scholarly journals HIGH AND LOW WATER PREDICTION AT LAGOS HARBOUR, NIGERIA

2017 ◽  
Vol 36 (3) ◽  
pp. 944-952
Author(s):  
OT Badejo ◽  
SO Akintoye
Keyword(s):  
Prediction Model ◽  
White Noise ◽  
Harmonic Analysis ◽  
Least Squares ◽  
Water Level ◽  
Observational Period ◽  
Tidal Constituent ◽  
Tidal Harmonic ◽  
Tidal Data ◽  
Tidal Constituents

In this work, 500 hourly water level tidal data were used to perform least squares tidal harmonic analysis. Eleven tidal constituents were used for the harmonic analysis. Astronomical arguments (v + u) and the nodal factor (f) were computed for each tidal constituent and at each observational period with a programme written in Matlab environment. The harmonic constants determined from the least squares tidal harmonic analysis were substituted into a tidal prediction model to predict hourly tidal data and tidal predictions at 5 minutes’ intervals. Series of high and low water heights from the tidal predictions made at 5 minutes’ intervals were determined and matched with their corresponding times. Autocorrelation at lags 1 to 30 for the residuals of the observed and predicted tidal data shows that there is no significant correlation in the range of the 30 lags. The series of residuals of the observed and predicted tidal data is therefore white noise.   http://dx.doi.org/10.4314/njt.v36i3.39

scholarly journals STUDIES ON THE CORRELATION OF TIDAL ELEVATION CHANGES ALONG THE WESTERN COASTLINE OF TAIWAN

1986 ◽  
Vol 1 (20) ◽  
pp. 23 ◽  
Author(s):  
H.H. Hwung ◽  
C.L. Tsai ◽  
C.C. Wu
Keyword(s):  
Energy Density ◽  
Harmonic Analysis ◽  
Water Level ◽  
High Water ◽  
Maximum Energy ◽  
Phase Lag ◽  
Tidal Elevation ◽  
Elevation Changes ◽  
Tidal Constituents ◽  
Phase Angles

In order to understand the characteristics of tidal elevation changes along the western coastline of Taiwan, the authors collected the tidal records at the same duration from eleven stations and made an elaborate analysis in this paper. First step, the main tidal constituents were picked out from spectrum analysis, and the amplitudes and phase angles of these tidal constituents would be obtained by harmonic analysis. Then the variations of amplitude and phase lag of the main constituents and the variations of mean high water level and mean low water level along the coastline would be presented in the figures respectively. Finally, based on the results of harmonic analysis, the energy density of tide for every station could be calculated separately, and the location of the maximum energy density would be determined by cubic spline method.

Measurement of Dielectric Loss Angle Based on Wavelet Threshold Denoising and Least Squares

2012 ◽  
Vol 220-223 ◽  
pp. 2160-2164
Author(s):  
Hai Wen Chen ◽  
Fu Jiang Mo
Keyword(s):  
Dielectric Loss ◽  
White Noise ◽  
Harmonic Analysis ◽  
Least Squares ◽  
Least Square ◽  
Fundamental Wave ◽  
Loss Angle ◽  
Analysis Method ◽  
Dielectric Loss Angle ◽  
Harmonic Analysis Method

Dielectric loss angle is the effective means to determine the status of the high voltage equipment.Influenced by power grid frequency fluctuation and white noise, the measurement accuracy of traditional method of dielectric loss angle such as harmonic analysis method, cannot meet the demand.A new methodbased on wavelet threshold denoising and least squares is presented in this paper. Firstly, harmonic wave and white noise are eliminated by improved wavelet thresholddenoising, Second, the fundamental wave of voltage and current signal is extracted by mean of least square method, then we can get dielectric loss angle.Simulation and calculation are validated in the different values of frequency fluxion and whtie noise.Comparing with harmonic analysis method,the result show the feasibility and effectiveness of this method.

scholarly journals EFFECT OF LONG TERM TIDAL CONSTITUENTS ON MEAN SEA LEVEL TREND DURING EL-NINO AND LA-NINA PHENOMENA

2017 ◽  
Vol XLII-4/W5 ◽  
pp. 225-234
Author(s):  
N. H. M. Yusof ◽  
M. R. Mahmud ◽  
M. H. Abdullah
Keyword(s):  
El Niño ◽  
El Nino ◽  
La Niña ◽  
The Moon ◽  
Tidal Constituent ◽  
La Nina ◽  
Tidal Data ◽  
The Difference ◽  
Tidal Constituents

There are many factors that influence the change of the tidal constituent pattern. The factors can be classified as astronomical factors and non- astronomical factors. The astronomical factors involve the gravitational force attraction by the moon and sun. The distance of sun and moon influence the gravitational attractions that are produced by the moon and sun towards earth surface. Non-astronomical factors involved the movements of currents, waves, temperature and so on which is cause by the phenomenon such as El-Nino, La-Nina. This study was conducted to investigate the effect of these phenomena towards the pattern of tidal constituent during the phenomenon on several stations that have been chosen. The difference stations were chose due to the change in the position of celestial body. In addition, tidal data for several stations were processed using the UTM Tidal Analysis and Prediction Software (μ-TAPS). Based on the tidal data that has been processed, the graphs were plotted for the predicted data and observed data to compare the different pattern between the data. The tidal data that has been processed were used to analyse the pattern of tidal constituents based on the changing amplitude of M<sub>2</sub>, S<sub>2</sub>, K<sub>1</sub>, O<sub>1</sub>, S<sub>a</sub>, S<sub>sa</sub>, Mm, Mf and MSf.

Study on Prediction Model of Karstic Large Spring Water Level Dynamic Coupling Multiple Factors in Jinan

2015 ◽  
Vol 8 (1) ◽  
pp. 309-317 ◽  
Author(s):  
Xing Liting ◽  
Zhou Juan ◽  
Zhang Fengjuan ◽  
Wang Song ◽  
Dou Tongwen ◽  
...  
Keyword(s):  
Prediction Model ◽  
Water Level ◽  
Spring Water ◽  
Anthropogenic Factors ◽  
Karst Water ◽  
Multiple Factors ◽  
Factors Influencing ◽  
Heterogeneous Features ◽  
Dry Seasons ◽  
Natural Decay

In karst regions, due to the heterogeneous features of karst medium, the characteristics of the groundwater flow turn to be of high complexity. Researchers have been seeking proper forecasting methods for karst water dynamic for many years. This paper, taking the spring in Jinan as an example, using regression analysis, analyzed the factors influencing spring water dynamic, and quantitatively evaluated the influencing coefficients of spring water level concerning rainfall, exploitation and recharge as well as the natural decay coefficient of spring water in dry seasons. The prediction model coupling multiple factors was built by investigating natural and anthropogenic factors influencing groundwater level, which could be used for forecasting dynamic of spring water in Jinan. The calculated value of model was highly coincided with the observed value. In consideration of the characteristics of uneven precipitation in Jinan, the suitable zones and volume of artificial recharge were investigated finally, which could help to sustain the spewing of Jinan springs significantly.

scholarly journals Predicting Ink Transfer Rate of 3D Additive Printing Using EGBO Optimized Least Squares Support Vector Machine Model

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Shengpu Li ◽  
Yize Sun
Keyword(s):  
Support Vector Machine ◽  
Prediction Model ◽  
Least Squares ◽  
Transfer Rate ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Support Vector ◽  
Experimental Sample ◽  
Machine Model ◽  
Ink Transfer

Ink transfer rate (ITR) is a reference index to measure the quality of 3D additive printing. In this study, an ink transfer rate prediction model is proposed by applying the least squares support vector machine (LSSVM). In addition, enhanced garden balsam optimization (EGBO) is used for selection and optimization of hyperparameters that are embedded in the LSSVM model. 102 sets of experimental sample data have been collected from the production line to train and test the hybrid prediction model. Experimental results show that the coefficient of determination (R2) for the introduced model is equal to 0.8476, the root-mean-square error (RMSE) is 6.6 × 10 (−3), and the mean absolute percentage error (MAPE) is 1.6502 × 10 (−3) for the ink transfer rate of 3D additive printing.

An application of backtracking search optimization–based least squares support vector machine for prediction of short-term wind speed

2019 ◽  
Vol 44 (3) ◽  
pp. 266-281 ◽  
Author(s):  
Zhongda Tian ◽  
Yi Ren ◽  
Gang Wang
Keyword(s):  
Support Vector Machine ◽  
Wind Speed ◽  
Prediction Model ◽  
Least Squares ◽  
Support Vector ◽  
Short Term ◽  
Backtracking Search ◽  
Wind Speed Prediction ◽  
Search Optimization ◽  
Speed Prediction

Wind speed prediction is an important technology in the wind power field; however, because of their chaotic nature, predicting wind speed accurately is difficult. Aims at this challenge, a backtracking search optimization–based least squares support vector machine model is proposed for short-term wind speed prediction. In this article, the least squares support vector machine is chosen as the short-term wind speed prediction model and backtracking search optimization algorithm is used to optimize the important parameters which influence the least squares support vector machine regression model. Furthermore, the optimal parameters of the model are obtained, and the short-term wind speed prediction model of least squares support vector machine is established through parameter optimization. For time-varying systems similar to short-term wind speed time series, a model updating method based on prediction error accuracy combined with sliding window strategy is proposed. When the prediction model does not match the actual short-term wind model, least squares support vector machine trains and re-establishes. This model updating method avoids the mismatch problem between prediction model and actual wind speed data. The actual collected short-term wind speed time series is used as the research object. Multi-step prediction simulation of short-term wind speed is carried out. The simulation results show that backtracking search optimization algorithm–based least squares support vector machine model has higher prediction accuracy and reliability for the short-term wind speed. At the same time, the prediction performance indicators are also improved. The prediction result is that root mean square error is 0.1248, mean absolute error is 0.1374, mean absolute percentile error is 0.1589% and R2 is 0.9648. When the short-term wind speed varies from 0 to 4 m/s, the average value of absolute prediction error is 0.1113 m/s, and average value of absolute relative prediction error is 8.7111%. The proposed prediction model in this article has high engineering application value.

EXAMINATION OF SHORT-TERM WATER LEVEL PREDICTION MODEL IN LOW-LYING LAKES USING MACHINE LEARNING

2020 ◽  
Vol 76 (2) ◽  
pp. I_439-I_444
Author(s):  
Masaomi KIMURA ◽  
Takahiro ISHIKAWA ◽  
Naoto OKUMURA ◽  
Issaku AZECHI ◽  
Toshiaki IIDA
Keyword(s):  
Machine Learning ◽  
Prediction Model ◽  
Water Level ◽  
Short Term ◽  
Water Level Prediction

scholarly journals MODEL VERIFICATION FOR TIDAL CONSTITUENTS

1976 ◽  
Vol 1 (15) ◽  
pp. 193
Author(s):  
R.W. Whalin ◽  
F.C. Perry ◽  
D.L. Durham
Keyword(s):  
Energy Transfer ◽  
Harmonic Analysis ◽  
Sampling Rate ◽  
Tidal Inlet ◽  
Model Verification ◽  
Ocean Tide ◽  
Maximum Deviation ◽  
Tidal Constituent ◽  
Tide Gage ◽  
Time Required

Installation and operation of an automated model data acquisition and control system have made it possible to make a quantum advance in the accuracy and time required for verification of tidal inlet (or estuary) hydraulic models. The flexible sampling rate (usually about 200 samples per model tidal cycle for each gage) and digital recording of these data make them ideal for harmonic analysis and comparison with prototype data defining the coefficients and phase for each tidal constituent at various key locations within the tidal lagoon and at an open-ocean station removed from the immediate influence of the tidal inlet. The concept used is to force the model with the M2 tidal constituent with the amplitude being correct at the ocean tide gage. A harmonic analysis is performed at all other gage locations corresponding to the prototype measurements, and the amplitude and phase (relative to the ocean tide gage) are calculated and compared with the prototype data. Investigation of the relative phases between various gages quickly shows those areas where either more or less model roughness is required. It is reasonable to expect to be able to have all phases for the M2 constituent verified within 1 degree. Tidal elevations can almost always be expected to be verified to within a maximum deviation of ;+0.1 ft in both tidal height and mean tide level. Upon verification of the M2 constituent, which practically insures that the proper channel roughness is obtained, a progressive tide can be constructed; and it should be attempted to perform a verification for a 14.765-day (synoptic period for M2 and S2 components) progressive tide at east coast locations using the prototype measurements of tidal velocities for the final verification data. Should additional roughness be necessary, it will almost always be on the mud flats or marsh areas. Computations are made to illustrate the energy transfer from the M2 constituent to higher order harmonics as the wave propagates from the ocean to the back of the estuary, and it is shown that this energy transfer is, at worst, the same order of magnitude in both the model and prototype.

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