The increasing effects of noise pollution have necessitated the prediction of noise levels. In this regard, it has become very prudent to find models which are practically applicable and have the capability to predict noise levels with accuracy. In this project, two dimensionality reduction techniques namely the Principal Component Analysis (PCA) and Partial Least Squares (PLS) were used in truncating the dimensions of observed noise levels data collected in the Tarkwa Mining Community (TMC) for which the data with reduced dimensions served as input data for a Back Propagation Neural Network noise prediction model. The accuracies of the techniques were determined using statistical indicators. The Partial Least Squares technique had a better accuracy with RMSE of 1.135 when hybridized with the Back Propagation Neural Network. The performance of the Principal Component Analysis was also with RMSE of 1.373 and that of the observed noise data produced an RMSE of 1.433. Graphical representations also showed the precision of individual predicted noise levels compared to the observed noise levels. The importance of the techniques used in predicting noise levels cannot be overemphasized based on the results obtained.
Published in | American Journal of Neural Networks and Applications (Volume 10, Issue 1) |
DOI | 10.11648/j.ajnna.20241001.12 |
Page(s) | 15-26 |
Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
Copyright |
Copyright © The Author(s), 2024. Published by Science Publishing Group |
Noise Level Prediction, Noise Mapping, Dimensionality Reduction Techniques, Back Propagation Neural Network
Observed | Observed (Centred) | Predicted (PCA) | Error (PCA) | Predicted (PLS) | Error (PLS) |
---|---|---|---|---|---|
65 | -20.2245 | -17.2029 | -3.0216 | -20.2245 | 0.0000 |
78 | -7.2245 | -6.7765 | -0.448 | -7.2241 | -0.0004 |
84 | -1.2245 | -0.6589 | -0.5656 | -0.2013 | -1.0232 |
84 | -1.2245 | 0.1041 | -1.3286 | -1.2249 | 0.0004 |
75 | -10.2245 | -10.141 | -0.0835 | -10.2246 | 0.0001 |
86 | 0.7755 | 2.1905 | -1.415 | 0.7754 | 0.0001 |
79 | -6.2245 | -6.7765 | 0.552 | -7.2241 | 0.9996 |
88 | 2.7755 | 2.7992 | -0.0237 | 2.7758 | -0.0003 |
85 | -0.2245 | 0.0805 | -0.305 | 0.2759 | -0.5004 |
86 | 0.7755 | 0.0805 | 0.695 | 0.2759 | 0.4996 |
89 | 3.7755 | 4.6571 | -0.8816 | 1.5378 | 2.2377 |
90 | 4.7755 | 4.6571 | 0.1184 | 1.5378 | 3.2377 |
91 | 5.7755 | 5.8973 | -0.1218 | 5.7508 | 0.0247 |
98 | 12.7755 | 12.2568 | 0.5187 | 12.6617 | 0.1138 |
96 | 10.7755 | 8.2406 | 2.5349 | 12.7685 | -1.9930 |
94 | 8.7755 | 10.7517 | -1.9762 | 8.5991 | 0.1764 |
83 | -2.2245 | -1.6951 | -0.5294 | -1.2266 | -0.9979 |
81 | -4.2245 | -2.449 | -1.7755 | -4.2289 | 0.0044 |
84 | -1.2245 | -1.6951 | 0.4706 | -1.2266 | 0.0021 |
85 | -0.2245 | -0.0421 | -0.1824 | -0.2238 | -0.0007 |
75 | -10.2245 | -10.2298 | 0.0053 | -10.2239 | -0.0006 |
76 | -9.2245 | -10.2298 | 1.0053 | -10.2239 | 0.9994 |
74 | -11.2245 | -10.2298 | -0.9947 | -10.2239 | -1.0006 |
77 | -8.2245 | -6.8932 | -1.3313 | -7.2241 | -1.0004 |
79 | -6.2245 | -6.8932 | 0.6687 | -7.2241 | 0.9996 |
74 | -11.2245 | -12.8798 | 1.6553 | -11.7244 | 0.4999 |
73 | -12.2245 | -12.8798 | 0.6553 | -11.7244 | -0.5001 |
86 | 0.7755 | 0.7813 | -0.0058 | 0.7772 | -0.0017 |
88 | 2.7755 | 4.3186 | -1.5431 | 2.7765 | -0.0010 |
84 | -1.2245 | -3.1993 | 1.9748 | -1.226 | 0.0015 |
89 | 3.7755 | 5.5993 | -1.8238 | 6.2817 | -2.5062 |
87 | 1.7755 | 2.0958 | -0.3203 | 1.8026 | -0.0271 |
89 | 3.7755 | 5.1805 | -1.405 | 4.7766 | -1.0011 |
90 | 4.7755 | 5.1805 | -0.405 | 4.7766 | -0.0011 |
95 | 9.7755 | 11.0214 | -1.2459 | 11.6161 | -1.8406 |
98 | 12.7755 | 10.1008 | 2.6747 | 10.8753 | 1.9002 |
97 | 11.7755 | 11.0214 | 0.7541 | 11.6161 | 0.1594 |
87 | 1.7755 | 0.8545 | 0.921 | 1.7757 | -0.0002 |
86 | 0.7755 | 0.8545 | -0.079 | 1.7757 | -1.0002 |
89 | 3.7755 | 6.6165 | -2.841 | 3.7719 | 0.0036 |
93 | 7.7755 | 7.9642 | -0.1887 | 8.7675 | -0.9920 |
95 | 9.7755 | 7.9642 | 1.8113 | 8.7675 | 1.0080 |
94 | 8.7755 | 5.5993 | 3.1762 | 6.2817 | 2.4938 |
96 | 10.7755 | 10.1008 | 0.6747 | 10.8753 | -0.0998 |
92 | 6.7755 | 5.1805 | 1.595 | 4.7766 | 1.9989 |
88 | 2.7755 | 1.8495 | 0.926 | 2.7764 | -0.0009 |
80 | -5.2245 | -6.8932 | 1.6687 | -7.2241 | 1.9996 |
76 | -9.2245 | -6.8932 | -2.3313 | -7.2241 | -2.0004 |
68 | -17.2245 | -17.211 | -0.0135 | -17.2224 | -0.0021 |
Model | Standard Deviation | Mean | RMSE | R^{2} |
---|---|---|---|---|
PCA-BPNN | 1.386138 | -0.04348 | 1.135 | 0.984 |
PLS-BPNN | 1.144861 | 0.058541 | 1.373 | 0.987 |
Components | R²X | R²X (Cumul.) | Eigenvals. | Q² | Limit | Q² (Cumul.) |
---|---|---|---|---|---|---|
1 | 0.45837 | 0.45837 | 2.29184 | 0.2201 | 0.21667 | 0.2201 |
2 | 0.26572 | 0.72409 | 1.32859 | 0.20066 | 0.26596 | 0.3766 |
Component | Eigenvalue | Percentage of Variance (%) | Cumulative Variance (%) |
---|---|---|---|
1 | 2.29194 | 45.84 | 45.84 |
2 | 1.32852 | 26.57 | 72.41 |
3 | 0.69895 | 13.98 | 86.39 |
4 | 0.58166 | 11.63 | 98.02 |
5 | 0.09893 | 1.98 | 100.00 |
Predictors | Coefficients of PC1 | Coefficients of PC2 |
---|---|---|
POP | -0.45426 | 0.35983 |
Traffic | 0.51782 | 0.26625 |
Road net. | -0.0031 | 0.74152 |
Land use | 0.4031 | 0.43554 |
Dist. | 0.60251 | -0.24511 |
Predictors | PC1 | PC2 |
---|---|---|
POP | 0.693273 | 0.404026 |
Traffic | -0.779713 | 0.309337 |
Road net. | 0.015944 | 0.859016 |
Land use | -0.603578 | 0.505647 |
Dist. | -0.915805 | -0.275817 |
Number of Factors | Variance Explained for X Effects (%) | Cumulative x variance (%) | Variance Explained for Y Responses (%) | Cumulative y variance (%) |
---|---|---|---|---|
1 | 42.8286 | 42.8286 | 56.39184 | 56.39184 |
2 | 27.67881 | 70.50741 | 9.98528 | 66.37712 |
3 | 5.19388 | 75.7013 | 26.75447 | 93.13159 |
4 | 11.49075 | 87.19205 | 2.89362 | 96.02521 |
5 | 12.80795 | 100 | 0.02797 | 96.05318 |
Predictors | Factor 1 | Factor 2 |
---|---|---|
POP | -5.43909 | 0.51595 |
Traffic | 4.35257 | 3.1581 |
Road net. | -2.33731 | 5.24753 |
Land use | 2.40894 | 5.27832 |
Dist. | 6.55699 | 0.89002 |
PCA | Principal Component Analysis |
PLS | Partial Least Square |
BPNN | Back Propagation Neural Network |
TMC | Tarkwa Mining Community |
[1] | Ali, I., Wasif, K. and Bavomi, H. (2024), “Dimensionality Reduction for Images loT using Machine Learning”, Earth Environment and Planetary Sciences, Vol. 40, pp. 47-61. |
[2] | Anowar, F., Samira, S., and Selim, B. (2023), “Conceptual and Empirical Comparison of Dimensionality Reduction Algorithm’. |
[3] | Baffoe, P. E., and Duker, A. A. (2019), “Evaluation of Two Noise Level Prediction Models: Multiple Linear Regression and a Hybrid Approach”, American Journal of Mathematical and Computer Modelling. Vol. 4, No. 3, pp. 91-99. |
[4] | Bauer, E. R., Kohler, J. L. (2000), “Cross sectional Survey of Noise Exposure in the Mining Industry”, National Institute for Occupational Safety and Health Office of Mine Safety and Health Research, Publication No. 76, pp. 2-15. |
[5] | Braj, B., and Jain, V. K. (1995), “A Comparative Study of Noise Levels in Some Residential, Industrial and Commercial Areas of Delhi”, Environmental Monitoring and Assessment, Vol. 35, pp. 1-11. |
[6] | Chepesiuk, R. (2005), “Decibel Hell: The Effects of Living in a Noisy World”, Environmental Health Perspectives, Vol. 113, No. 1, pp. 35 - 37. |
[7] | Elezaj, R., (2019), “Noise Pollution Effects: What Do You Think It Does to Humans?”, Health Europa, 7^{th} Ed., Grosag Publication, Vancouver, 2pp. |
[8] | Genaro N., Torija A., Ramos-Ridao A., Requena, I., Ruiz D. P., and Zamorano M. (2010), “A Neural Network-Based Model for Urban Noise Prediction”, The Journal of the Acoustical Society of America, Vol. 128, pp. 1738-1746. |
[9] | Hamad, K., Khalilb, M., and Shanableha, A. (2017), “Modeling Roadway Traffic Noise in A Hot Climate Using Artificial Neural Networks” Transportation Research Part D: Transport and Environment, Vol. 53, pp. 161-177. |
[10] | Kumar, K., Parida, M., and Katiyar, V. K. (2012), “Artificial Neural Network Modeling for Road Traffic Noise Prediction”, Third International Conference on Computing, Communication and Networking Technologies, pp. 3-5. |
[11] | Nedic, V., Despotovic, B., and Cvetanovic S. (2014), “Comparison of Classical Statistical Methods and Artificial Neural Network in Traffic Noise Prediction”, Environmental Impact Assessment Review, Vol. 49, pp. 24-30. |
[12] | Rafieian, B., Hermosilla, P. and Vazquez, P. P. (2023), “Improving Dimensionality Reduction Projections for Data Visualization”, Appl. Sci., Vol. 13 (17), 9967: |
[13] | Shillington, K (2012). “History of Africa”, London: Palgrave Macmillan. pp. 93–94. |
[14] | Smith, A. (2003), “Effects of Noise, Job Characteristics and Stress on Mental Health and Accidents, Injuries and Cognitive Failures at Work”, International Commission on Biological Effects of Noise, pp. 1-6. |
[15] | Sukeerth G., Munilakshmi N. and Anilkumarreddy C. (2017), “Mathematical Modeling for the Prediction of Road Traffic Noise Levels in Tirupati Town”, International Journal of Engineering Development and Research, Vol. 5, pp. 2091-2097. |
[16] | Wright, J. B., Hastings, D. A., Jones, W. B., Williams, H. R. (1985). “Geology and Mineral Resources of West Africa”, London: George Allen & UNWIN, pp. 45–47. |
APA Style
Baffoe, P. E., Ziggah, Y. Y. (2024). Accuracy Assessment of Dimensionality Reduction Techniques in Novel Approach of Precise Noise Levels Prediction and Mapping. American Journal of Neural Networks and Applications, 10(1), 15-26. https://doi.org/10.11648/j.ajnna.20241001.12
ACS Style
Baffoe, P. E.; Ziggah, Y. Y. Accuracy Assessment of Dimensionality Reduction Techniques in Novel Approach of Precise Noise Levels Prediction and Mapping. Am. J. Neural Netw. Appl. 2024, 10(1), 15-26. doi: 10.11648/j.ajnna.20241001.12
AMA Style
Baffoe PE, Ziggah YY. Accuracy Assessment of Dimensionality Reduction Techniques in Novel Approach of Precise Noise Levels Prediction and Mapping. Am J Neural Netw Appl. 2024;10(1):15-26. doi: 10.11648/j.ajnna.20241001.12
@article{10.11648/j.ajnna.20241001.12, author = {Peter Ekow Baffoe and Yao Yevenyo Ziggah}, title = {Accuracy Assessment of Dimensionality Reduction Techniques in Novel Approach of Precise Noise Levels Prediction and Mapping }, journal = {American Journal of Neural Networks and Applications}, volume = {10}, number = {1}, pages = {15-26}, doi = {10.11648/j.ajnna.20241001.12}, url = {https://doi.org/10.11648/j.ajnna.20241001.12}, eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajnna.20241001.12}, abstract = {The increasing effects of noise pollution have necessitated the prediction of noise levels. In this regard, it has become very prudent to find models which are practically applicable and have the capability to predict noise levels with accuracy. In this project, two dimensionality reduction techniques namely the Principal Component Analysis (PCA) and Partial Least Squares (PLS) were used in truncating the dimensions of observed noise levels data collected in the Tarkwa Mining Community (TMC) for which the data with reduced dimensions served as input data for a Back Propagation Neural Network noise prediction model. The accuracies of the techniques were determined using statistical indicators. The Partial Least Squares technique had a better accuracy with RMSE of 1.135 when hybridized with the Back Propagation Neural Network. The performance of the Principal Component Analysis was also with RMSE of 1.373 and that of the observed noise data produced an RMSE of 1.433. Graphical representations also showed the precision of individual predicted noise levels compared to the observed noise levels. The importance of the techniques used in predicting noise levels cannot be overemphasized based on the results obtained. }, year = {2024} }
TY - JOUR T1 - Accuracy Assessment of Dimensionality Reduction Techniques in Novel Approach of Precise Noise Levels Prediction and Mapping AU - Peter Ekow Baffoe AU - Yao Yevenyo Ziggah Y1 - 2024/05/24 PY - 2024 N1 - https://doi.org/10.11648/j.ajnna.20241001.12 DO - 10.11648/j.ajnna.20241001.12 T2 - American Journal of Neural Networks and Applications JF - American Journal of Neural Networks and Applications JO - American Journal of Neural Networks and Applications SP - 15 EP - 26 PB - Science Publishing Group SN - 2469-7419 UR - https://doi.org/10.11648/j.ajnna.20241001.12 AB - The increasing effects of noise pollution have necessitated the prediction of noise levels. In this regard, it has become very prudent to find models which are practically applicable and have the capability to predict noise levels with accuracy. In this project, two dimensionality reduction techniques namely the Principal Component Analysis (PCA) and Partial Least Squares (PLS) were used in truncating the dimensions of observed noise levels data collected in the Tarkwa Mining Community (TMC) for which the data with reduced dimensions served as input data for a Back Propagation Neural Network noise prediction model. The accuracies of the techniques were determined using statistical indicators. The Partial Least Squares technique had a better accuracy with RMSE of 1.135 when hybridized with the Back Propagation Neural Network. The performance of the Principal Component Analysis was also with RMSE of 1.373 and that of the observed noise data produced an RMSE of 1.433. Graphical representations also showed the precision of individual predicted noise levels compared to the observed noise levels. The importance of the techniques used in predicting noise levels cannot be overemphasized based on the results obtained. VL - 10 IS - 1 ER -