ORCID Profile
0000-0001-9526-6087
Current Organisation
Universiteit Utrecht
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Publisher: American Geophysical Union (AGU)
Date: 06-2004
DOI: 10.1029/2003JC002214
Publisher: University of Toronto Press Inc. (UTPress)
Date: 06-2007
Abstract: Remote sensors are widely used in coastal morphodynamic research, and users are often confronted with abundant data sensors generated by these sensors. These data, if properly explored, can be used to study and manage many coastal features and processes that are not well understood. As an ex le, we present the exploration of rip channels. Coastal data sets are currently explored using animated image sequences. Users are dissatisfied, however, because exploration remains largely a subjective and time-consuming process. In particular, detecting the presence and evolution of relatively small, highly dynamic rip channels has proved difficult. This article examines how the visual exploration of rip objects can be improved. We first look at the factors limiting exploratory use of conventional animations for rip studies and argue that two main factors are responsible: data complexity and animation design based on images that mimic reality. Then we present an ex le of how the current approach to visualizing time series of coastal images can be improved by computational methods, particularly by feature tracking. Next, we describe a visualization prototype and discuss the representational, data-mining, and interactive functionality resulting from such a combination in an environment dedicated to the exploration of dynamic objects.
Publisher: Elsevier BV
Date: 07-2016
Publisher: Wiley
Date: 17-01-2017
DOI: 10.1002/ESP.4088
Publisher: Elsevier BV
Date: 07-2009
Publisher: Elsevier BV
Date: 05-2007
DOI: 10.1016/J.NEUNET.2007.04.007
Abstract: The temporal evolution of nearshore sandbars (alongshore ridges of sand fringing coasts in water depths less than 10 m and of paramount importance for coastal safety) is commonly predicted using process-based models. These models are autoregressive and require offshore wave characteristics as input, properties that find their neural network equivalent in the NARX (Nonlinear AutoRegressive model with eXogenous input) architecture. Earlier literature results suggest that the evolution of sandbars depends nonlinearly on the wave forcing and that the sandbar position at a specific moment contains 'memory', that is, time-series of sandbar positions show dependencies spanning several days. Using observations of an outer sandbar collected daily for over seven years at the double-barred Surfers Paradise, Gold Coast, Australia several data-driven models are compared. Nonlinear and linear models as well as recurrent and nonrecurrent parameter estimation methods are applied to investigate the claims about nonlinear and long-term dependencies. We find a small performance increase for long-term predictions (>40 days) with nonlinear models, indicating that nonlinear effects expose themselves for larger prediction horizons, and no significant difference between nonrecurrent and recurrent methods meaning that the effects of dependencies spanning several days are of no importance.
Publisher: Elsevier BV
Date: 07-2016
Publisher: Elsevier BV
Date: 02-2007
Publisher: American Geophysical Union (AGU)
Date: 07-2007
DOI: 10.1029/2006JC003799
Publisher: American Geophysical Union (AGU)
Date: 06-2015
DOI: 10.1002/2014JC010450
No related grants have been discovered for Gerben Ruessink.