Evaluation of Artificial Neural Network Model for Predicting Nitrogen Oxides (NOₓ) Concentration

Muhammad Farrih Mahabbataka Arsyada, Raras Tyasnurita

Abstract


Nitrogen Oxides (NOₓ) are air pollutants that require serious attention due to their potential negative impacts on human health, the environment, and the economy. This research is crucial to provide accurate predictive models of NOₓ concentration, which can serve as a foundation for decision-making and effective air pollution mitigation measures. The objective of this study is to evaluate several artificial neural network (ANN) models to determine the most effective model for accurately predicting NOₓ concentrations. One of the methods used for predicting air pollution data, such as NOₓ, is artificial neural networks (ANN). In this study, four ANN models were constructed and evaluated: Feed Forward Neural Network (FNN), Time Lagged Neural Network (TLNN), Seasonal Artificial Neural Network (SANN), and Long Short-Term Memory (LSTM). The models predict NOₓ concentration using data from the air quality dataset provided by the UCI Machine Learning Repository. Testing results indicate that the LSTM model performs best, achieving the lowest error value, characterized by 24 input nodes, three hidden nodes, one output node, and 300 training epochs. The RMSE values for LSTM, FNN, TLNN, and SANN are 57.3, 62.8, 64, and 89, respectively.

Keywords


LSTM; artificial neural networks; air pollution; NOₓ concentration; predictive model

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References


G. Indonesia, “Laporan Kualitas Udara Dunia IQAir 2023: Indonesia Terburuk se- Asia Tenggara,” greenpeace.org, Mar. 19, 2024. https://www.greenpeace.org/indonesia/siaran-pers-2/58036/laporan-kualitas-udara-dunia-iqair-2023-indonesia-terburuk-se-asia-tenggara/ (accessed Mar. 14, 2025).

Rokom, “Polusi Udara Sebabkan Angka Penyakit Respirasi Tinggi,” kemkes.go.id, Apr. 04, 2023. https://sehatnegeriku.kemkes.go.id/baca/rilis-media/20230404/2642721/polusi-udara-sebabkan-angka-penyakit-respirasi-tinggi/ (accessed Mar. 14, 2025).

H. Haruna, L. Lahming, F. Amir, dan A. R. Asrib, “Pencemaran Udara Akibat Gas Buang Kendaraan Bermotor dan Dampaknya terhadap Kesehatan,” UNM Environmental Journals, vol. 2, no. 2, p. 57, Apr. 2019, doi: https://doi.org/10.26858/uej.v2i2.10092.

G. M. Maula, “Efektivitas Implementasi Kebijakan Pengendalian Pencemaran Udara di Indonesia,” Savana: Indonesian Journal of Natural Resources and Environmental Law, vol. 1, no. 2, pp. 145–159, Aug. 2024, doi: https://doi.org/10.25134/savana.v1i2.223.

NOAA, Air Quality Forecasting: A Review of Federal Programs and Research Needs. Boulder, CO: National Oceanic and Atmospheric Administration, 2001. [Online]. Available: https://www.esrl.noaa.gov/csd/aqrsd/reports/forecasting.pdf (accessed: Mar. 15, 2025).

L. S. Rini, O. T. Karya, dan F. Sirait, “Implementasi Jaringan Syaraf Tiruan pada Pendeteksi Keaslian dan Nominal Uang,” Jurnal Teknologi Elektro, vol. 12, no. 2, p. 65, Jul. 2021, doi: https://doi.org/10.22441/jte.2021.v12i2.004.

J. Veri, S. Surmayanti, dan G. Guslendra, “Prediksi Harga Minyak Mentah menggunakan Jaringan Syaraf Tiruan,” MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer, vol. 21, no. 3, pp. 503–512, Jul. 2022, doi: https://doi.org/10.30812/matrik.v21i3.1382.

S. Agarwal, S. Sharma, S. R., M. H. Rahman, S. Vranckx, B. Maiheu, L. Blyth, S. Janssen, P. Gargava, V. K. Shukla, S. Batra, “Air Quality Forecasting using Artificial Neural Networks with Real Time Dynamic Error Correction in Highly Polluted Regions,” Science of the Total Environment, vol. 735, no. 735, pp. 139454–139454, Sep. 2020, doi: https://doi.org/10.1016/j.scitotenv.2020.139454.

L. Zhang, P. Liu, L. Zhao, G. Wang, W. Zhang, dan J. Liu, “Air Quality Predictions with a Semi-Supervised Bidirectional LSTM Neural Network,” Atmospheric Pollution Research, vol. 12, no. 1, pp. 328–339, Jan. 2021, doi: https://doi.org/10.1016/j.apr.2020.09.003.

S. Agarwa, S. Sharma, R. Suresh, Md. H. Rahman, S. Vranckx, B. Maiheu, L. Blyth, S. Janssen, P. Gargava, V. K. Shukla, S. D. Batra, “Air Quality Forecasting using Artificial Neural Networks with Real Time Dynamic Error Correction in Highly Polluted Regions,” Science of the Total Environment, vol. 735, pp. 139454–139454, Sep. 2020, doi: https://doi.org/10.1016/j.scitotenv.2020.139454.

M. A. Faishol, “Analisis Data Runtun Waktu Prediksi Polusi Udara di Kota Surabaya menggunakan Deep Learning RNN-LSTM,” Thesis, Sepuluh Nopember Institute of Technology, 2020. Accessed: Jan. 31, 2024. [Online]. Available: http://repository.its.ac.id/id/eprint/76659

Y. Karyadi, “Prediksi Kualitas Udara dengan Metoda LSTM, Bidirectional LSTM, dan GRU,” JATISI (Jurnal Teknik Informatika dan Sistem Informasi), vol. 9, no. 1, pp. 671–684, Mar. 2022, doi: https://doi.org/10.35957/jatisi.v9i1.1588.

B. K. Hidayatullah, M. Kallista, and C. Setianingsih, “Prediksi Indeks Standar Pencemaran Udara menggunakan Metode Long Short-Term Memory berbasis Web (Studi Kasus pada Kota Jakarta),” in e-Proceeding of Engineering , Telkom University, Jun. 2022, pp. 1274–1255.

D. Seng, Q. Zhang, X. Zhang, G. Chen, and X. Chen, “Spatiotemporal Prediction of Air Quality Based on LSTM Neural Network,” Alexandria Engineering Journal, vol. 60, no. 2, pp. 2021–2032, Apr. 2021, doi: https://doi.org/10.1016/j.aej.2020.12.009.

Q. Guo, Z. He, S. Li, X. Li, J. Meng, Z. Hou, J. Liu, Y. Chen, “Air Pollution Forecasting using Artificial and Wavelet Neural Networks with Meteorological Conditions,” Aerosol and Air Quality Research, vol. 20, no. 6, pp. 1429–1439, 2020, doi: https://doi.org/10.4209/aaqr.2020.03.0097.

T. Xayasouk, H. Lee, and G. Lee, “Air Pollution Prediction using Long Short-Term Memory (LSTM) and Deep Autoencoder (DAE) Models,” Sustainability, vol. 12, no. 6, p. 2570, Mar. 2020, doi: https://doi.org/10.3390/su12062570.

S. R. Shams, A. Jahani, S. Kalantary, M. Moeinaddini, and N. Khorasani, “The Evaluation on Artificial Neural Networks (ANN) and Multiple Linear Regressions (MLR) Models for Predicting SO2 Concentration,” Urban Climate, vol. 37, p. 100837, May 2021, doi: https://doi.org/10.1016/j.uclim.2021.100837.

L. Zhang, P. Liu, L. Zhao, G. Wang, W. Zhang, and J. Liu, “Air Quality Predictions with a Semi-Supervised Bidirectional LSTM Neural Network,” Atmospheric Pollution Research, vol. 12, no. 1, pp. 328–339, Jan. 2021, doi: https://doi.org/10.1016/j.apr.2020.09.003.

S. Vito, "Air Quality," UCI Machine Learning Repository, 2016. [Online]. Available: https://doi.org/10.24432/C59K5F.

J. Li, H. Izakian, W. Pedrycz, and I. Jamal, “Clustering-based Anomaly Detection in Multivariate Time Series Data,” Applied Soft Computing, vol. 100, p. 106919, Mar. 2021, doi: https://doi.org/10.1016/j.asoc.2020.106919.

D. Musfiroh, U. Khaira, P. E. P. Utomo, and T. Suratno, “Analisis Sentimen terhadap Perkuliahan Daring di Indonesia dari Twitter Dataset menggunakan InSet Lexicon,” MALCOM: Indonesian Journal of Machine Learning and Computer Science, vol. 1, no. 1, pp. 24–33, Mar. 2021, doi: https://doi.org/10.57152/malcom.v1i1.20.




DOI: https://doi.org/10.32520/stmsi.v14i3.4371

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