Hybrid deep learning model for ozone concentration prediction: comprehensive evaluation and comparison with various machine and deep learning algorithms

Ayman Yafouz, Ali Najah Ahmed, Nur’atiah Zaini, Mohsen Sherif, Ahmed Sefelnasr, Ahmed El-Shafie

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)902-933
Number of pages32
JournalEngineering Applications of Computational Fluid Mechanics
Volume15
Issue number1
DOIs
Publication statusPublished - 2021

Keywords

  • Air quality
  • deep learning
  • hybrid model
  • machine learning
  • ozone concentration prediction
  • uncertainty and sensitivity analysis

ASJC Scopus subject areas

  • Computer Science(all)
  • Modelling and Simulation

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