Applicability of denoising-based artificial intelligence to forecast the environmental externalities

Abstract

The current study attempts to compare the hybrid artificial intelligence models to forecast the environmental externalities in Saudi Arabia. We have used the denoising based artificial intelligence models to construct hybrid models. While comparing the denoising techniques, the CSD-based denoising has outperformed. However, we have used the CSD-based hybrid models. CSD-ANN and CSD-RNN are used for denoising-based artificial intelligence models, whereas CSD-ARIMA is used for denoising-based traditional models. All these models are used to check and compare their performance in terms of level and direction of prediction for PM₁₀. The results show that the CSD-based ANN model has a higher predictability for PM₁₀ levels in Saudi Arabia due to low error values and higher D_stat values. In comparing original and forecasted data, the superiority of CSD-ANN is evident in predicting the PM₁₀ in Saudi Arabia. Hence, this hybrid model can predict the environmental externalities for non-linear and highly noised data. Moreover, the findings can be useful in achieving the sustainable development goal.

Keywords

Hybrid artificial intelligence / Forecasting / Saudi Arabia / Environment / PM₁₀

CRediT authorship contribution statement

Dongsheng Cai: Writing – review & editing, Methodology. Ghazala Aziz: Conceptualization, Resources, Validation, Visualization, Writing – original draft. Suleman Sarwar: Supervision. Majid Ibrahim Alsaggaf: Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Software. Avik Sinha: Supervision, Writing - review & editing.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

References

[1] N. Ahmed, T. Natarajan, K.R. Rao. Discrete cosine transform. IEEE Trans. Comput., 100 (1974), pp. 90-93

[2] A. Anwar, M. Siddique, E. Dogan, A. Sharif. The moderating role of renewable and non-renewable energy in environment-income nexus for ASEAN countries: Evidence from Method of Moments Quantile Regression. Renew. Energy, 164 (2021), pp. 956-967,

[3] H. Apaydin, H. Feizi, M.T. Sattari, M.S. Colak. Comparative analysis of recurrent neural network. Water (Switzerland), 12 (2020), pp. 1-18

[4] Bashir, M.F., MA, B., Hussain, H.I., Shahbaz, M., Koca, K., Shahzadi, I., 2022. Evaluating environmental commitments to COP21 and the role of economic complexity, renewable energy, financial development, urbanization, and energy innovation: Empirical evidence from the RCEP countries. Renew. Energy 184, 541–550. doi:

[5] M. Baxter, R.G. King. Approximate band-pass filters for economic time series. NBER Working Paper Series, 5022 (1995), pp. 1-53

[6] C. Blease, C. Locher, M. Leon-Carlyle, M. Doraiswamy. Artificial intelligence and the future of psychiatry: Qualitative findings from a global physician survey. Digital Health, 6 (2020),

[7] B.L. Bowerman, R.T. O’Connell, A.B. Koehler. Forecasting, Time Series, and Regression: an Applied Approach. South-Western Pub (2005)

[8] Box, G.E., Jenkins, G.M., 1976. Time series analysis, control, and forecasting. Holden Day 3226.

[9] A. Bozdağ, Y. Dokuz, Ö.B. Gökçek. Spatial prediction of PM10 concentration using machine learning algorithms in Ankara, Turkey. Environ. Pollut., 263 (2020), Article 114635,

[10] M. Bristone, R. Prasad, A.A. Abubakar. CPPCNDL: Crude oil price prediction using complex network and deep learning algorithms. Petroleum, 6 (4) (2020), pp. 353-361,

[11] M. Castelli, F.M. Clemente, A. Popovič, S. Silva, L. Vanneschi. A machine learning approach to predict air quality in California. Complexity, 2020 (2020), Article 8049504,

[12] S. Chae, J. Shin, S. Kwon, S. Lee, S. Kang, D. Lee. PM10 and PM2.5 real-time prediction models using an interpolated convolutional neural network. Sci. Rep., 11 (1) (2021), pp. 1-9,

[13] A. Cujia, D. Agudelo-Castañeda, C. Pacheco-Bustos, E.C. Teixeira. Forecast of PM10 time-series data: A study case in Caribbean cities. Atmos. Pollut. Res., 10 (6) (2019), pp. 2053-2062,

[14] Y. Ding. A novel decompose-ensemble methodology with AIC-ANN approach for crude oil forecasting. Energy, 154 (2018), pp. 328-336,

[15] D.L. Donoho. De-noising by modified soft-thresholding. IEEE Asia-Pacific Conference on Circuits and Systems - Proceedings, 41 (3) (2000), pp. 760-762,

[16] Y. Elder, G. Kutyniok. Compressed Sensing: Theory and Applications. Cambridge University Press, Cambridge (2012), pp. 1-64

[17] E.S. Gardner. Exponential smoothing: The state of the art. J. Forecast., 4 (1) (1985), pp. 1-28,

[18] Hamwi, N.I., 2018. Use Akaike (AIC) and Schwartz (SC) information criterions in the differentiation between nonlinear growth models of different fish species (Issue March). Hamah University. https://shamra-academia.com/en/show/5b7fa739aae12.

[19] B. Han, J. Xiong, L. Li, J. Yang, Z. Wang. Research on millimeter-wave image denoising method based on contourlet and compressed sensing. 2010 2nd International Conference on Signal Processing Systems (2010), p. 2

[20] Hodrick, R.J., Prescott, E.C., 1997. Postwar U. S. Business Cycles: An Empirical Investigation. J. Money Credit Bank. 29(1), 1–16.

[21] S. Hochreiter, J. Schmidhuber. Long short-term memory. Neural Computation, 9 (8) (1997), pp. 1735-1780,

[22] K. Hornik, M. Stinchcombe, H. White. Multilayer feedforward networks are universal approximators. Neural Netw., 2 (5) (1989), pp. 359-366

[23] Kalman, R.E., Buey, R., 1961. A new approach to linear filtering and prediction theory. Trans. ASME, J. Basic Eng. 83(Series D), 95–108.

[24] J. Kujawska, M. Kulisz, P. Oleszczuk, W. Cel. Machine learning methods to forecast the concentration of PM10 in Lublin, Poland. Energies, 15 (17) (2022), pp. 1-23,

[25] S. Li, J. Cifuentes-Faura, B. Talbi, M. Sadiq, K. Si Mohammed, M.F. Bashir. Dynamic correlated effects of electricity prices, biomass energy, and technological innovation in Tunisia’s energy transition. Util. Policy, 82 (2023), Article 101521,

[26] Li, J.H., Li, X., Wang, K., 2019. Atmospheric PM2.5 concentration prediction based on time series and interactive multiple model approach. Adv. Meteorol. 2019, 1279565. doi:

[27] J.M. Li, M. Jin, H. Li. Exploring spatial influence of remotely sensed PM2.5 concentration using a developed deep convolutional neural network model. Int. J. Environ. Res. Public Health, 16 (3) (2019), p. 454,

[28] K.P. Lin, P.F. Pai, S.L. Yang. Forecasting concentrations of air pollutants by logarithm support vector regression with immune algorithms. Appl. Math Comput., 217 (12) (2011), pp. 5318-5327,

[29] J. Liu, M. Murshed, F. Chen, M. Shahbaz, D. Kirikkaleli, Z. Khan. An empirical analysis of the household consumption-induced carbon emissions in China. Sustain. Prod. Consum., 26 (2021), pp. 943-957,

[30] B. Ma, S. Lin, M.F. Bashir, H. Sun, M. Zafar. Revisiting the role of firm-level carbon disclosure in sustainable development goals: Research agenda and policy implications. Gondwana Res., 117 (2023), pp. 230-242,

[31] S.G. Mallat. A theory for multiresolution signal decomposition: The wavelet representation. IEEE Trans. Pattern Anal. Mach. Intell., 11 (7) (1989), pp. 79-85

[32] N. Masseran. Modeling fluctuation of PM10 data with existence of volatility effect. Environ. Eng. Sci., 34 (11) (2017), pp. 816-827,

[33] N. Masseran, M.A.M. Safari. Risk assessment of extreme air pollution based on partial duration series: IDF approach. Stochk. Environ. Res. Risk A, 34 (3–4) (2020), pp. 545-559,

[34] D. Mishra, P. Goyal. Neuro-Fuzzy approach to forecasting Ozone Episodes over the urban area of Delhi, India. Environ. Technol. Innov., 5 (2016), pp. 83-94,

[35] J.M. Montero, G. Fernández-Avilés. Functional kriging prediction of atmospheric particulate matter concentrations in Madrid, Spain: Is the new monitoring system masking potential public health problems?. J. Clean. Prod., 175 (2018), pp. 283-293,

[36] Muhammad, B., 2022. Discovering the evolution of Pollution Haven Hypothesis: A literature review and future research agenda. Environ. Sci. Pollut. Res. 29, 48210–48232. doi: https://doi-org.ezproxy.ums.edu.my/10.1007/s11356-022-20782-1.

[37] V.R. Pamula, C. Van Hoof, M. Verhelst. Introduction to compressive sampling (CS). M. Ismail, M. Sawan (Eds.), Analog-and-Algorithm-Assisted Ultra-low Power BioSignal Acquisition Systems. Analog Circuits and Signal Processing, Springer, Cham (2019),

[38] S. Park, S. Woo, C. Lim. Predicting PM10 and PM2.5 concentration in container ports: A deep learning approach. Transp. Res. D, 115 (2023), Article 103601,

[39] Rijal, N., Gutta, R.T., Cao, T., Lin, J., Bo, Q., Zhang, J., 2018. Ensemble of deep neural networks for estimating particulate matter from images. In: 2018 3rd IEEE International Conference on Image, Vision and Computing, ICIVC 2018, x, 733–738. doi:

[40] A.G. Salman, Y. Heryadi, E. Abdurahman, W. Suparta. Single layer & multi-layer long short-term memory (LSTM) model with intermediate variables for weather forecasting. Procedia Comput. Sci., 135 (2018), pp. 89-98,

[41] J. Schmidhuber. System Modeling and Optimization. The Technical University of Munich (TUM) Habilitation (1993)

[42] E. Sharma, R.C. Deo, J. Soar, R. Prasad, A.V. Parisi, N. Raj. Novel hybrid deep learning model for satellite based PM10 forecasting in the most polluted Australian hotspots. Atmos. Environ., 279 (2022), Article 119111,

[43] Z.R. Struzik. Wavelet methods in (financial) time-series processing. Physica A, 296 (1–2) (2001), pp. 307-319,

[44] W. Sun, J. Sun. Daily PM2.5 concentration prediction based on principal component analysis and LSSVM optimized by cuckoo search algorithm. J. Environ. Manage., 188 (2017), pp. 144-152,

[45] B. Talbi, M.B. Jebli, M.F. Bashir, U. Shahzad. Does economic progress and electricity price induce electricity demand: A new appraisal in context of Tunisia. J. Public Aff., 22 (1) (2022), p. e2379

[46] E. Veleva, I. Zheleva. GARCH models for particulate matter PM10 air pollutant in the city of Ruse, Bulgaria. AIP Conf. Proc., 2025 (2018),

[47] D. Voukantsis, K. Karatzas, J. Kukkonen, T. Räsänen, A. Karppinen, M. Kolehmainen. Intercomparison of air quality data using principal component analysis, and forecasting of PM10 and PM2.5 concentrations using artificial neural networks, in Thessaloniki and Helsinki. Sci. Tot. Environ., 409 (7) (2011), pp. 1266-1276,

[48] P. Wang, Y. Liu, Z. Qin, G. Zhang. A novel hybrid forecasting model for PM10 and SO2 daily concentrations. Sci. Tot. Environ., 505 (2015), pp. 1202-1212,

[49] W. Wang, S. Zhao, L. Jiao, M. Taylor, B. Zhang, G. Xu, H. Hou. Estimation of PM2.5 concentrations in China using a spatial back propagation neural network. Sci. Rep., 9 (1) (2019), pp. 1-10,

[50] J.J. Wu, K. Segerson, C. Wang. Is environmental regulation the answer to pollution problems in urbanizing economies?. J. Environ. Econ. Manag., 117 (2023), Article 102754,

[51] T. Xayasouk, H.M. Lee, G. Lee. Air pollution prediction using long short-term memory (LSTM) and deep autoencoder (DAE) models. Sustainability (Switzerland), 12 (6) (2020), p. 2570,

[52] X. Xu. Forecasting air pollution PM2.5 in Beijing using weather data and multiple kernel learning. J. Forecast., 39 (2) (2020), pp. 117-125,

[53] T. Yin, Y. Wang. Predicting the price of WTI crude oil futures using artificial intelligence model with chaos. Fuel, 316 (2022), Article 122523,

[54] L. Yu, S. Wang, K.K. Lai. Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm. Energy Econ., 30 (5) (2008), pp. 2623-2635,

[55] R. Yu, Y. Yang, L. Yang, G. Han, O.A. Move. RAQ–A random forest approach for predicting air quality in urban sensing systems. Sensors (Switzerland), 16 (1) (2016), p. 86,

[56] L. Yu, Y. Zhao, L. Tang. A compressed sensing based AI learning paradigm for crude oil price forecasting. Energy Econ., 46 (2014), pp. 236-245,

[57] M.A. Zaidan, L. Dada, M.A. Alghamdi, H. Al-Jeelani, H. Lihavainen, A. Hyvärinen, T. Hussein. Mutual information input selector and probabilistic machine learning utilisation for air pollution proxies. Appl. Sci. (Switzerland), 9 (20) (2019), pp. 1-20,

[58] J. Zhang, W. Ding. Prediction of air pollutants concentration based on an extreme learning machine: The case of Hong Kong. Int. J. Environ. Res. Public Health, 14 (2) (2017), pp. 1-19,

[59] K. Zhang, X. Yang, H. Cao, J. Thé, Z. Tan, H. Yu. Multi-step forecast of PM2.5 and PM10 concentrations using convolutional neural network integrated with spatial–temporal attention and residual learning. Environ. Int., 171 (2023), Article 107691,

[60] Zhu, L., Zhu, Y., Mao, H., Gu, M., 2009. A new method for sparse signal denoising based on compressed sensing. In: 2009 2nd International Symposium on Knowledge Acquisition and Modeling, KAM 2009, 1(1), 35–38. doi: https://doi.org/10.1109/KAM.2009.25.