A combined model for tsunami wave propagation, dispersion, breaking and fluid-structure interaction

In this work, a fully combined tsunami model was developed, by coupling a sequence of 3 distinct numerical models, with different characteristics, for particular phases of the tsunami lifecycle. The computational codes that compose the fully combined tsunami model are the GeoClaw code, the FUNWAVE-TVD code and the OpenFOAM code, via the olaFlow solver. The coupling of GeoClaw with FUNWAVE-TVD was designated as the combined model 1 (CM1) and the combination of FUNWAVE-TVD/CM1 with the CFD code was designated as the combined model 2 (CM2). The full combination of both CM1 and CM2 resulted in the fully combined tsunami model CM. To achieve the coupling between numerical models, individual coupling methodologies were approached, tested and analysed. For the CM1, we choose a refined covered gauge domain coupling methodology and for the CM2 a timeSeries condition coupling methodology was used, which applied waveType wavemaker and the waveTheory tveta, from the olaFlow module. The validation of the individual numerical codes and of the combined model patches was performed with both numerical and physical test cases. Several physical experiments were carried out to generate both solitary and N-waves and a novel first-order theoretical formulation, necessary to generate N-waves experimentally, by means of a piston wave generating system, was developed and detailed in this work. The large-scale physical experiments were performed in the wave basin and in a beach composed by a 1:15 plane slope and a 1:30 plane slope. The generated solitary and N-waves were classified according to their Stokes number. Experimental free surface elevation, run-in, run-up and pressure measurements were retrieved from the physical experiments. Run-in, run-up and pressure laws were proposed for solitary waves and N-waves respectively. The experimental measurements were compared with numerical simulation results. The objectives of the development of the fully combined tsunami model were (1) to join the advantages of the individual models in a single one, attempting to increase the accuracy, efficiency and regime of validity, and (2) to bring a contribution in the tackling of some of the existing problems and challenges of tsunami science, such as the frequency dispersion in long distance tsunami propagation, the complex tsunami on land propagation and fluid flow interactions with river courses and with the coastal and urban areas. The fully combined tsunami model CM simulation results for a Mω 8.5 Earthquake and Tsunami hitting the Portuguese coast showed the ability of the combined model to cover all the tsunami stages. We show that with a 2DV simulation of the CFD code for the Marina of Cascais bathymetric and topographic profile it was possible to observe the vortices behind the breakwater. The analysis of the free surface elevation, velocities and pressure of the tsunami waves was performed. This allowed us to understand the consequence of three diferent tsunami waves scenarios after the breakwater zone. It was possible to draw some brief conclusions considering the tsunami impact. The fully combined tsunami model achieved in this work is a novelty, since it is composed by a sequence of distinct numerical models, including the three-dimensional component granted by the CFD code. With this combined model, it is possible to perform the simulation of real case tsunami events and hypothetical scenarios, applying real or synthetic tsunami-type wave profiles, studying and researching the impact and the tsunami interaction with the coastal areas

Traffic State Prediction Using 1-Dimensional Convolution Neural Networks and Long Short-Term Memory

Traffic prediction is a vitally important keystone of an intelligent transportation system (ITS). It aims to improve travel route selection, reduce overall carbon emissions, mitigate congestion, and enhance safety. However, efficiently modelling traffic flow is challenging due to its dynamic and non-linear behaviour. With the availability of a vast number of data samples, deep neural network-based models are best suited to solve these challenges. However, conventional network-based models lack robustness and accuracy because of their incapability to capture traffic’s spatial and temporal correlations. Besides, they usually require data from adjacent roads to achieve accurate predictions. Hence, this article presents a one-dimensional (1D) convolution neural network (CNN) and long short-term memory (LSTM)-based traffic state prediction model, which was evaluated using the Zenodo and PeMS datasets. The model used three stacked layers of 1D CNN, and LSTM with a logarithmic hyperbolic cosine loss function. The 1D CNN layers extract the features from the data, and the goodness of the LSTM is used to remember the past events to leverage them for the learnt features for traffic state prediction. A comparative performance analysis of the proposed model against support vector regression, standard LSTM, gated recurrent units (GRUs), and CNN and GRU-based models under the same conditions is also presented. The results demonstrate very encouraging performance of the proposed model, improving the mean absolute error, root mean squared error, mean percentage absolute error, and coefficient of determination scores by a mean of 16.97%, 52.1%, 54.15%, and 7.87%, respectively, relative to the baselines under comparison