Vector‐time‐series‐based back propagation neural network modeling of air quality inside a public transportation bus using available software

Abstract

This software review article describes the development of hybrid indoor air quality (IAQ) models by integrating the use of vector time series (VTS) and back propagation neural network (BPNN) modeling approaches. BPNNs are the most widely adopted artificial neural networks that serve as universal approximators and provide a flexible computational platform to integrate conventional modeling approaches like time series in developing hybrid environmental prediction (or forecasting) models. The hybrid VTS‐based BPNN IAQ prediction models developed and validated in this study using available software are based on the monitoried in‐bus contaminants of carbon dioxide and carbon monoxide. © 2015 American Institute of Chemical Engineers Environ Prog, 35: 7–13, 2016