Open AccessPrediction of PM2.5 and PM10 parameters using artificial neural network: a case study in Kemayoran, Jakarta
Author(s) -
A M M B Putra,
Martarizal,
Richard Mahendra Putra
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1528/1/012036
Subject(s) - artificial neural network , air quality index , wind speed , meteorology , humidity , environmental science , air temperature , air humidity , computer science , machine learning , geography
It was recorded that in August 2019 the case of acute respiratory infection in Indonesia had doubled compared to the previous months. This is in line with the increasing levels of PM10 and PM2.5 in several regions in Indonesia. In the end the public is increasingly aware of the importance of air quality information. Prediction of air quality will greatly help the public to anticipate the dangers of declining air quality. The use of Artificial Neural Network can be a solution in making daily air quality forecasts whose parameters are not linear. This research shows that utilization of historical data parameters of temperature, humidity, air pressure, rainfall, sun exposure and wind speed as well as BMKG’s PM10 and PM2.5 data is able to produce forecasting modeling for PM2.5 and PM10 concentrations in the Kemayoran area, Jakarta by utilizing Artificial Neural Network Modeling. The result is success to make prediction of PM2.5 and PM10 and it will be better if more historical data applied.