Multivariate time series based radial basis function neural network modeling of air quality inside a public transportation bus using available software

作者：Kadiyala Akhil; Kumar Ashok^*

来源：Environmental Progress & Sustainable Energy, 2016, 35(4): 931-935.

DOI：10.1002/ep.12387

• radial basis function
• neural networks
• time series
• ARMAX/ARIMAX
• indoor air quality
• in-bus air contaminants
• biodiesel
• public transportation buses
• operational performance measures

The development of valid air quality models (adressing the complex interrelationships between air contaminants and influential variables) is an integral component to developing good indoor air quality (IAQ) management strategies. With an increase in the capabilities of computational resources to process large datasets utilizing the hybrid mathematical calculations, environmentalists are now better equipped to develop and use hybrid IAQ models. This software review paper presents the development and evaluation of one such hybrid IAQ model, referred to as the multivariate time series based radial basis function neural network models (multivariate time series+radial basis function neural networks) for the monitored contaminants of carbon dioxide and carbon monoxide inside a public transportation bus using available software.

• 出版日期2016-7