摘要 :
Today the aviation industry is strongly exposed to criticism based on its impact on climate change. While this is a very important challenge, the industry and especially airports also have a second important challenge: noise impac...
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Today the aviation industry is strongly exposed to criticism based on its impact on climate change. While this is a very important challenge, the industry and especially airports also have a second important challenge: noise impact, which needs to be managed more strategically and proactively.This paper discusses the key factors that influence noise impact, looking at the influence of the evolution of passenger numbers and movements, the influence of the time slots used for these movements and the movements of the lowest noise class aircraft. It will also examine why the permissiblenoise curve currently under discussion is much larger than that of the 2009 noise register, the first definition by the Swiss aviation regulator of the foreseen noise impact of Geneva Airport. The paper will investigate to what extent Geneva Airport can influence the noise curves through openinghours and slot availability, as well as ways in which the airport might achieve the 20 per cent noise reduction announced in the aeronautical infrastructure sector plan of 2018. Finally, it will suggest a methodology to produce a forecast of noise impact based on key variables such as movementsduring specific hours of the day and the percentage of movements of aircraft of the lowest noise class.
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摘要 :
In this paper Product-Units neural networks (PUNNs), which probably have never been used within the field of hydrology, are introduced and applied for catchment runoff forecasting in cold climate zones. This type of neural network...
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In this paper Product-Units neural networks (PUNNs), which probably have never been used within the field of hydrology, are introduced and applied for catchment runoff forecasting in cold climate zones. This type of neural networks, a subclass of higher order neural networks uses product nodes with inputs raised to exponential weights in one layer and well-known summation nodes in another layer. The present paper empirically shows that PUNNs with unbounded weights are difficult to train and do not perform well for catchment runoff forecasting. However, a very good predictive performance may be achieved when the weights are bounded within [-1,1] interval. Several variants of optimization methods, mostly Differential Evolution-based algorithms, and a few approaches enabling good generalization capabilities of neural networks are compared in order to select the appropriate technique for PUNNs training. PUNNs with parameters bounded within [-1,1] interval are shown to outperform Multi-Layer Perceptron neural networks and HBV conceptual model for runoff forecasting case study at Annapolis River, Nova Scotia, Canada. Gradient-based Levenberg-Marquardt algorithm and Evolutionary Computation-based Differential Evolution with Global and Local Neighborhood method turn out to be the most successful among the tested training algorithms. Surprisingly, in the case of Product-Units neural networks with weights bounded within [-1,1] interval using noise injection or early stopping do not improve the results obtained when no method to avoid overfitting is used.
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