Indoor Pollutant Classification Modeling using Relevant Sensors under Thermodynamic Conditions with Multilayer Perceptron Hyperparameter Tuning

Abstract

Air pollutants that are generated from indoor sources such as cigarettes, cleaning products, air fresheners, etc. impact human health. These sources are usually safe but exposure beyond the recommended standards could be hazardous to health. Due to this fact, people started to use technology to monitor indoor air quality (IAQ) but have no capability of recognizing pollutant sources. This research is an improvement in building a classification model for recognizing pollutant sources using the multilayer perceptron. The current research model receives four data parameters under warm & humid and cool & dry conditions compared to nine parameters of the previous literature in detecting five pollutant sources. The classification model was optimized using GridSearchCV to obtain the best combination of hyperparameters while giving the best-fit model accuracy, loss, and computational time. The tuned classification model gives an accuracy of 98.9% and a loss function value of 0.0986 under the number of epochs equal to 50. In comparison with the previous research, the accuracy was 100% with the number of epochs equal to 1000. Computational time was greatly reduced at the same time giving the best-fit accuracy and loss function values without incurring the problem of overfitting.