Open AccessThree-Hour-Ahead of Multiple Linear Regression (MLR) Models for Particulate Matter (PM10) Forecasting
Author(s) -
Amalina Abu Mansor,
Samsuri Abdullah,
Nazri Che Dom,
Nur Nazmi Liyana Mohd Napi,
Ali Najah Ahmed,
Marzuki Ismail,
Mohammad Fakhratul Ridwan Zulkifli
Publication year - 2021
Publication title -
international journal of design and nature and ecodynamics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.193
H-Index - 13
eISSN - 1755-7445
pISSN - 1755-7437
DOI - 10.18280/ijdne.160107
Subject(s) - particulates , linear regression , statistics , meteorology , regression analysis , environmental science , air pollutants , variance (accounting) , human health , mathematics , air pollution , econometrics , atmospheric sciences , geography , environmental health , business , ecology , medicine , accounting , geology , biology
The increase of air pollutants emission through anthropogenic activities and natural phenomena in the atmosphere can give an adverse impact on human health especially to some groups of people such as children, the elderly, and people that have cardiovascular problems. Multiple Linear Regression (MLR) model establishments for the particulate matter (PM10) forecasting can be useful, as it provides early warning information to the local authorities and the communities. We aim to develop MLR models for PM10 forecasting in Peninsular Malaysia, specifically in the southern part. In this study, the hourly data of PM10, meteorological factors, and gaseous pollutants from the year 2009-2011 had been used. As a result, the next first hour of the MLR prediction model, PM10,t+1 has been selected as the best-fitted model as compared to the second and third prediction hour models, PM10,t+2, and PM10,t+3, respectively. The PM10,t+1 model was explained 61.4% (R2=0.614) variance in the data which is higher compared to model PM10,t+2 and PM10,t+3 with 42.3% (R2=0.423) and 34.7% (R2=0.347), respectively. Thus, the validation of PM10, t+1 model also has a high accuracy value of R2 (55.1%) as compared to the other two models. We conclude that the development of MLR models is adequate for PM10 forecasting in the industrial area.