Forecasting Air Quality Indeks Using Long Short Term Memory

Irfan Wahyu Ramadhani; Filmada Ocky Saputra; Ricardus Anggi Pramunendar; Galuh Wilujeng Saraswati; Nurul Anisa Sri Winarsih; Muhammad Syaifur Rohman; Danny Oka Ratmana; Guruh Fajar Shidik

doi:10.30871/jaic.v8i1.7402

Irfan Wahyu Ramadhani Dian Nuswantoro University
Filmada Ocky Saputra Dian Nuswantoro University
Ricardus Anggi Pramunendar Dian Nuswantoro University
Galuh Wilujeng Saraswati Dian Nuswantoro University
Nurul Anisa Sri Winarsih Dian Nuswantoro University
Muhammad Syaifur Rohman Dian Nuswantoro University
Danny Oka Ratmana Dian Nuswantoro University
Guruh Fajar Shidik Dian Nuswantoro University

DOI: https://doi.org/10.30871/jaic.v8i1.7402

Keywords: Air Quality Index, Air Pollution, Forecasting, LSTM, Sports

Abstract

Exercise offers significant physical and mental health benefits. However, undetected air pollution can have a negative impact on individual health, especially lung health when doing physical activity in crowded sports venues. This study addresses the need for accurate air quality predictions in such environments. Using the Long Short-Term Memory (LSTM) method or what is known as high performance time series prediction, this research focuses on forecasting the Air Quality Index (AQI) around crowded sports venues and its supporting parameters such as ozone gas, carbon dioxide, etc. -others as internal factors, without involving external factors causing the increase in AQI. Preprocessing of the data involves removing zero values and calculating correlations with AQI and the final step performs calculations with the LSTM model. The LSTM model which adds tuning parameters, namely with epoch 100, learning rate with a value of 0.001, and batch size with a value of 64, consistently shows a reduction in losses. The best results from the AQI, PM2.5, and PM10 features based on performance are MSE with the smallest value of 6.045, RMSE with the smallest value of 4.283, and MAE with a value of 2.757.

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