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Modelling the soundscape quality of urban waterfronts by artificial neural networks

机译：用人工神经网络模拟城市滨水区的声景质量

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The renewal of the urban waterfronts has become a major focus of attention for politicians and decision makers in the city's management programs. The recognition of the patterns that define the waterfronts' identity is essential to select new strategies of intervention for the environmental recovery. In order to create adequate environments for everyday life within a sustainable development, new links between human senses, human perception and design need to be created. Within this wide approach, the landscape and the soundscape play a significant role and can become a key driving force in the implementation of the changes. New techniques have to be tested to identify the sonic and visual parameters capable to explain the specificity of a waterfront. With this purpose, an artificial neural network (ANN) was developed, and the relative importance of the input variables was evaluated. The collected database was also analysed by multiple linear regression (MLR) to compare the outcomes of both models. The urban waterfront of Naples (Italy) was chosen as case study. The results obtained show that the performance of the neural network is better than the one of the linear regression (rANN = 0.949, rMLR = 0.639). The interpretation of the relative importance method is also quite satisfactory in the ANN. (C) 2016 Elsevier Ltd. All rights reserved.
机译：城市滨水区的更新已成为城市管理计划中政客和决策者关注的重点。识别定义滨水区特征的模式对于选择新的环境恢复干预策略至关重要。为了在可持续发展中为日常生活创造适当的环境，需要在人类感官，人类知觉和设计之间建立新的联系。在这种广泛的方法中，景观和声音景观起着重要作用，并且可以成为实施变更的关键驱动力。必须测试新技术以识别能够解释滨水区特殊性的声音和视觉参数。为此，开发了人工神经网络（ANN），并评估了输入变量的相对重要性。还通过多元线性回归（MLR）分析了收集的数据库，以比较两个模型的结果。选择了那不勒斯（意大利）的城市滨水区作为案例研究。获得的结果表明，神经网络的性能优于线性回归之一（rANN = 0.949，rMLR = 0.639）。相对重要性方法的解释在ANN中也很令人满意。 （C）2016 Elsevier Ltd.保留所有权利。

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