Hybrid deep learning model for ozone concentration prediction: comprehensive evaluation and comparison with various machine and deep learning algorithms. Issue 1 (1st January 2021)

• Record Type: Journal Article
• Authors: Yafouz, Ayman; Ahmed, Ali Najah; Zaini, Nur'atiah; Sherif, Mohsen; Sefelnasr, Ahmed; El-Shafie, Ahmed
• Is Part Of: Engineering applications of computational fluid mechanics. Volume 15:Issue 1(2021)
• Page Start: 902
• Page End: 933
• Publication Date: 2021-01-01
• Subjects: Air quality -- ozone concentration prediction -- machine learning -- deep learning -- hybrid model -- uncertainty and sensitivity analysis; Computational fluid dynamics -- Periodicals; 620.10640285
• DOI: 10.1080/19942060.2021.1926328
• Languages: English
• ISSNs: 1994-2060

Abstract

Abstract : To accurately predict tropospheric ozone concentration(O3 ), it is needed to investigate the variety of artificial intelligence techniques' performance, such as machine learning, deep learning and hybrid models. This research aims to effectively predict the hourly ozone trend via fewer input variables. This ozone prediction attempt is performed on diversity data of air pollutants (NO2, NOx, CO, SO2 ) and meteorological parameters (wind-speed and humidity). The historical datasets are collected from 3 sites in Malaysia. The study's methodology progressed in two paths: standalone and hybrid models where hourly-averaged datasets are applied based on 5-time horizon analysis scenario, with different inputs' combinations. For evaluation, all models are tested throughout 5-performance indicator and illustrated on Modified Taylor diagram. Sensitivity analysis of input variables is quantified. Additionally, uncertainty analysis is conducted to assess their confidence level associated with Willmott Index. Based on R 2, results indicated that XGBoost has higher accuracy compared to MLP and SVR; meanwhile, LSTM and CNN outweighs XGBoost. In terms of robustness and accuracy, the proposed hybrid model possesses superlative performance compared to all above-mentioned techniques. The proposed model achieved exceptional results as the highest R 2, the highest 95% confidence degree, and narrower confidence interval width, are 93.48%, 98.16%, and 0.0014195, respectively.