Time Series Of Water Surface Elevation Research Articles

Computing River Discharge Using Water Surface Elevation Based on Deep Learning Networks

Accurately computing river discharge is crucial, but traditional computing methods are complex and need the assistance of many other hydraulic parameters. Therefore, it is of practical value to develop a convenient and effective auto-computation technique for river discharge. Water surface elevation is relatively easy to obtain and there is a strong relationship between river discharge and water surface elevation, which can be used to compute river discharge. Unlike previous usage of deep learning to predict short-term river discharge that need multiple parameters besides water level, this paper proved that deep learning has the potential to accurately compute long-term river discharge purely based on water level. It showed that the majority of relative errors on the test dataset were within ±5%, particularly it could operate continuously for almost one year with high precision without retraining. Then, we used BiGRU to compute river flow with different hyperparameters, and its best RMSE, NSE, MAE, and MAPE values were 256 m3/s, 0.9973, 207 m3/s, and 0.0336, respectively. With this data-driven based technology, it will be more convenient to obtain river discharge time series directly from local water surface elevation time series accurately in natural rivers, which is of practical value to water resources management and flood protection.

A combined Gaussian process regression and one-dimensional least squares harmonic method for tidal current prediction

Predictions of tidal currents are required for various activities in the maritime industry, including marine power exploitation, operational forecasting, and coastal engineering. In addition to the conventional least squares harmonic method (LSHM) and numerical simulation, a machine learning algorithm has recently been developed and applied for predicting tidal currents in time series. Gaussian Process Regression (GPR) is one of the most important machine learning algorithms due to its ability to process time-series data. In this paper, we propose a combined one-dimensional LSHM and GPR method for predicting depth-averaged current velocity vectors according to time-series changes of water surface elevation due to tide. LSHM firstly decomposes water surface elevation time series into a number of possible constituents (i.e., amplitudes, phase-lags) according to known tidal frequencies. For each of the contributing constituents, GPR is then applied to produce the corresponding current generator using known tidal current as training data. The training data are generated by numerical simulations representing different coastal settings. The predicted tidal current is calculated according to the previously trained GPR model. As a comparison, we also generated tidal current predictions using a conventional two-dimensional LSHM. It is found that the tidal currents predicted using the sequence of computations proposed in this paper show comparable results with slightly better accuracy than conventional two-dimensional LSHM. The results from this study could provide an alternative method of tidal current prediction, minimising the need for the laborious re-runs of numerical simulations and lengthy current observations, as well as removing the requirement to carry out a two-dimensional analysis of the tidal current using LSHM.

Three different models to evaluate water discharge: An application to a river section at Vinh Tuy location in the Lo river basin, Vietnam

This study presents three different models, namely power-law rating curve, one-dimensional lateral distribution method (1D–LDM), and gated recurrent network (GRU) model that can be applied to evaluate water discharge from water surface elevation time-series in a river cross-section for a long time period. A river section at Vinh Tuy location on the Lo river basin (Vietnam) is used to demonstrate the models. Appropriate values of modelling parameters are carefully determined using (i) the daily observed discharge values collected in the period from 2012 to 2018 and (ii) five error estimates for quantitatively assessing the agreement between estimated and observed water discharges. The results showed that all three models reproduced very well the observed discharge values, with root mean square error and mean absolute error, as well as mean error of discharge, are only about 5.5% of the maximum value of discharge monitoring in the studied cross-section, while Nash–Sutcliffe efficiency and Pearson’s correlation coefficient are greater than 0.89. The models are then applied to evaluate discharge values in the studied cross-section for the period from 1972 to 2011, revealing that statistical indicators, i.e. mean value, standard derivation, and covariance of estimated water discharge, are consistent with those obtained from the observations. Among three investigated models, the GRU model was finally proved to be the best one, providing even better results than the 1D-LDM and power-law rating curve.

Identifying Higher-Order Interactions in Wave Time-Series

Abstract Reliable design and reanalysis of coastal and offshore structures require, among other things, characterization of extreme crest elevation corresponding to long return periods. Extreme crests typically correspond to focused wave events enhanced by wave–wave interactions of different orders—third-order, four-wave interactions dominating in deep water (Janssen, P. A. E. M., 2003, “Nonlinear Four-Wave Interactions and Freak Waves,” J. Phys. Oceanogr., 33(4), pp. 863–884). Higher-order spectral (HOS) analysis can be used to identify wave–wave interactions in time-series of water surface elevation; trispectral analysis is needed to detect third-order, four-wave interactions. Four-wave interactions between Fourier components can involve interactions of the type where f1 + f2 + f3 = f4 and where f1 + f2 = f3 + f4, resulting in two definitions of the trispectrum—the T- and V-trispectrum (with corresponding tricoherences), respectively. It is shown that the T-tricoherence is capable of detecting phase-locked four-wave interactions of the type f4 = f1 + f2 + f3 when these are simulated with simple sinusoids, but such interactions were not detected in HOS model simulations and field data. It is also found that high V-tricoherence levels are detected at frequencies at which four-wave interactions of the type f1 + f2 = f3 + f4 are expected, but these may simply indicate combinations of independent pairs of Fourier components that happen to satisfy the frequency relationship. Preliminary analysis shows that using a cumulant-based trispectrum (Kravtchenko-Berejnoi, V., Lefeuvre, F., Krasnosel'skikh, V. V., and Lagoutte, D., 1995, “On the Use of Tricoherent Analysis to Detect Nonlinear Wave–Wave Interactions,” Signal Process., 42(3), pp. 291–309) may improve identification of wave–wave interactions. These results highlight that caution needs to be exercised in interpreting trispectra in terms of specific four-wave interactions occurring in sea states and further research is needed to establish whether this is in fact possible in practice.

A Sterescopic System to Measure Water Waves in Laboratories

A new system for estimating the synthetic parameters of sea states during physical investigations has been implemented. The technique proposed herein is based on stereographic analysis of digital images acquired with optical sensors. A series of ad hoc floating markers has been made and properly moored to the bottom of a large wave tank to estimate the synthetic parameters of generated waves. The implemented acquisition system and the proposed algorithm provide automatic recognition of all markers by a pair of optical sensors that synchronously captures their instantaneous location and tracks their movements over time. After transformation from the image to the real-world coordinates, water surface elevation time series have been obtained. Several experimental tests have been carried out to assess the feasibility and reliability of the proposed approach. The estimated wave synthetic parameters have been then compared with those obtained by employing standard resistive probes. The deviation were found to be equal to ~6% for the significant wave height and 1% for peak, mean, and significant wave periods.

Investigation of nonlinear wave-induced seabed response around mono-pile foundation

Stability and safety of offshore wind turbines with mono-pile foundations, affected by nonlinear wave effect and dynamic seabed response, are the primary concerns in offshore foundation design. In order to address these problems, the effects of wave nonlinearity on dynamic seabed response in the vicinity of mono-pile foundation is investigated using an integrated model, developed using OpenFOAM, which incorporates both wave model (waves2Foam) and Biot's poro-elastic model. The present model was validated against several laboratory experiments and promising agreements were obtained. Special attention was paid to the systematic analysis of pore water pressure as well as the momentary liquefaction in the proximity of mono-pile induced by nonlinear wave effects. Various embedded depths of mono-pile relevant for practical engineering design were studied in order to attain the insights into nonlinear wave effect around and underneath the mono-pile foundation. By comparing time-series of water surface elevation, inline force, and wave-induced pore water pressure at the front, lateral, and lee side of mono-pile, the distinct nonlinear wave effect on pore water pressure was shown. Simulated results confirmed that the presence of mono-pile foundation in a porous seabed had evident blocking effect on the vertical and horizontal development of pore water pressure. Increasing embedded depth enhances the blockage of vertical pore pressure development and hence results in somewhat reduced momentary liquefaction depth of the soil around the mono-pile foundation.

Accurate Estimation of Wave Reflection on a High Energy, Dissipative Beach

ABSTRACT Inch, K.; Davidson, M.; Masselink, G., and Russell, P., 2016. Accurate estimation of wave reflection on a high energy, dissipative beach. In: Vila-Concejo, A.; Bruce, E.; Kennedy, D.M., and McCarroll, R.J. (eds.), Proceedings of the 14th International Coastal Symposium (Sydney, Australia). Journal of Coastal Research, Special Issue, No. 75, pp. 877–881. Coconut Creek (Florida), ISSN 0749-0208. Noise induced error in estimated wave reflection coefficients using an array method is investigated using simulated time series of water surface elevation with known reflection coefficients and added uncorrelated noise. Reflection coefficients are found to be increasingly overestimated with the addition of noise and this positive bias is more significant for lower levels of true reflection. Coherence between the water surface elevation time series changes systematically with the bias and, along with the estimated reflection coefficients, is used to create a “look-up table” which serves as a function to corr...

Sinking of irregular shape blocks into marine seabed under wave-induced liquefaction

The sinking of initially buried irregular blocks into the seabed under wave-induced liquefaction was investigated by experimental methods. Pore-water pressure in the soil, water surface elevation time series and block displacements were measured. Results indicated that initiation of sinking coincides with the instant at which the accumulated pore-water pressure at the bottom level of the block reaches the initial mean normal effective stress. The drag forces and drag coefficients on steadily sinking irregular shaped blocks, as well as spherical and cubical ones, were calculated from the obtained data and compared with the available data in the literature. The results show that the shape of sinking block is of minor importance as far as the kinematics and dynamics of the sinking block is concerned. The conditions at which the sinking terminates are discussed in the light of experiments. Using the approach presented here, the ultimate sinking depths are calculated for the tested cases and compared with the experimental results. The calculated and measured values showed a reasonable agreement when compared. Finally a summary and remarks are presented to calculate the ultimate sinking depth of irregular shaped blocks for practical applications.

Nonintrusive Measurement of Ocean Waves: Lidar Wave Gauge

Abstract In December 1999, a nonintrusive directional lidar wave gauge (LWG) was field tested at the Field Research Facility (FRF) in North Carolina. The LWG uses proven lidar technology to directly measure water surface elevation from above the water’s surface. Therefore, unlike bottom-mounted gauges, the surface elevation measurement from the LWG is independent of other hydrodynamic processes. The LWG prototype consists of four rangefinders, where each collects water surface elevation time series at a rate of 10 Hz. During the field test, ground truth data were collected concurrently with the LWG data using a biaxial current and pressure meter mounted directly beneath the LWG. Additional ground truth data were available from the existing FRF wave-gauging infrastructure. This paper describes principles of LWG operation and field experiment setup, LWG environmental and technical constraints, and LWG performance in measuring spectral wave parameters.

Removing Tidal-Period Variations from Time-Series Data Using Low-Pass Digital Filters

Abstract Several low-pass, digital filters are examined for their ability to remove tidal Period Variations from a time-series of water surface elevation for San Francisco Bay. The most efficient filter is the one which is applied to the Fourier coefficients of the transformed data, and the filtered data recovered through an inverse transform. The ability of the filters to remove the tidal components increased in the following order: 1) cosine-Lanczos filter, 2) cosine-Lanczos squared filter; 3) Godin filter; and 4) a transform fitter. The Godin fitter is not sufficiently sharp to prevent severe attenuation of 2–3 day variations in surface elevation resulting from weather events.