Deep Hybrid AI Models Applied to Predict, Model, and Forecast the Next Upcoming Periods of Ozone in Craiova City

Abstract

Artificial intelligence (AI) plays an important role in analyzing air quality, providing new insights that enable informed environmental policy decisions at the local level based on air pollution modeling and forecasting. The aim of this study is to analyze various hybrid AI methods to predict, model, and anticipate hourly ground-level ozone concentrations. Ground-level ozone concentrations impact human health and the environment. The data used in this study was downloaded from the website of the Romanian Agency for Environmental Protection and spans five years (2020–2024). The dataset comprises two categories of data: (i) seven meteorological parameters, including temperature (T), relative humidity, precipitation, air pressure, solar brightness, wind direction, and velocity; (ii) twenty air pollutants, including two types of particulate matter, carbon monoxide, sulfur dioxide, ground-level ozone, three types of nitrogen oxide, ammonia, six volatile organic compounds, and five toxic elements. The study follows a six-stage approach: (1) data preprocessing is conducted to identify and address anomalies, outliers, and missing values, while ozone trends are analyzed; (2) correlations between ozone concentrations and other variables are examined, considering only non-missing values; (3) data splitting is carried out in training and testing sets; (4) a total of 27 hybrid AI-based algorithms are applied to determine the optimal predictive model for ozone concentration based on related variables; (5) fifty feature selection methods are applied to find the most relevant predictors for predicting ozone concentration; (6) a novel deep NARMAX model is employed to model and anticipate hourly ozone levels in Craiova. Using a set of statistical metrics, the results of the models are assessed. This research provides a novel perspective on the robustness of the predictive performance of the proposed model.

Symbolic Artificial Intelligence for Ground-Level Ozone Prediction Through Association Rule Mining

“…Numerous studies have focused specifically on predicting ozone concentrations in particular, which constitute a key indicator of air quality and have significant implications for public health and environmental management [18]. Other studies have investigated ozone prediction using AI techniques, including [19,20,21]. These studies have demonstrated the ability of AI to accurately predict ozone concentrations by capturing complex nonlinear relationships and interactions among atmospheric variables that are difficult to model using conventional statistical approaches.…”

Symbolic Artificial Intelligence for Ground-Level Ozone Prediction Through Association Rule Mining

“…Numerous studies have focused specifically on predicting ozone concentrations in particular, which constitute a key indicator of air quality and have significant implications for public health and environmental management [18]. Other studies have investigated ozone prediction using AI techniques, including [19,20,21]. These studies have demonstrated the ability of AI to accurately predict ozone concentrations by capturing complex nonlinear relationships and interactions among atmospheric variables that are difficult to model using conventional statistical approaches.…”

Symbolic Artificial Intelligence for Ground-Level Ozone Prediction Through Association Rule Mining

“…Numerous studies have focused specifically on predicting ozone concentrations in particular, which constitute a key indicator of air quality and have significant implications for public health and environmental management [18]. Other studies have investigated ozone prediction using AI techniques, including [19,20,21]. These studies have demonstrated the ability of AI to accurately predict ozone concentrations by capturing complex nonlinear relationships and interactions among atmospheric variables that are difficult to model using conventional statistical approaches.…”

Symbolic Artificial Intelligence for Ground-Level Ozone Prediction Through Association Rule Mining

“…Numerous studies have focused specifically on predicting ozone concentrations in particular, which constitute a key indicator of air quality and have significant implications for public health and environmental management [18]. Other studies have investigated ozone prediction using AI techniques, including [19,20,21]. These studies have demonstrated the ability of AI to accurately predict ozone concentrations by capturing complex nonlinear relationships and interactions among atmospheric variables that are difficult to model using conventional statistical approaches.…”