽  
Nicholas Kraus ◽  
Julie Rosati
Keyword(s):  
Neural Network ◽  
Water Level ◽  
Coastal Water ◽  
Coastal Water Level

scholarly journals Spatial distribution of water level impact to back-barrier bays

2018 ◽  
Author(s):  
Alfredo L. Aretxabaleta ◽  
Neil K. Ganju ◽  
Zafer Defne ◽  
Richard P. Signell
Keyword(s):  
Water Level ◽  
Sea Level ◽  
Water Levels ◽  
Analytical Models ◽  
Barnegat Bay ◽  
Sea Level Fluctuations ◽  
Flooding Hazard ◽  
Short Period ◽  
Inlet Geometry ◽  
Spatial Estimates

Abstract. Water level in semi-enclosed bays, landward of barrier islands, is mainly driven by offshore sea level fluctuations that are modulated by bay geometry and bathymetry, causing spatial variability in the ensuing response (transfer). Local wind setup can have a secondary role that depends on wind speed, fetch, and relative orientation of the wind direction and the bay. Inlet geometry and bathymetry primarily regulate the magnitude of the transfer between open ocean and bay. Tides and short-period offshore oscillations are more damped in the bays than longer-lasting offshore fluctuations, such as storm surge and sea level rise. We compare observed and modeled water levels at stations in a mid-Atlantic bay (Barnegat Bay) with offshore water level proxies. Observed water levels in Barnegat Bay are compared and combined with model results from the Coupled Ocean–Atmosphere–Wave–Sediment Transport (COAWST) modeling system to evaluate the spatial structure of the water level transfer. Analytical models based on the dimensional characteristics of the bay are used to combine the observed data and the numerical model results in a physically consistent approach. Model water level transfers match observed values at locations inside the Bay in the storm frequency band (transfers ranging from 70–100 %) and tidal frequencies (10–55 %). The contribution of frequency-dependent local setup caused by wind acting along the bay is also considered. The approach provides transfer estimates for locations inside the Bay where observations were not available resulting in a complete spatial characterization. The approach allows for the study of the Bay response to alternative forcing scenarios (landscape changes, future storms, and rising sea level). Detailed spatial estimates of water level transfer can inform decisions on inlet management and contribute to the assessment of current and future flooding hazard in back-barrier bays and along mainland shorelines.

scholarly journals Application of Neural Network Models and ANFIS for Water Level Forecasting of the Salve Faccha Dam in the Andean Zone in Northern Ecuador

Water ◽  
2021 ◽  
Vol 13 (15) ◽  
pp. 2011
Author(s):  
Pablo Páliz Larrea ◽  
Xavier Zapata Ríos ◽  
Lenin Campozano Parra
Keyword(s):  
Neural Network ◽  
Water Level ◽  
Clustering Algorithm ◽  
Network Models ◽  
Water Levels ◽  
Subtractive Clustering ◽  
Neural Network Models ◽  
Reservoir Water ◽  
Reservoir Water Level ◽  
Maximum Delay

Despite the importance of dams for water distribution of various uses, adequate forecasting on a day-to-day scale is still in great need of intensive study worldwide. Machine learning models have had a wide application in water resource studies and have shown satisfactory results, including the time series forecasting of water levels and dam flows. In this study, neural network models (NN) and adaptive neuro-fuzzy inference systems (ANFIS) models were generated to forecast the water level of the Salve Faccha reservoir, which supplies water to Quito, the Capital of Ecuador. For NN, a non-linear input–output net with a maximum delay of 13 days was used with variation in the number of nodes and hidden layers. For ANFIS, after up to four days of delay, the subtractive clustering algorithm was used with a hyperparameter variation from 0.5 to 0.8. The results indicate that precipitation was not influencing input in the prediction of the reservoir water level. The best neural network and ANFIS models showed high performance, with a r > 0.95, a Nash index > 0.95, and a RMSE < 0.1. The best the neural network model was t + 4, and the best ANFIS model was model t + 6.

scholarly journals Water level oscillations in Monterey Bay and Harbor

Ocean Science ◽  
2015 ◽  
Vol 11 (3) ◽  
pp. 439-453 ◽  
Author(s):  
J. Park ◽  
W. V. Sweet ◽  
R. Heitsenrether
Keyword(s):  
Water Level ◽  
Wave Energy ◽  
Normal Modes ◽  
Low Frequency ◽  
Water Levels ◽  
Level Data ◽  
Modal Amplitude ◽  
Primary Driver ◽  
Potential Mode ◽  
Offshore Wave

Abstract. Seiches are normal modes of water bodies responding to geophysical forcings with potential to significantly impact ecology and maritime operations. Analysis of high-frequency (1 Hz) water level data in Monterey, California, identifies harbor modes between 10 and 120 s that are attributed to specific geographic features. It is found that modal amplitude modulation arises from cross-modal interaction and that offshore wave energy is a primary driver of these modes. Synchronous coupling between modes is observed to significantly impact dynamic water levels. At lower frequencies with periods between 15 and 60 min, modes are independent of offshore wave energy, yet are continuously present. This is unexpected since seiches normally dissipate after cessation of the driving force, indicating an unknown forcing. Spectral and kinematic estimates of these low-frequency oscillations support the idea that a persistent anticyclonic mesoscale gyre adjacent to the bay is a potential mode driver, while discounting other sources.

scholarly journals Determining Extreme Still Water Levels for Design and Planning Purposes Incorporating Sea Level Rise: Sydney, Australia

Atmosphere ◽  
2022 ◽  
Vol 13 (1) ◽  
pp. 95
Author(s):  
Phil J. Watson
Keyword(s):  
Sea Level Rise ◽  
Water Level ◽  
Sea Level ◽  
Tide Gauge ◽  
Water Levels ◽  
Extreme Value Analysis ◽  
Tide Gauge Record ◽  
Value Analysis ◽  
Mean Sea Level ◽  
The Impact

This paper provides an Extreme Value Analysis (EVA) of the hourly water level record at Fort Denison dating back to 1915 to understand the statistical likelihood of the combination of high predicted tides and the more dynamic influences that can drive ocean water levels higher at the coast. The analysis is based on the Peaks-Over-Threshold (POT) method using a fitted Generalised Pareto Distribution (GPD) function to estimate extreme hourly heights above mean sea level. The analysis highlights the impact of the 1974 East Coast Low event and rarity of the associated measured water level above mean sea level at Sydney, with an estimated return period exceeding 1000 years. Extreme hourly predictions are integrated with future projections of sea level rise to provide estimates of relevant still water levels at 2050, 2070 and 2100 for a range of return periods (1 to 1000 years) for use in coastal zone management, design, and sea level rise adaptation planning along the NSW coastline. The analytical procedures described provide a step-by-step guide for practitioners on how to develop similar baseline information from any long tide gauge record and the associated limitations and key sensitivities that must be understood and appreciated in applying EVA.

Dead Sea Water Levels Analysis Using Artificial Neural Networks and Firefly Algorithm

2022 ◽  
pp. 1118-1129
Author(s):  
Nawaf N. Hamadneh
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Water Level ◽  
Firefly Algorithm ◽  
Sea Water ◽  
Dead Sea ◽  
Water Levels ◽  
Artificial Neural ◽  
The Dead ◽  
Predicted Values

In this study, the performance of adaptive multilayer perceptron neural network (MLPNN) for predicting the Dead Sea water level is discussed. Firefly Algorithm (FFA), as an optimization algorithm is used for training the neural networks. To propose the MLPNN-FFA model, Dead Sea water levels over the period 1810–2005 are applied to train MLPNN. Statistical tests evaluate the accuracy of the hybrid MLPNN-FFA model. The predicted values of the proposed model were compared with the results obtained by another method. The results reveal that the artificial neural network (ANN) models exhibit high accuracy and reliability for the prediction of the Dead Sea water levels. The results also reveal that the Dead Sea water level would be around -450 until 2050.

Prediction of Water Level Using Time Series, Wavelet and Neural Network Approaches

2022 ◽  
pp. 1077-1097
Author(s):  
Nguyen Quang Dat ◽  
Ngoc Anh Nguyen Thi ◽  
Vijender Kumar Solanki ◽  
Ngo Le An
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Water Level ◽  
Project Work ◽  
Level Data ◽  
Water Level Data ◽  
Ann Models ◽  
Input Variables ◽  
Network Approaches ◽  
Short Time

To control water resources in many domains such as agriculture, flood forecasting, and hydro-electrical dams, forecasting water level needs to predict. In this article, a new computational approach using a data driven model and time series is proposed to calculate the forecast water level in short time. Concretely, wavelet-artificial neural network (WAANN) and time series (TS) are combined together called WAANN-TS that encourages the advantage of each model. For this real time project work, Yen Bai station, Northwest Vietnam was chosen as an experimental case study to apply the proposed model. Input variables into the Wavelet-ANN structure is water level data. Time series and ANN models are built, and their performances are compared. The results indicate the greater accuracy of the proposed models at Hanoi station. The final proposal WAANN−TS for water level forecasting shows good performance with root mean square error (RMSE) from 10−10 to 10−11.

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