Open AccessAn artificial neural network model for forecasting air pollution
Author(s) -
Wan Dorishah Wan Abdul Manan,
Norantonina Abdullah
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1176/1/012032
Subject(s) - artificial neural network , hyperparameter , multilayer perceptron , air quality index , computer science , mean squared error , air pollution index , python (programming language) , machine learning , air pollution , artificial intelligence , data mining , statistics , meteorology , mathematics , geography , chemistry , organic chemistry , operating system
Air pollution has caused a lot of problems to people in terms of health and economy, as well as affecting various floras and faunas. Thus, monitoring air quality levels and forecasting the occurrence of air pollution is important so that preventive measures could be taken. In this study, Artificial Neural Network (ANN) was used to forecast the air pollution index (API) in Kuala Terengganu. This study focused on the prediction of API based on 5 years of data of main pollutants’ daily concentration taken at the air quality monitoring station in Kuala Terengganu. The aim was to develop an Artificial Neural Network model that can predict the API. A Multilayer Perceptron Neural Network (MLP) engine was implemented in the system prototype and developed by using Keras, a deep learning library in Python. The model’s performance was evaluated using the Mean Squared Error (MSE) statistical method and functionality tests were done to ensure the prototype was working correctly. In order to get a good performance model, a hyperparameter tuning process was carried out and the best hyperparameters values were selected. The performance of the model in making predictions was good as the MSE value was 0.0195.