LRX: A Hybrid-based Real-Time Air Quality Index Prediction and Visualization Model
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Accurately predicting the air quality index significantly reduces health risks and supports urban environmental planning. This paper presents LRX, a hybrid predictive model, for Air Quality Index (AQI) prediction. The model employs Long short-term memory to capture temporal dependencies, Random Forest to fine-tune the features, and Extreme Gradient Boosting to enhance the final predictions. The objective of the study is to build a model that can accurately predict air quality index numbers in real time for many cities in India. The proposed model LRX design influences the depth of each algorithm to enhance accuracy and generalization. The experimental results show the model's ability to predict the AQI forecast of various cities in India with a root mean square error of 0.014 and R2 of 0.948, performing better compared to the models individually. To enhance this, a Stream lit-based user interface has been developed to enable real-time AQI predictions and visualization. The interface incorporates tabs for interactive inputs, model selection, graphical representation of predicted trends, ensuring accessibility and usability, and enhancing the practical applicability of the proposed model. This easy-to-navigate tool not only makes the prediction process more accessible but also helps bridge the gap between complex model results and practical environmental decision-making, enhancing the overall impact of the research. This research contributes to air quality prediction by presenting a robust modelling approach that can be applied in the real world.
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[1] Nguyen, A. T., Pham, D. H., Oo, B. L., Ahn, Y., & Lim, B. T. H. (2024). Predicting air quality index using attention hybrid deep learning and quantum-inspired particle swarm optimization. Journal of Big Data, 11(1), 71. doi:10.1186/s40537-024-00926-5.
[2] Palaniappan, S., Logeswaran, R., Khanam, S., & Zhang, Y. (2025). Machine Learning Model for Predicting Net Environmental Effects. Journal of Informatics and Web Engineering, 4(1), 243–253. doi:10.33093/jiwe.2025.4.1.18.
[3] Zayed, R., & Abbod, M. (2024). Air Quality Index Prediction Using DNN-Markov Modeling. Applied Artificial Intelligence, 38(1). doi:10.1080/08839514.2024.2371540.
[4] Palaniappan, S., Logeswaran, R., Velayutham, A., & Bui, N. D. (2025). Predicting Short-Range Weather in Tropical Regions Using Random Forest Classifier. Journal of Informatics and Web Engineering, 4(1), 18–28. doi:10.33093/jiwe.2025.4.1.2.
[5] Subramanian, A., Palanichamy, N., Ng, K.-W., & Aneja, S. (2025). Climate Change Analysis in Malaysia Using Machine Learning. Journal of Informatics and Web Engineering, 4(1), 307–319. doi:10.33093/jiwe.2025.4.1.22.
[6] Sarkar, N., Gupta, R., Keserwani, P. K., & Govil, M. C. (2022). Air Quality Index prediction using an effective hybrid deep learning model. Environmental Pollution, 315, 120404. doi:10.1016/j.envpol.2022.120404.
[7] Alzu’bi, F., Al-Rawabdeh, A., & Almagbile, A. (2024). Predicting air quality using random forest: A case study in Amman-Zarqa. Egyptian Journal of Remote Sensing and Space Science, 27(3), 604–613. doi:10.1016/j.ejrs.2024.07.004.
[8] Mishra, A., & Gupta, Y. (2024). Comparative analysis of Air Quality Index prediction using deep learning algorithms. Spatial Information Research, 32(1), 63–72. doi:10.1007/s41324-023-00541-1.
[9] Gaikar, D., Patel, U., Vispute, O., Singh, S., Sanghvi, T., & Professor, A. (2023). Prediction of Air Quality Index using Random Forest Algorithm. International Research Journal of Engineering and Technology, 10(4), 1248–1252.
[10] Janarthanan, R., Partheeban, P., Somasundaram, K., & Navin Elamparithi, P. (2021). A deep learning approach for prediction of air quality index in a metropolitan city. Sustainable Cities and Society, 67, 102720. doi:10.1016/j.scs.2021.102720.
[11] Jiao, Y., Wang, Z., & Zhang, Y. (2019). Prediction of air quality index based on LSTM. Proceedings of 2019 IEEE 8th Joint International Information Technology and Artificial Intelligence Conference, ITAIC 2019, 17–20. doi:10.1109/ITAIC.2019.8785602.
[12] Liu, H., Li, Q., Yu, D., & Gu, Y. (2019). Air quality index and air pollutant concentration prediction based on machine learning algorithms. Applied Sciences (Switzerland), 9(19), 4069. doi:10.3390/app9194069.
[13] Wang, J., Li, X., Jin, L., Li, J., Sun, Q., & Wang, H. (2022). An air quality index prediction model based on CNN-ILSTM. Scientific Reports, 12(1), 8373. doi:10.1038/s41598-022-12355-6.
[14] Natarajan, S. K., Shanmurthy, P., Arockiam, D., Balusamy, B., & Selvarajan, S. (2024). Optimized machine learning model for air quality index prediction in major cities in India. Scientific Reports, 14(1), 6795. doi:10.1038/s41598-024-54807-1.
[15] Duan, J., Gong, Y., Luo, J., & Zhao, Z. (2023). Air-quality prediction based on the ARIMA-CNN-LSTM combination model optimized by dung beetle optimizer. Scientific Reports, 13(1), 12127. doi:10.1038/s41598-023-36620-4.
[16] Gupta, N. S., Mohta, Y., Heda, K., Armaan, R., Valarmathi, B., & Arulkumaran, G. (2023). Prediction of Air Quality Index Using Machine Learning Techniques: A Comparative Analysis. Journal of Environmental and Public Health, 2023, 1–26. doi:10.1155/2023/4916267.
[17] Zhang, Z., Zhang, S., Chen, C., & Yuan, J. (2024). A systematic survey of air quality prediction based on deep learning. Alexandria Engineering Journal, 93, 128–141. doi:10.1016/j.aej.2024.03.031.
[18] Kumar Singh, R., Raghav, S., Maini, T., Kumar Singh, M., & Arquam, Md. (2022). Air Quality Prediction using Machine Learning. SSRN Electronic Journal. doi:10.2139/ssrn.4157651.
[19] Anggraini, T. S., Irie, H., Sakti, A. D., & Wikantika, K. (2024). Machine learning-based global air quality index development using remote sensing and ground-based stations. Environmental Advances, 15, 100456. doi:10.1016/j.envadv.2023.100456.
[20] Ravindiran, G., Hayder, G., Kanagarathinam, K., Alagumalai, A., & Sonne, C. (2023). Air quality prediction by machine learning models: A predictive study on the Indian coastal city of Visakhapatnam. Chemosphere, 338, 139518. doi:10.1016/j.chemosphere.2023.139518.
[21] Farooq, O., Shahid, M., Arshad, S., Altaf, A., Iqbal, F., Vera, Y. A. M., Flores, M. A. L., & Ashraf, I. (2024). An enhanced approach for predicting air pollution using quantum support vector machine. Scientific Reports, 14(1), 19521. doi:10.1038/s41598-024-69663-2.
[22] Liang, Y. C., Maimury, Y., Chen, A. H. L., & Juarez, J. R. C. (2020). Machine learning-based prediction of air quality. Applied Sciences (Switzerland), 10(24), 1–17. doi:10.3390/app10249151.
[23] Maltare, N. N., & Vahora, S. (2023). Air Quality Index prediction using machine learning for Ahmedabad city. Digital Chemical Engineering, 7, 100093. doi:10.1016/j.dche.2023.100093.
[24] Wang, S.-J., Huang, B.-J., & Hu, M.-H. (2023). A Deep Learning-based Air Quality Index Prediction Model Using LSTM and Reference Stations: A Real Application in Taiwan. 33rd International Telecommunication Networks and Applications Conference, 204–209. doi:10.1109/itnac59571.2023.10368496.
[25] Sachdeva, S., Kaur, R., Kimmi, Singh, H., Aggarwal, K., & Kharb, S. (2024). Meteorological AQI and pollutants concentration-based AQI predictor. International Journal of Environmental Science and Technology, 21(5), 4979–4996. doi:10.1007/s13762-023-05307-8.
[26] Chang, W., Chen, X., He, Z., & Zhou, S. (2023). A Prediction Hybrid Framework for Air Quality Integrated with W-BiLSTM(PSO)-GRU and XGBoost Methods. Sustainability (Switzerland), 15(22), 16064. doi:10.3390/su152216064.
[27] Zhongjie, Y., Shengwei, W., & Ze, W. (2022). Air quality prediction method based on the CS-LSTM. 5th International Conference on Data Science and Information Technology (DSIT), 1–5. doi:10.1109/dsit55514.2022.9943922.
[28] Li, C., Li, Y., & Bao, Y. (2021). Research on Air Quality Prediction Based on Machine Learning. 2nd International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI), 77–81. doi:10.1109/ichci54629.2021.00022.
[29] Gupta, V., Kapadia, S., & Bhadane, C. (2023). Time Series Analysis and Forecasting of Air Quality in India. 5th International Conference on Electrical, Computer and Communication Technologies, ICECCT 2023, 1–5. doi:10.1109/ICECCT56650.2023.10179673.
[30] Zhou, Z. (2023). Air Quality Prediction Based on Improved LSTM Model. 4th International Conference on Computer Engineering and Application (ICCEA), 392–395. doi:10.1109/iccea58433.2023.10135512.
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