Open AccessComparison of Suspended Particulate Matter Prediction Based on Linear and Non-Linear Models
Author(s) -
Sri Sumiyati,
Budi Warsito
Publication year - 2020
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/448/1/012029
Subject(s) - autoregressive integrated moving average , particulates , artificial neural network , linear regression , mist , linear model , predictive modelling , computer science , machine learning , data mining , statistics , meteorology , mathematics , time series , geography , ecology , biology
Air pollution has been a serious problem in recent years. Air pollutants consist of gaseous pollutants, odours, and suspended particulate matter (SPM) such as dust, fumes, mist, and smoke. SPM has the potential to cause environmental and health problems. With the aim to anticipate the impact, SPM prediction from time to time is needed. In this research, we compared four models for predicting the SPM data. The two linear models selected were ARIMA and wavelet whereas the two non-linear models were neural networks based models, i.e. Feed Forward Neural Network (FFNN) and General Regression Neural Network (GRNN). All four models are built with the same input, which were the past data at the same lagged time based on the best ARIMA model. By using lagged time data as input, the goal is to predict the current of SPM data. Model accuracy is measured based on RMSE values, both in training and testing data. Data processing has provided interesting results that show the superiority of nonlinear models over linear models, especially in the training data.