No abstract

This article focuses on the optimal architecture of the neural network for determining the three characteristic points of the bars (starting, crest and final point). For the definition of the network, precision profiles, sedimentological and wave data were used. A total of 209 profiles taken for 22 years was used. The inputs were analysed and selected considering the variables that influenced the formation of the bars and their movement. For the selection of the optimal model different architectures were studied, generating 50 models for each of them and selecting with better results and with the smaller number of neurons in the hidden layer. To evaluate the performance of the model, various statistical errors were used (absolute error, mean magnitude of relative error and percentage relative error), with an average absolute error of 17.3 m in the distances to the coast and 0.26 m in the depths. The results were compared with equations currently employed (Table 1), which show that the errors generated by the ANN (Artificial Neural Network) are much lower (per example the MAPE committed by the proposed equation for distance to shore of the crest is 47%, while the ANN is made of 29%).

No abstract

The paper describes the training, validation, testing and application of models of artificial neural networks (ANN) for computing the cross-shore beach profile of the sand beaches of the province of Valencia (Spain). Sixty ANN models were generated by modifying both the input variables as the number of neurons in the hidden layer. The input variables consist of wave data

Monitoring of the quality of bathing water in line with the European Commission bathing water directive (Directive 2006/7/EC) is a significant economic expense for those countries with great lengths of coastline. In this study a numerical model based on finite elements is generated whose objective is partially substituting the microbiological analysis of the quality of coastal bathing waters. According to a study of the concentration of Escherichia coli in 299 Spanish Mediterranean beaches, it was established that the most important variables that influence the concentration are: monthly sunshine hours, mean monthly precipitation, number of goat cattle heads, population density, presence of Posidonia oceanica, UV, urbanization level, type of sediment, wastewater treatment ratio, salinity, distance to the nearest discharge, and wave height perpendicular to the coast. Using these variables, a model with an absolute error of 10.6±1.5CFU/100ml is achieved. With this model, if there are no significant changes in the beach environment and the variables remain more or less stable, the concentration of E. coli in bathing water can be determined, performing only specific microbiological analyses to verify the water quality.

No abstract

Mathematical models used for the understanding of coastal seabed morphology play a key role in beach nourishment projects. These projects have become the fundamental strategy for coastal maintenance during the last few years. Accordingly, the accuracy of these models is vital to optimize the costs of coastal regeneration projects. Planning of such interventions requires methodologies that do not generate uncertainties in their interpretation. A study and comparison of mathematical simulation models of the coastline is carried out in this paper, as well as elements that are part of the model that are a source of uncertainty. The Equilibrium Profile (EP) and the offshore limit corresponding A C C E P T E D M A N U S C R I P T ACCEPTED MANUSCRIPTto the Depth of Closure (DoC) have been analysed taking into account different timescale ranges. The results have thus been compared using data sets from three different periods which are identified as present, past and future. Accuracy in data collection for the beach profiles and the definition of the median grain size calculation using collected samples are the two main factors that have been taken into account in this paper. These data can generate high uncertainties and can produce a lack of accuracy in nourishment projects. Together they can generate excessive costs due to possible excess or shortage of sand used for the nourishment.The main goal of this paper is the development of a new methodology to increase the accuracy of the existing equilibrium beach profile models, providing an improvement to the inputs used in such models and in the fitting of the formulae used to obtain seabed shape. This new methodology has been applied and tested on Valencia's beaches.

System modeling is a main task in several research fields. The development of numerical models is of crucial importance at the present because of its wide use in the applications of the generically named machine learning technology, including different kinds of neural networks, random field models, and kernel-based methodologies. However, some problems involving the reliability of their predictions are common to their use in the real world. Octahedric regression is a kernel averaged methodology developed by the authors that tries to simplify the entire process from raw data acquisition to model generation. A discussion about the treatment and prevention of overfitting is presented and, as a result, models are obtained that allow for the measurement of this effect. In this paper, this methodology is applied to the problem of estimating the energetic needs of different buildings according to their principal characteristics, a problem that has importance in architecture and civil and environmental engineering due to increasing concerns about energetic efficiency and ecological footprint.