Hybrid artificial neural network algorithm for air pollution estimation

Vijayalaxmi S. Kumbhar; Shaminder Singh Sohi; V Jayaram; Pillai G Sreelekshmy; Surendra Kumar Shukla; K S Abhilash

doi:10.53730/ijhs.v6nS5.9080

Authors

Vijayalaxmi S. Kumbhar Assistant Professor, PCET's Pimpri Chinchwad College of Engineering and Research, Ravet
Shaminder Singh Sohi Assistant Professor, Chandigarh University, Gharuan, Mohali, Punjab
Jayaram V Research Scholar, Dept of Mechanical Engineering, Noorul Islam Center for Higher Education Tamilnadu
Sreelekshmy Pillai G Associate Professor In Civil Engineering, NSS College Of Engineering, Palakkad
Surendra Kumar Shukla Designation: Associate Professor, Department of Computer Science & Engineering, Graphic Era Deemed to be University, Dehradun, Uttarakhand, India, 248002
Abhilash. KS Managing Director, EduCorp Centre for Research and Advanced Studies Pvt. Ltd. Thiruvananthapuram, Kerala

Keywords:

Air Quality Estimation, Hybrid Model, Artificial Neural Network, Pollutants, Linear Vector Quantization

Abstract

In recent years, airborne broadcasting has grown more prevalent in cities. Air quality degradation is a severe air pollution issue that exists daily. To forecast the amount of pollutants, Artificial Neural Network (ANN) and Linear Vector Quantization (LVQ) techniques were utilized. The data set dimensions are defined by the pre-processing procedure and the feature extraction mechanism. The ANN model predicts categorization concentration, allowing the LVQ model to classify direct situations with greater accuracy using explanatory factors. The ANN+LVQ model outperformed other technologies in terms of classification accuracy. The raw data was cleaned to improve the accuracy of the prediction algorithms. The pollutants discovered in the collection are NO₂, NO_x, O₃, Benzene, Xylene, NH₃, CO, SO₂, PM₁₀, NO, and Toluene. The performance of the recommendation and forecast models were tested in this study using two datasets in two distinct experiments. In urban, rural, and industrial settings, the proposed ANN model is successful in detecting air quality and predicting pollution levels. The ANN-LVQ model obtained 90% percent sensitivity, 97.59% accuracy, and 99.46% specificity with 2.43% error rate. The suggested model's accuracy is much greater than that of other current research models.

Downloads

Download data is not yet available.

References

F. Bre, J. M. Gimenez, and V. D. Fachinotti, “Prediction of wind pressure coefficients on building surfaces using artificial neural networks,” Energy Build., 2018, doi: 10.1016/j.enbuild.2017.11.045.

N. H. A. Rahman, M. H. Lee, M. T. Latif, and Suhartono, “Forecasting of air pollution index with artificial neural network,” J. Teknol. (Sciences Eng., 2013, doi: 10.11113/jt.v63.1913.

A. Azid, H. Juahir, M. T. Latif, S. M. Zain, and M. R. Osman, “Feed-Forward Artificial Neural Network Model for Air Pollutant Index Prediction in the Southern Region of Peninsular Malaysia,” J. Environ. Prot. (Irvine,. Calif)., vol. 04, no. 12, pp. 1–10, 2013, doi: 10.4236/jep.2013.412a1001.

“Vapnik, V. (2013). "Introduction to Artificial Neural Network Theory", The nature of statistical learning

A. Alimissis, K. Philippopoulos, C. G. Tzanis, and D. Deligiorgi, “Spatial estimation of urban air pollution with the use of artificial neural network models,” Atmos. Environ., vol. 191, pp. 205–213, 2018, doi: 10.1016/j.atmosenv.2018.07.058.

P. A. Rahman, A. A. Panchenko, and A. M. Safarov, “Using neural networks for prediction of air pollution index in industrial city,” 2017, doi: 10.1088/1755- 1315/87/4/042016.

A. Challoner, F. Pilla, L. Gill, G. Adamkiewicz, and M. P. Fabian, “Prediction of Indoor Air Exposure from Outdoor Air Quality Using an Artificial Neural Network Model for Inner City Commercial Buildings,” mdpi.com, 2015, doi: 10.3390/ijerph121214975.

Chauhan, R., Kaur, H., & Alankar, B. (2021). Air quality forecast using convolutional neural network for sustainable development in urban environments. Sustainable Cities and Society, 75, 103239.

Wang, J., Li, J., Wang, X., Wang, J., & Huang, M. (2021). Air quality prediction using CT-LSTM. Neural Computing and Applications, 33(10), 4779-4792.

Wardana, I. N. K., Gardner, J. W., & Fahmy, S. A. (2021). Optimising deep learning at the edge for accurate hourly air quality prediction. Sensors, 21(4), 1064.

Zhang, Y., Zhang, R., Ma, Q., Wang, Y., Wang, Q., Huang, Z., & Huang, L. (2020). A feature selection and multi-model fusion-based approach of predicting air quality. ISA transactions, 100, 210-220.

Sharma, A., Mitra, A., Sharma, S., & Roy, S. (2018, October). Estimation of air quality index from seasonal trends using deep neural network. In International Conference on Artificial Neural Networks (pp. 511-521). Springer, Cham.

Zhou, Y., Chang, F. J., Chang, L. C., Kao, I. F., & Wang, Y. S. (2019). Explore a deep learning multi-output neural network for regional multi-step-ahead air quality forecasts. Journal of cleaner production, 209, 134-145.

Nawahda, A., & Zhong, J. Classification with Quantification for Air Quality Monitoring.

Bai, L., Wang, J., Ma, X., & Lu, H. (2018). Air pollution forecasts: An overview. International journal of environmental research and public health, 15(4), 780.