CO and PM10 Prediction Model based on Air Quality Index Considering Meteorological Factors in DKI Jakarta using LSTM
(1) Department of Computer Science, IPB University, Indonesia
(2) Department of Computer Science, IPB University, Indonesia
(3) Department of Computer Science, IPB University, Indonesia
Abstract
Purpose: This study aimed to make CO and PM10 prediction models in DKI Jakarta using Long Short-Term Memory (LSTM) with and without meteorological variables, consisting of wind speed, solar radiation, air humidity, and air temperature to see how far these variables affect the model.
Methods: The method chosen in this study is LSTM recurrent neural network as one of the best algorithms that perform better in predicting time series. The LSTM models in this study were used to compare the performance between modeling using meteorological factors and without meteorological factors.
Result: The results show that the use of meteorological predictors in the CO prediction model has no effect on the model used, but the use of meteorological predictors influences the PM10 prediction model. The prediction model with meteorological predictors produces a smaller RMSE and stronger correlation coefficient than modeling without using meteorological predictors.
Novelty: In this paper, a comparison between the prediction model of CO and PM10 has been conducted with two scenarios, modeling with meteorological factors and modeling without meteorological factors. After the comparative analysis was done, it was found that the meteorological variables do not affect the CO index in 5 air quality monitoring stations in DKI Jakarta. It can be said that the level of CO pollutants tends to be influenced by factors other than meteorological factors.
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