Jul 2026· International Scientific Journal of Engineering and Management· 0 citations
TL;DR
An optimized machine learning-based Air Quality Forecasting System that integrates Extreme Learning Machines (ELM) and Genetic Algorithms (GA) to predict short-term variations in air quality and demonstrates a robust, scalable, and practical solution for short-term air quality prediction.
Abstract
Abstract— Air pollution poses one of the most serious environmental threats to public health and sustainable urban living. Accurate and timely forecasting of the Air Quality Index (AQI) can support policymakers, environmental agencies, and citizens in taking proactive measures to mitigate pollution effects. This project presents an optimized machine learning-based Air Quality Forecasting System that integrates Extreme Learning Machines (ELM) and Genetic Algorithms (GA) to predict short-term variations in air quality.
The ELM component provides a high-speed, single-hidden layer feedforward neural network capable of mapping complex non-linear relationships between pollutants. To overcome the traditional challenge of random weight initialization in ELMs, a Genetic Algorithm (GA) is implemented to "evolve" and select the most optimal input weights and biases. This hybrid approach ensures that the model captures intricate correlations among multiple pollutants and meteorological variables with significantly higher computational efficiency than iterative deep learning models.
A comprehensive dataset containing historical pollutant concentrations (PM2.5, PM10, NO₂, SO₂, CO, NH₃, O₃) and meteorological parameters (temperature, humidity, wind speed, etc.) is collected and preprocessed to remove noise, handle missing values, and normalize features using Min-Max scaling. The hybrid GA-ELM model is trained and evaluated using key performance metrics such as Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). Experimental results demonstrate that the proposed architecture achieves superior prediction speed and stability compared to traditional regression models and standalone neural networks.
In addition to the model, the system includes an interactive Flask-based web dashboard for real-time AQI visualization and forecasting. The dashboard provides 1–12 hour ahead predictions, dynamic charts, and color-coded AQI indicators based on national standards for easy interpretation.Overall, the developed system demonstrates a robust, scalable, and practical solution for short-term air quality prediction. .
Air pollution has become one of the most significant environmental and public health challenges worldwide, necessitating accurate prediction systems to support effective air quality management and informed decision-making. Deep learning has emerged as a powerful approach for modelling the complex nonlinear and spatiotemporal relationships that characterise air quality data. This review provides a comprehensive synthesis of recent advances in deep learning techniques for air pollution prediction. Following the PRISMA 2020 guidelines, the literature was systematically reviewed to evaluate deep learning architectures, environmental data sources, model performance, and emerging research trends. The review traces the evolution of deep learning models from Artificial Neural Networks (ANNs) to Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, Gated Recurrent Units (GRUs), Transformer-based models, transfer learning, and hybrid architectures. It further examines the integration of ground monitoring stations, satellite remote sensing, meteorological observations, IoT-based sensors, and multi-source data fusion for improving prediction accuracy and model robustness. Comparative analysis indicates that hybrid and Transformer-based models consistently achieve superior predictive performance, while RMSE, MAE, and R² remain the most widely adopted evaluation metrics. The review also identifies key research challenges, including data scarcity, model interpretability, computational complexity, and benchmark standardisation, and highlights emerging directions such as Explainable Artificial Intelligence, Graph Neural Networks, Physics-Informed Neural Networks, Federated Learning, and Edge AI for developing accurate, scalable, and intelligent air quality prediction systems.
Mary Ann Yeboah, Evans Kotei, T. Ramkumar et al.· Archives of Computational Me...· 0 citations
Air pollution is an escalating concern driven by urbanization and population growth, leading to significant health problems. Accurate information on air quality and associated health risks is essential for effective environmental management. In this study, the implementation of Artificial Neural Network (ANN) techniques for hourly, daily, & monthly air pollution prediction is investigated. The study highlights that ANN methods offer superior accuracy in air pollution forecasting compared to traditional approaches. It emphasizes that the accuracy of ANN-based models is influenced by input parameters and the type of architecture algorithms employed. Due to their ability to process a diverse range of meteorological data, ANN models prove to be more reliable and precise than other empirical models. The study also emphasizes the present research gaps pertaining to the utilize of neural networks in air pollution prediction. This analysis aims to stimulate further research and encourage advancements in artificial intelligence for improving air quality forecasting. The proposed system integrates heterogeneous real-time data streams — including ground-level electrochemical sensor readings, satellite-derived atmospheric measurements, vehicular flow indices, and localized meteorological parameters — into a unified analytical pipeline.Spatial interpolation modules further enhance prediction granularity across city zones with uneven sensor coverage.
The system delivers pollutant concentration forecasts for particulate matter (PM2.5, PM10), nitrogen dioxide (NO₂), carbon monoxide (CO), and ozone (O₃) at hourly and 48-hour prediction horizons.Beyond technical performance, the framework is engineered for operational integration with Smart City command infrastructure, enabling automated public advisory dissemination, dynamic traffic rerouting triggers, and evidence-based environmental policy support. This study establishes a replicable, data-driven blueprint for intelligent air quality governance in the era of connected urban environments.
M. Kshirsagar, Sopan Bapu, D. Abdul et al.· Journal of Intelligent Decis...· 0 citations
Air quality forecasting has become an important tool for the management of public health and urban planning with the rapid increase of the urban population and the vehicular traffic. Although deep learning models have been able to predict the Air Quality Index (AQI) with a high accuracy, their black-box nature limits their use by regulators and city planners who need transparent and trustworthy support for their decision-making. This paper aims to design a explainable deep learning model taking into account meteorological parameters, traffic-flow factors and historical pollutant information to predict the AQI and detect abnormal pollution events. The combination of CNN-BiLSTM with an attention mechanism is used to model the spatial correlation between pollutants and long-term temporal correlations. To understand the model, SHapley Additive exPlanations (SHAP) and Integrated Gradients, which quantify the contribution of different meteorological and traffic features to each prediction, are used. An unsupervised residual-thresholding module also identifies abnormal AQI episodes that do not match the normal pattern, e.g. due to an increase in traffic congestion or an atmospheric temperature inversion. The experimental results using a multi-source dataset that is a combination of air-quality monitoring records, meteorological observations, and traffic-sensor data demonstrate that the proposed model achieves a coefficient of determination (R²) of 0.943 and a root-mean-square error (RMSE) of 9.82, surpassing the baseline LSTM, GRU, and CNN-LSTM models. The lagged PM2.5 concentration, traffic volume and relative humidity are the top three factors, which agree with the knowledge of the atmospheric chemistry, and thus confirms the validity of the model's reasoning process. The proposed framework shows that the prediction accuracy and interpretability can be achieved concurrently, and can provide a practical and transparent decision support tool for environmental agencies and smart-city traffic management systems.
Meena Kumari, Jitendra Nath Shrivastava, Arijit Dey· International journal of com...· 0 citations
Accurate forecasting of air pollution is important for monitoring the environment and protecting human health. The Air Quality Index (AQI) is commonly used to measure pollution levels, but forecasting it proves to be challenging because of the complex interactions that occur between contaminants, weather conditions, and changes that happen over time. This study utilizes machine learning (ML) techniques to predict the Air Quality Index (AQI) based on pollution and environmental data. The method uses feature analysis, data pre-processing, and an ensemble voting regression model. Chaotic-Guided Crested Porcupine Optimizer (CG-CPO) is used to find the best values for the parameters. The model achieves low error values with a Mean Absolute Error of 5.72 and a Root Mean Square Error of 8.71, alongside a high R2 score of 0.981, indicating strong results. To facilitate understanding of the data, AQI values are categorized into standard air quality classifications. This approach offers a useful and reliable method for monitoring air quality and making decisions based on that information.
Meshram Lalitha, Dr. V. Jaiganesh· 2026 6th International Confe...· 0 citations
Accurate air pollution forecasting is vital for protecting the environment and public health. However, predicting air pollution continues to be a challenge due to complicated relationships between meteorological variables, pollutant concentrations, and time-dependent characteristics. This paper proposes a Temporal-Aware Multi-Task Learning (TMTL-AQI) framework to assess urban air quality via structured data. The model simultaneously performs three tasks: Air Quality Index category classification, PM2.5 and PM10 particulate matter regression, and auxiliary AQI value prediction. The framework consists of data preprocessing, cyclical temporal encoding, a shared backbone based on hard parameter sharing, and three task-specific output heads optimized jointly. It uses cyclical transformations to encode temporal traits that capture periodic patterns of the environment and produces a common neural representation through hard parameter sharing. The TRAQID dataset was used for empirical testing. The model outperforms single-task and baseline multi-task models with an accuracy of 0.7605 and an F1-score of 0.7422 for AQI classification. Although based solely on structured input (no image-based features), the model performed competitively with a state-of-the-art approach due to the reduced prediction error in regression tasks (MAE values of 13.34 μg/m³ for PM2.5 and 22.06 μg/m³ for PM10).
Iman Youssif Ibrahim, D. M. Ahmed· Dasinya Journal for Engineer...· 0 citations
Rapid urbanization, industrialization, increasing vehicle emissions, fossil fuel consumption, and construction activities have significantly deteriorated urban air quality, posing serious risks to public health, the environment, and the economy. Traditional air quality monitoring systems, which rely on fixed monitoring stations and statistical forecasting methods, often lack adequate spatial coverage and fail to capture the complex relationships among environmental factors. Artificial Intelligence (AI) offers an effective alternative by integrating data from IoT sensors, satellite observations, meteorological stations, traffic systems, and historical pollution records to generate accurate real-time air quality predictions. This paper presents an AI-based predictive framework for urban air quality management that combines data preprocessing, feature engineering, machine learning, deep learning, and ensemble models. The framework analyzes key environmental parameters, including particulate matter (PM₂.₅ and PM₁₀), gaseous pollutants, weather conditions, traffic density, and industrial emissions. Advanced preprocessing techniques improve data quality, while algorithms such as Random Forest, Support Vector Machine (SVM), Artificial Neural Networks (ANN), Long Short-Term Memory (LSTM), and Gradient Boosting enhance forecasting accuracy. The proposed model supports intelligent decision-making by enabling early pollution warnings, optimized traffic management, industrial emission control, and sustainable urban planning. Experimental results demonstrate that AI-based models outperform conventional statistical approaches in prediction accuracy and computational efficiency. Overall, the framework provides a scalable and reliable solution for smart city applications, contributing to healthier, more sustainable, and resilient urban environments.
Seshagiri N, Narendra Karmarkar· International Journal of Mod...· 0 citations
