International Research Journal of Engineering and Technology (IRJET) Volume: 12 Issue: 06 | Jun 2025 www.irjet.net
e-ISSN: 2395-0056 p-ISSN: 2395-0072
A DEEP LEARNING FRAMEWORK COMBINING DCNN AND LSTM FOR AQI TIME-SERIES CLASSIFICATION Akshayaa M1 and T. Rajasenbagam2 1PG Scholar, Dept. of CSE, Government College of Technology, Coimbatore, India 2ASSISTANT PROFESSOR, DEPT. OF CSE, GOVERNMENT COLLEGE OF TECHNOLOGY, COIMBATORE, INDIA
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A hybrid model is one that was created by combining two conventional models together, that gains its own characteristics, workflow, and advantages, ultimately creating a more effective, adaptable, and efficient model.
Abstract - Air pollution is an increasingly serious environmental and public health concern, especially in the urban regions of developing nations like India. Harmful pollutants such as particulate matter and carbon monoxide pose significant health risks. This research presents a hybrid deep learning model that integrates Deep Convolutional Neural Networks with Long Short-Term Memory networks to forecast the Air Quality Index using time-series data. The model incorporates meteorological variables—such as temperature, humidity, and atmospheric pressure alongside AQI data for both training and evaluation. The proposed architecture outperforms individual DCNN models and traditional machine learning techniques. While the DCNN component is responsible for learning spatial patterns, the LSTM component effectively captures temporal trends. The hybrid model achieves a classification accuracy of 97.45% and an AUC-ROC score of 0.97, surpassing the performance of existing approaches. This study highlights the effectiveness of the combined model in AQI forecasting, offering valuable insights for early warning systems and public health interventions against air pollution.
A DCNN is an advanced ANN tailored to analyze structured data, such as time-series, videos, and images. Their primary advantage is their capacity to automatically. They are very good at pattern recognition tasks because they can directly learn hierarchical features from raw input data. DCNNs were first created for computer vision, but they have since been effectively used in a variety of other domains, including audio analysis, environmental modeling, and natural language processing. The LSTM is a specialized form of RNN optimized for processing sequential and time-series data, effectively managing long-term dependencies. They are especially skilled at obtaining long-term relationships and resolving issues with vanishing or expanding gradients that frequently impede conventional RNN training. Because of this feature, LSTMs are ideal for applications involving temporal patterns, like natural language processing, audio identification, and timeseries forecasting. Memory cells with systems in place to handle and store data for lengthy periods of time make up an LSTM network. In the architecture, the movement of data is governed by core parts such as the input, forget, and output gates, as well as the internal cell state. A memory-enhanced RNN model cell's workflow consists of removing unnecessary information from the cell state, incorporating fresh data via the input gate, combining the new and retained data to enhance the cell state, and producing an output for the current step that also affects the subsequent phase.
Key Words: Air Pollution, Air Quality Index , Deep Learning, Hybrid Model, Deep Convolutional Neural Network, Long Short-Term Memory, Time-Series Prediction, Meteorological parameters.
1.INTRODUCTION Air pollution has become a major issue for both environmental sustainability and public health, especially in the urban regions of developing countries such as India. Rapid industrialization, vehicular emissions, and biomass burning are major contributors to the degradation of air quality in Indian cities. This presents serious health risks, such as cardiovascular and respiratory conditions, so monitoring and forecasting air quality is crucial to urban management. The Air Quality Index is a standardized measure used to represent the concentration of air pollution and its potential effects on public health.
2. RELATED WORK The most populous province in Turkey, Van, has seen a serious environmental and public health problem with air pollution. Seasonal and climatic factors make this issue especially acute during the winter months. Increased use of inferior fuels for home heating is one of the main causes of the declining air quality during this time. Particulate matter and an increase in atmospheric sulfur dioxide is caused by households using cheap but extremely polluting fuel sources when evening temperatures drop sharply. Weather patterns have a significant impact on how pollutants spread and build up. A detailed study conducted in Van City Center over a five-year period (2015–2020)
Accurate AQI prediction is crucial for enabling timely interventions, raising public awareness, and supporting evidence-based policy making. However, the complexity of air pollution, driven by non-linear interactions between various pollutants and meteorological factors, makes accurate forecasting a challenging task.
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