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.

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