Random Forest Algorithm to Measure the Air Pollution Standard Index

Ariyono Setiawan; Untung Lestari Wibowo; Ahmad Mubarok; Khoirunnisa Larasati; Jehad A.H Hammad

doi:10.17977/um018v7i12024p86-100

Random Forest Algorithm to Measure the Air Pollution Standard Index

Ariyono Setiawan, Untung Lestari Wibowo, Ahmad Mubarok, Khoirunnisa Larasati, Jehad A.H Hammad

Abstract

This study uses the Random Forest algorithm to measure and predict the Air Pollution Standard Index (APSI) at Blimbing Banyuwangi Airport. Air pollution data, including concentrations of O3, CO, NO2, SO2, PM2.5, and PM10, were collected from air monitoring stations at the airport from April 15-30, 2024. APSI measurement followed established formulas by relevant authorities. Data analysis utilized statistical approaches and computational algorithms. The findings reveal that air quality at the airport is generally "Moderate," with occasional "Good" days. The Random Forest algorithm effectively predicts APSI based on existing pollution data. These results provide insights for improving air pollution management at the airport and surrounding areas, emphasizing the need for continuous air quality monitoring. Days classified as "Moderate" suggest health risks for sensitive groups, indicating the need for targeted mitigation strategies. Recommendations include increasing green spaces, optimizing flight schedules to reduce peak pollution, and raising public awareness about air quality. The effectiveness of the Random Forest algorithm suggests its potential application in other airports for proactive air quality management. Future research could integrate real-time data and advanced machine learning models for more accurate and timelier APSI predictions.

Full Text:

PDF

References

K. M. Bendtsen, E. Bengtsen, A. T. Saber, and U. Vogel, “Correction to: A review of health effects associated with exposure to jet engine emissions in and around airports,” Environ. Heal., vol. 20, no. 1, p. 20, Dec. 2021.

I. Manisalidis, E. Stavropoulou, A. Stavropoulos, and E. Bezirtzoglou, “Environmental and Health Impacts of Air Pollution: A Review,” Front. Public Heal., vol. 8, Feb. 2020.

M. Maharani, S. Viphindrartin, and D. Yunitasari, “The Analysis of Improving Social Economic Quality of the Community Through the Role of Airports in Banyuwangi District,” Repository.Unej.Ac.Id, vol. 5, no. 9, 2020.

N. Ma, D. Aviv, H. Guo, and W. W. Braham, “Measuring the right factors: A review of variables and models for thermal comfort and indoor air quality,” Renew. Sustain. Energy Rev., vol. 135, p. 110436, Jan. 2021.

M. Santoso et al., “Assessment of Urban Air Quality in Indonesia,” Aerosol Air Qual. Res., vol. 20, pp. 2142–2158, 2020.

Y. Nazarenko, D. Pal, and P. A. Ariya, “Air quality standards for the concentration of particulate matter 2.5, global descriptive analysis,” Bull. World Health Organ., vol. 99, no. 2, pp. 125–137, 2021.

O. A. Towoju and F. A. Ishola, “A case for the internal combustion engine powered vehicle,” Energy Reports, vol. 6, pp. 315–321, Feb. 2020.

G. Mahalisa and N. Nurarminarahmah, “Air Pollution Standard Index (APSI) Detection Application Based on the Flask Model,” Brill. Res. Artif. Intell., vol. 3, no. 2, pp. 270–274, 2023.

A. B. Adetunji, O. N. Akande, F. A. Ajala, O. Oyewo, Y. F. Akande, and G. Oluwadara, “House Price Prediction using Random Forest Machine Learning Technique,” Procedia Comput. Sci., vol. 199, pp. 806–813, 2022.

N. R. Sasmita, S. Ramadeska, Z. M. Kesuma, and T. R. Noviandy, “Decision Tree versus k-NN : A Performance Comparison for Air Quality Classification in Indonesia,” Infolitika J. Data Sci., vol. 2, no. 1, pp. 9–16, 2024.

X. Zhou, P. Lu, Z. Zheng, D. Tolliver, and A. Keramati, “Accident Prediction Accuracy Assessment for Highway-Rail Grade Crossings Using Random Forest Algorithm Compared with Decision Tree,” Reliab. Eng. Syst. Saf., vol. 200, p. 106931, Aug. 2020.

F. Insani and A. P. Sari, “Optimzation of Interval Fuzzy Time Series With Particle Swarm Optimization for Prediction Air Quality on Pekanbaru,” Indones. J. Artif. Intell. Data Min., vol. 3, no. 1, p. 36, 2020.

P. Alusvigayana, A. S. Yuwono, M. Yani, and S. Syarwan, “Evaluation of the Air Pollutant Standard Index (ISPU) parameter concentration limits in industrial estates on Java Island,” J. Pengelolaan Sumberd. Alam dan Lingkung., vol. 13, no. 4, pp. 537–548, 2023.

T. Toharudin et al., “Boosting Algorithm to Handle Unbalanced Classification of PM2.5Concentration Levels by Observing Meteorological Parameters in Jakarta-Indonesia Using AdaBoost, XGBoost, CatBoost, and LightGBM,” IEEE Access, vol. 11, no. April, pp. 35680–35696, 2023.

World Health Organization, WHO guidelines for indoor air quality: household fuel combustion. World Health Organization, 2014.

Z. Hweju, F. Kopi, and K. Abou-El-Hossein, “Parameter importance analysis: Random forest approach,” J. Phys. Conf. Ser., vol. 2256, no. 1, 2022.

Y. Park and J. C. Ho, “PaloBoost: An Overfitting-robust TreeBoost with Out-of-Bag Sample Regularization Techniques,” vol. 1, no. 1, 2018.

B. Ramosaj and M. Pauly, “Consistent estimation of residual variance with random forest Out-Of-Bag errors,” Stat. Probab. Lett., vol. 151, pp. 49–57, Aug. 2019.

S. Janitza and R. Hornung, On the overestimation of random forest’s out-of-bag error, vol. 13, no. 8. 2018.

Z. Khan, N. Gul, N. Faiz, A. Gul, W. Adler, and B. Lausen, “Optimal Trees Selection for Classification via Out-of-Bag Assessment and Sub-Bagging,” IEEE Access, vol. 9, pp. 28591–28607, 2021.

S. H. Tajmir et al., “Artificial intelligence-assisted interpretation of bone age radiographs improves accuracy and decreases variability,” Skeletal Radiol., vol. 48, no. 2, pp. 275–283, 2019.

I. Altaf, M. A. Butt, and M. Zaman, “Hard Voting Meta Classifier for Disease Diagnosis Using Mean Decrease in Impurity for Tree Models,” Rev. Comput. Eng. Res., vol. 9, no. 2, pp. 71–82, 2022.

A. Akhmad, L. Lukas, and B. Mahawan, “Improving Performance Loan Fraud Model Prediction Using Mean Decrease Accuracy and Mean Decrease Gini,” ACMIT Proc., vol. 6, no. 1, pp. 36–41, 2021.

S. Leschka et al., “Effect of decrease in heart rate variability on the diagnostic accuracy of 64-MDCT coronary angiography,” Am. J. Roentgenol., vol. 190, no. 6, pp. 1583–1590, 2008.

C. Benard, S. Da Veiga, and E. Scornet, “Mean decrease accuracy for random forests: inconsistency, and a practical solution via the Sobol-MDA,” Biometrika, vol. 109, no. 4, pp. 881–900, 2022.

C. Röver, D. Rindskopf, and T. Friede, “How trace plots help interpret meta-analysis results,” Res. Synth. Methods, vol. 15, no. 3, pp. 413–429, 2024.

J. A. Bortoloti, R. E. Bruns, J. C. De Andrade, and R. K. Vieira, “Split-plot design optimization for trace determination of lead by anodic stripping voltammetry in a homogeneous ternary solvent system,” Chemom. Intell. Lab. Syst., vol. 70, no. 2, pp. 113–121, 2004.

M. Friendly, “The generalized ridge trace plot: Visualizing bias and precision,” J. Comput. Graph. Stat., vol. 22, no. 1, pp. 50–68, 2013.

G. Xu et al., “Advances in emission control of diesel vehicles in China,” J. Environ. Sci., vol. 123, pp. 15–29, Jan. 2023.

S. Agrawal, M. Guevara, and S. P. Verma, “Tectonic discrimination of basic and ultrabasic volcanic rocks through log-transformed ratios of immobile trace elements,” Int. Geol. Rev., vol. 50, no. 12, pp. 1057–1079, 2008.

B. Fournier, N. Rupin, M. Bigerelle, D. Najjar, A. Iost, and R. Wilcox, “Estimating the parameters of a generalized lambda distribution,” Comput. Stat. Data Anal., vol. 51, no. 6, pp. 2813–2835, 2007.

B. Fournier, N. Rupin, M. Bigerelle, D. Najjar, and A. Iost, “Application of the generalized lambda distributions in a statistical process control methodology,” J. Process Control, vol. 16, no. 10, pp. 1087–1098, Dec. 2006.

S. Verhagen, B. Li, and P. J. G. Teunissen, “Ps-LAMBDA: Ambiguity success rate evaluation software for interferometric applications,” Comput. Geosci., vol. 54, pp. 361–376, 2013.

H. Hasna, N. Amalita, D. Permana, and A. Salma, “Random Forest Implementation for Air Pollution Standard Index Classification in DKI Jakarta 2022,” vol. 2, pp. 226–233, 2024.

R. Yu, Y. Yang, L. Yang, G. Han, and O. A. Move, “RAQ–A random forest approach for predicting air quality in urban sensing systems,” Sensors (Switzerland), vol. 16, no. 1, 2016.

T. Madan, S. Sagar, and D. Virmani, “Air Quality Prediction using Machine Learning Algorithms-A Review,” Proc. - IEEE 2020 2nd Int. Conf. Adv. Comput. Commun. Control Networking, ICACCCN 2020, no. December 2020, pp. 140–145, 2020.

B. Han, L. Wang, Z. Deng, Y. Shi, and J. Yu, “Source emission and attribution of a large airport in Central China,” Sci. Total Environ., vol. 829, p. 154519, Jul. 2022.

L. Li et al., “Air quality changes during the COVID-19 lockdown over the Yangtze River Delta Region: An insight into the impact of human activity pattern changes on air pollution variation,” Sci. Total Environ., vol. 732, p. 139282, Aug. 2020.

R. Horsley et al., “Lattice determination of Sigma-Lambda mixing,” Phys. Rev. D - Part. Fields, Gravit. Cosmol., vol. 91, no. 7, pp. 1–40, 2015.

D. Mazinanian, A. Ketkar, N. Tsantalis, and D. Dig, “Understanding the use of lambda expressions in Java,” Proc. ACM Program. Lang., vol. 1, no. OOPSLA, 2017.

DOI: http://dx.doi.org/10.17977/um018v7i12024p86-100