Adonis F. Velegrakis

A Chebyshev polynomial radial basis function neural network for automated shoreline extraction from coastal imagery

This paper investigates the potential of using a polynomial radial basis function (RBF) neural network to extract the shoreline position from coastal video images. The basic structure of the proposed network encompasses a standard RBF network module, a module of nodes that use Chebyshev polynomials as activation functions, and an inference module. The experimental setup is an operational coastal video monitoring system deployed in two sites in Southern Europe to generate variance coastal images. Thehistogram of each image isapproximated by non-linear regression, and associated witha manually extracted intensity threshold value that quantifies the shoreline position. The key idea is to use the set of the resulting regression parameters as input data, and the intensity threshold values as output data of the network. In summary, the data set is extracted by quantifying the qualitative image information, and the proposed network takes the advantage of the powerful approximation capabilities of the Chebyshev polynomials by utilizing a small number of coefficients. For comparative reasons, we apply a polynomial RBF network trained by fuzzy clustering, and a feed-forward neural network trained by the back propagation algorithm. The comparison criteria used are the standard mean square error; the data return rates, and the root mean square error of the cross- shore shoreline position, calculated against the shorelines extracted by the aforementioned annotated threshold values. The main conclusions of the simulation study are: (a) the proposed method outperforms the other networks, especially in extracting the shoreline from images used as testing data; (b) for higher polynomial orders it obtains data return rates greater than 84%, and the root mean square error of the cross-shore shoreline position is less than 1.8 meters.

Recommendations for the sustainable exploitation of tidal sandbanks

A basic requirement for allowing marine aggregate (sand) extraction on the Belgian Continental Shelf (which takes place on sandbanks) is that it should not result in major environmental changes. However, a tidal sandbank (Kwinte Bank, Flemish Banks), exploited intensively since the 1970’s, has shown evidence of significant morphological changes with the development of a 5 m deep depression in its middle section; thus, since February 2003, sand extraction has ceased in this area in order to study the environmental impacts and the regeneration potential of the seabed. The present contribution synthesises the results of the multidisciplinary research, which has taken place in the area and, on the basis of these findings, considers the need for an efficient management framework, in both the planning and monitoring stages of the extraction. The investigation has shown that extraction has had significant impacts on the seabed sedimentary character and ecology and the local hydro-and sediment dynamic regime. Under these conditions, regeneration of the seabed is not likely in the short-term and, although modelling exercises have indicated possible recovery in the medium- and long-term, this is likely to be inhibited by the lack of appropriate sediments in the area. The results have provided the basis of the identification of a ‘suite’ of criteria, which can assist in the strategic planning/design of marine aggregate concession zones, the efficient management of marine aggregate extraction and the planning of effective environmental monitoring; these criteria are related to considerations on resource location, the nature/thickness of the targeted deposits, morphodynamics and sediment dynamics, biology and ecology and extraction practices. The Kwinte Bank investigation has demonstrated also the need for intensive monitoring schemes in order to identify the morphological, sedimentary and ecological impacts, related to the dredging activities. A critical part of these schemes should be the evaluation of the dredging-related effects, against the background of the natural dynamics of the seabed; thus, baseline information is crucial, as, in its absence, impact assessments are likely to remain inconclusive.

Influence of Dams on Downstream Beaches: Eressos, Lesbos, Eastern Mediterranean

Small water storage dams are nowadays regarded as the ideal solution for the water-thirsty islands of the Greek Archipelago. Several of these dams have been already constructed and more are planned for the near future. However, dams can also create problems to coastal areas, particularly to the beaches found at the lower reaches of the dammed rivers. The present contribution reports the results of a study undertaken on the effects of such a dam located at Eressos, Lesbos (E. Mediterranean), using both morphological and sedimentological information and a GIS-based sediment erosion model. The results showed that Eressos Beach is currently under erosion, which however is spatially variable. The spatial variability of the beach erosion can only partly be explained by the patterns of longshore sediment transport, suggesting also a negative sedimentary balance. The results of the sediment erosion model showed that the dam retains more than half of the sediment produced in the basin, irrespective of the scenario used. Thus, it is likely that the effects of the dam on the downstream beach are already apparent.

Aggregate extraction from tidal sandbanks: is dredging with nature an option?: introduction

Sandbanks are considered as primary targets for the marine aggregate industry, not only because of considerations related to resource quality and operational advantages, but also due to the notion that natural sediment transport processes that form and maintain sandbanks are able to counterbalance the loss of sediment due to extraction. This paper introduces: (a) the problems related to the assessment of the impacts of aggregate extraction from tidal sandbanks; and (b) the multidisciplinary and integrated research that was undertaken on the potential for regeneration of the most intensively exploited area of the Kwinte Bank (Flemish Banks, Belgian Continental Shelf), following the cessation of extraction on this part of the sandbank. We assert that the results of a 30-year monitoring of exploitation effects along the Kwinte Bank have put in doubt the universal notion of ‘dredging with nature’. The elongated depressions that have been observed in the most heavily exploited areas provide a clear signal that more detailed information and thorough assessment are required in order to understand and predict the most likely evolution of the bank’s hydro-sedimentary regime and its natural and anthropogenically-induced dynamics.

Assessment of island beach erosion due to sea level rise: the case of the Aegean archipelago (Eastern Mediterranean)

The present contribution constitutes the first comprehensive attempt to (a) record the spatial characteristics of the beaches of the Aegean archipelago (Greece), a critical resource for both the local and national economy, and (b) provide a rapid assessment of the impacts of the longterm and episodic sea level rise (SLR) under different scenarios. Spatial information and other attributes (e.g., presence of coastal protection works and backshore development) of the beaches of the 58 largest islands of the archipelago were obtained on the basis of remote-sensed images available on the web. Ranges of SLR-induced beach retreats under different morphological, sedimentological and hydrodynamic forcing, and SLR scenarios were estimated using suitable ensembles of cross-shore (1-D) morphodynamic models. These ranges, combined with empirically derived estimations of wave runup induced flooding, were then compared with the recorded maximum beach widths to provide ranges of retreat/erosion and flooding at the archipelago scale. The spatial information shows that the Aegean “pocket” beaches may be particularly vulnerable to mean sea level rise (MSLR) and episodic SLRs due to (i) their narrow widths (about 59 % of the beaches have maximum widths < 20 m), (ii) their limited terrestrial sediment supply, (iii) the substantial coastal development and (iv) the limited existing coastal protection. Modeling results indeed project severe impacts under mean and episodic SLRs, which by 2100 could be devastating. For example, under MSLR of 0.5 m – representative concentration pathway (RCP) 4.5 of the Fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate change (IPCC) – a storm-induced sea level rise of 0.6 m is projected to result in a complete erosion of between 31 and 88 % of all beaches (29–87 % of beaches are currently fronting coastal infrastructure and assets), at least temporarily. Our results suggest a very considerable risk which will require significant effort, financial resources and policies/regulation in order to protect/maintain the critical economic resource of the Aegean archipelago. Published by Copernicus Publications on behalf of the European Geosciences Union. 450 I. N. Monioudi et al.: Assessment of island beach erosion due to sea level rise

Shoreline Extraction from Coastal Images Using Chebyshev Polynomials and RBF Neural Networks

In this study, we use a specialized coastal monitoring system for the test case of Faro beach (Portugal), and generate a database consisted of variance coastal images. The images are elaborated in terms of an empirical image thresholding procedure and the Chebyshev polynomials. The resulting polynomial coefficients constitute the input data, while the resulting thresholds the output data. We, then, use the above data set to train a radial basis function network structure with the aid of input-output fuzzy clustering and a steepest descent approach. The implementation of the RBF network leads to an effective detection and extraction of the shoreline of the beach under consideration.

Modeling Beach Rotation Using a Novel Legendre Polynomial Feedforward Neural Network Trained by Nonlinear Constrained Optimization

A Legendre polynomial feedforward neural network is proposed to model/predict beach rotation. The study area is the reef-fronted Ammoudara beach, located at the northern coastline of Crete Island (Greece). Specialized experimental devices were deployed to generate a set of input-output data concerning the inshore bathymetry, the wave conditions and the shoreline position. The presence of the fronting beachrock reef (parallel to the shoreline) increases complexity and imposes high non-linear effects. The use of Legendre polynomials enables the network to capture data non-linearities. However, in order to maintain specific functional requirements, the connection weights must be confined within a pre-determined domain of values; it turns out that the network’s training process constitutes a constrained nonlinear programming problem, solved by the barrier method. The performance of the network is compared to other two neural-based approaches. Simulations show that the proposed network achieves a superior performance, which could be improved if an additional wave parameter (wave direction) was to be included in the input variables.

A Neural-Fuzzy Network Based on Hermite Polynomials to Predict the Coastal Erosion

In this study, we investigate the potential of using a novel neural-fuzzy network to predict the coastal erosion from bathymetry field data taken from the Eresos beach located at the SW coastline of Lesvos island, Greece. The bathymetry data were collected using specialized experimental devices deployed in the study area. To elaborate the data and predict the coastal erosion, we have developed a neural-fuzzy network implemented in three phases. The first phase defines the rule antecedent parts and includes three layers of hidden nodes. The second phase employs truncated Hermite polynomial series to form the rule consequent parts. Finally, the third phase intertwines the information coming from the above phases and infers the network’s output. The performance of the network is compared to other two relative approaches. The simulation study shows that the network achieves an accurate behavior, while outperforming the other methods.

Neuro-Fuzzy Network for Modeling the Shoreline Realignment of the Kamari Beach, Santorini, Greece

In this paper, a novel multiple-layer neuro-fuzzy network is proposed to model/predict shoreline realignment at a highly touristic island beach (Kamari beach, Santorini, Greece). A specialized experimental setup was deployed to generate a set of input-output data that comprise parameters describing the beach morphology and wave conditions and the cross-shore shoreline position at 30 cross-sections of the beach extracted from coastal video imagery, respectively. The proposed network consists of three distinct modules. The first module concerns the network representation of a fuzzy model equipped with a typical inference mechanism. The second module implements a novel competitive learning network to generate initial values for the rule base antecedent parameters. These parameters are, then, used to facilitate the third module that employs particle swarm optimization to perform a stochastic search for optimal parameter estimation. The network is compared favorably to two other neural networks: a radial basis function neural network and a feedforward neural network. Regarding the effectiveness of the proposed network to model shoreline re-alignment, the RMSE found (7.2–7.7 m, depending on the number of rules/nodes), reflects the high variability of the shoreline position of the Kamari beach during the period of observations: the RMSE is of a similar order to the standard deviation (up to 8 m) of the cross-shore shoreline position. The results are encouraging and the effectiveness of the proposed network could be further improved by changes (fine-tuning) of the input variables.

A Hermite neural network incorporating artificial bee colony optimization to model shoreline realignment at a reef-fronted beach

Abstract This paper investigates the potential of using a novel Hermite polynomial neural network to model shoreline realignment along an urban beach fronted by a highly irregular beachrock reef. Modeling takes place on the basis of a number of input variables related to reef morphology and wave forcing, whereas the output variable is time series of shoreline position that have been recorded in high spatio-temporal resolution using a coastal video monitoring system. The main network functionality is the generation of Hermite truncated polynomial series of linear combinations of the input variables, and output is calculated as the weighted sum of these truncated series. It is shown that the proposed network can approximate any continuous function defined on a compact set of the multidimensional Euclidean space to arbitrary accuracy. The network is optimized in terms of a modified artificial bee colony method. For comparative reasons, three more related neural networks have been tested that have been optimized by employing different swarm intelligence-based algorithms. Comparison between the four networks has been carried out by standard performance criteria and detailed parametric statistical analysis. Main results of the study are: (a) polynomial orders 3 and 4 are able to effectively handle reasonably well the high nonlinear effects imposed by the presence of the reef; (b) the statistical analysis indicates that the proposed network outperforms the other networks tested; and (c) model efficiency improves noticeably when beach sections behind reef inlets and/or particular wide sections of the reef that introduce high shoreline variability are not considered.