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

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

“…The neurons in the hidden layer of CO 2 and CO RBFNN MTS models are added up until the desired accuracy is reached for a given spread. The RBFNN MTS database were normalized within a range of [0,1] using Equation to avoid the overflow of network due to large or small weights produced for the dataset and to eliminate the influence of dimensions of data on the network . where,…”

“…Kadiyala and Kumar software review papers successfully demonstrated the modeling of in‐vehicle air quality with univariate, multivariate, and vector time series methods. They extended the research to develop valid hybrid back propagation neural network models with inputs optimized from the use of univariate , multivariate , and vector time series methods. Kadiyala and Kumar developed valid hybrid RBFNN models with inputs optimized from the use of univariate time series methods.…”

Applications of Python to evaluate environmental data science problems

“…Configure ! (12) EXPLORATORY DATA ANALYSIS Generating a summary of the statistics in combination with the development of graphical visualizations for all the variables considered in any study is essential when performing exploratory data analysis. There are multiple methods of importing different types of data into the Spyder IDE for use with Scikit-learn.…”

Vector Time Series‐Based Radial Basis Function Neural Network Modeling of Air Quality Inside a Public Transportation Bus Using Available Software

“…Kadiyala and Kumar software review papers successfully demonstrated the modeling of in‐vehicle air quality with univariate, multivariate, and VTS methods. They extended the research to develop valid hybrid back propagation neural network models with inputs optimized from the use of univariate , multivariate , and vector time series methods. They also developed valid hybrid RBFNN models with inputs optimized from the use of univariate and multivariate time series methods.…”

“…They also developed valid hybrid RBFNN models with inputs optimized from the use of univariate and multivariate time series methods. This study is a follow‐up to the prior in‐vehicle studies on VTS and emphasizes on providing a step‐by‐step approach to the evaluation of the proposed methodology in developing RBFNN VTS models for the monitored contaminants of carbon dioxide (CO 2 ) and carbon monoxide (CO) inside a 20% grade biodiesel (BD20) operated public transportation bus in Toledo, Ohio using available software (Microsoft ® Excel 2013, SPSS Statistics 17, MathWorks ® MATLAB 2011b, BOOT v2.0, MINITAB 16). Environmental modelers and managers may use their own datasets to develop corresponding RBFNN VTS models by following the steps outlined in this software review article.…”