Hydrodynamic model intercomparison for the patos lagoon (Brazil)

An intercomparison study is carried out between two models with different formulations and spatial discretizations in order to overcome the limitations posed by the standard calibration and validation procedures and improve confidence in the hydrodynamic results for the Patos Lagoon. Numerical simulations were carried out applying the TELEMAC and MOHID models, based on the same boundary conditions and identical calibration coefficients so differences in calculated flow conditions result from the formulations and parameterizations of each model. Results from both models are compared with measurements from three stations inside the lagoon. Preliminary results indicate that both models compare well with the measurements and with each other. These results increase the confidence on hydrodynamic results for the Patos Lagoon and provide the first step towards water quality studies for the area.

Sediment dynamics and tidal inlet relocation in mixed-energy settings: the case of Ancão inlet (Algarve - Portugal)

Tese de dout., Ciências do Mar, Faculdade de Ciências do Mar e do Ambiente, Univ. do Algarve, 2003

Model comparison for temperature estimation inside buildings

This paper presents a comparison between a physical model and an artificial neural network model (NN) for temperature estimation inside a building room. Despite the obvious advantages of the physical model for structure optimisation purposes, this paper will test the performance of neural models for inside temperature estimation. The great advantage of the NN model is a big reduction of human effort time, because it is not needed to develop the structural geometry and structural thermal capacities and to simulate, which consumes a great human effort and great computation time. The NN model deals with this problem as a “black box” problem. We describe the use of the Radial Basis Function (RBF), the training method and a multi-objective genetic algorithm for optimisation/selection of the RBF neural network inputs and number of neurons.

Application of radial basis function neural networks to a greenhouse inside air temperature model

The problem with the adequacy of radial basis function neural networks to model the inside air temperature as a function of the outside air temperature and solar radiation, and the inside relative humidity in an hydroponic greenhouse is addressed.

Predicting the greenhouse inside air temperature with RBF neural networks

The application of the Radial Basis Function (RBF) Neural Network (NN) to greenhouse inside air temperature modelling has been previously investigated (Ferreira et al., 2000a). In those studies, the inside air temperature is modelled as a function of the inside relative humidity and of the outside temperature and solar radiation. A second-order model structure previously selected (Cunha et al., 1996) in the context of dynamic temperature models identification, is used.