Augmenting wave-kinematics algorithms with machine learning to enable rapid littoral mapping and surf-zone state characterization from imagery

2020 ◽  
Author(s):  
Katherine L. Brodie ◽  
Adam Collins ◽  
Tyler J. Hesser ◽  
Matthew W. Farthing ◽  
Spicer Bak ◽  
...  
Keyword(s):  
Machine Learning ◽  
Surf Zone ◽  
Wave Kinematics ◽  
Zone State

Wave Kinematics and Wave Bottom Pressures in the Surf Zone

2001 ◽  
Author(s):  
O/ivind A. Arntsen ◽  
Tom Lilleås ◽  
Franziska Kuhnen ◽  
Andreas Menze ◽  
Matthias Löbermann ◽  
...  
Keyword(s):  
Surf Zone ◽  
Wave Kinematics

scholarly journals NEW BREAKER INDEX FORMULA IN PARAMETRIC WAVE MODEL

2020 ◽  
pp. 44
Author(s):  
Chi Zhang ◽  
Yuan Li ◽  
Yu Cai ◽  
Jian Shi ◽  
Jinhai Zheng
Keyword(s):  
Surf Zone ◽  
Wave Model ◽  
Parametric Models ◽  
Index Formula ◽  
Local Water ◽  
Wave Models ◽  
Offshore Wave ◽  
Zone State ◽  
Breaking Energy ◽  
Parametric Wave Model

Phase-averaged parametric wave models have been widely used to predict nearshore wave height transformation. The performance of parametric models depends significantly on the wave breaker index (), which controls the amount of breaking energy dissipation. Previous parameterizations improved the model predictability by considering the breaker index as a tunable coefficient, while made less effort to the physical interpretation for the proposed formulas. Indeed, inconsistency from the physical perspectives might exist. Therefore, the parameterization of still requires further investigation by considering the comprehensive influences of the offshore wave parameters and the local water depth, as well as the possible relationships with the breaker type and the surf zone state.

scholarly journals WAVE KINEMATICS AND DIRECTIONALITY IN THE SURF ZONE

1984 ◽  
Vol 1 (19) ◽  
pp. 5 ◽  
Author(s):  
J. Van Heteren ◽  
M.J.F. Stive
Keyword(s):  
Linear Theory ◽  
Linear Prediction ◽  
Surf Zone ◽  
Turbulent Energy ◽  
Peak Frequency ◽  
High Energy ◽  
Radiation Stress ◽  
Wave Kinematics ◽  
Stress Prediction ◽  
Quantitative Performance

Measurements of surface elevations and internal velocities have been conducted in a natural surf zone. The results were used to investigate the quantitative performance of linear theory in predicting the wave kinematics from the surface elevations. It appears that linear theory systematically overpredicts the horizontal velocities by 20 % in the frequency range around the peak, where the coherence with the surface motion is high, by 15 % at 2 times the peak frequency, changing in an underprediction of 15 % at higher frequencies. In these higher frequency ranges the rate of turbulent energy induced by breaking, contributes to the variance, so that the ratio of measured to theoretical r.m.s. fluctuation shows a trend of 25 % theoretical overprediction at negligible turbulent energy rates to 5 % underprediction at high turbulent energy rates. Furthermore the results were used to investigate the linear prediction of radiation stress and the effect of directionality on the radiation stress. Prediction of the radiation stress by unidirectional, linear theory gives an overestimation of 50 % at negligible turbulent energy rates to 35 % at high energy rates, which percentages reduce to 45 % and 25 i when the effect of shortcrestedness is taken into account.

Mind wandering as data augmentation: How mental travel supports abstraction

2020 ◽  
Vol 43 ◽  
Author(s):  
Myrthe Faber
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
Mental Content ◽  
Mind Wandering ◽  
Theoretical Framework ◽  
Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

Data Mining and Machine Learning

2020 ◽  
Author(s):  
Mohammed J. Zaki ◽  
Wagner Meira, Jr
Keyword(s):  
Machine Learning ◽  
Data Mining
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