University of Notre Dame, Civil Engineering & Geological Sciences & Environmental Fluid Dynamics Laboratories, Notre Dame, IN 46446, USA.
Environ Pollut. 2012 Apr;163:62-7. doi: 10.1016/j.envpol.2011.12.018. Epub 2012 Jan 11.
Deterministic photochemical air quality models are commonly used for regulatory management and planning of urban airsheds. These models are complex, computer intensive, and hence are prohibitively expensive for routine air quality predictions. Stochastic methods are becoming increasingly popular as an alternative, which relegate decision making to artificial intelligence based on Neural Networks that are made of artificial neurons or 'nodes' capable of 'learning through training' via historic data. A Neural Network was used to predict particulate matter concentration at a regulatory monitoring site in Phoenix, Arizona; its development, efficacy as a predictive tool and performance vis-à-vis a commonly used regulatory photochemical model are described in this paper. It is concluded that Neural Networks are much easier, quicker and economical to implement without compromising the accuracy of predictions. Neural Networks can be used to develop rapid air quality warning systems based on a network of automated monitoring stations.
确定性光化学空气质量模型通常用于城市空气流域的监管管理和规划。这些模型非常复杂，计算密集，因此对于常规空气质量预测来说过于昂贵。随机方法作为替代方法越来越受欢迎，它将决策交给基于神经网络的人工智能，这些神经网络由人工神经元或“节点”组成，能够通过历史数据进行“通过训练学习”。本文介绍了一种用于预测亚利桑那州凤凰城监管监测点颗粒物浓度的神经网络；描述了其开发、作为预测工具的功效以及与常用的监管光化学模型的性能。结论是，神经网络更容易、更快且更经济，实施起来不会影响预测的准确性。可以使用神经网络来开发基于自动化监测站网络的快速空气质量预警系统。
