Article Sidebar
Article Details
Copyright belongs to the Faculty of Engineering, University of Nigeria, Nsukka, Nigeria
The contents of the articles are the sole opinion of the author(s) and not of NIJOTECH.
NIJOTECH allows open access for distribution of the published articles in any media so long as whole (not part) of articles are distributed.
A copyright and statement of originality documents will need to be filled out clearly and signed prior to publication of an accepted article. The Copyright form can be downloaded from https://www.nijotech.com/downloads/COPYRIGHT%20FORM.pdf while the Statement of Originality is in Statement of Originality.pdf (nijotech.com)
For articles that were developed from funded research, a clear acknowledgement of such support should be mentioned in the article with relevant references. Authors are expected to provide complete information on the sponsorship and intellectual property rights of the article together with all exceptions.
It is forbidden to publish the same research report in more than one journal.
Main Article Content
Prediction of indoor Total Volatile Organic Compound in a university hostel using a Neural Network Model
Abstract
The study predicted the concentration of indoor total volatile organic compounds (TVOC) concentration in a randomly selected room at the Umar Kabir male Hostel located at the Federal University of Agriculture, Abeokuta, Nigeria. Readings were taken using an active sampler to measure particulate matter, PM (1.0, 2.5 and 10), TVOC, Relative Humidity (RH), Temperature and Formaldehyde. Two network types namely; feedforward back propagation and the cascaded forward back propagation were adopted randomly to predict TVOC as an output variable using data set generated from six different parameters mentioned earlier. The best performing neural network was the cascaded feed forward with a coefficient of determination of 0.98 which exhibited the lowest mean square error of 0.000124 with a network structure of 6-15-1-1. The results show the ability of Artificial Neural Networks to map inputs and outputs in complex non-linear situations such as the existence of volatile compounds in the atmosphere. It can be adopted for monitoring environmental systems by engineers and public health workers, stakeholders can use such models for initiating environmental related policies aimed at safeguarding human health.
