Development of Intelligent Pollution Control Frameworks Using Deep Learning and Sensor Networks
Abstract
Environmental pollution has become a critical global challenge due to rapid industrialization, urban expansion, and increasing anthropogenic activities. Conventional pollution monitoring and control systems often suffer from delayed response times, limited predictive capabilities, and inefficient resource utilization. This study addresses the need for intelligent and proactive pollution management by developing an integrated pollution control framework based on deep learning techniques and wireless sensor networks. The primary objective of this research is to design and evaluate an intelligent framework capable of real-time pollution monitoring, prediction, and automated control. A quantitative research approach was adopted, utilizing distributed environmental sensors to collect data on key pollution indicators, including air quality, particulate matter, carbon emissions, and hazardous gases. The collected data were processed through deep learning models, particularly Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN), to identify pollution patterns and forecast pollution levels with high accuracy. The findings indicate that the proposed framework significantly improves pollution prediction accuracy, enhances real-time environmental monitoring, and enables timely intervention strategies. The deep learning models demonstrated strong performance in detecting abnormal pollution events and forecasting future pollution trends. Furthermore, the integration of sensor networks facilitated continuous data acquisition and rapid decision-making. The study concludes that intelligent pollution control frameworks combining deep learning and sensor network technologies offer a scalable and efficient solution for environmental management. The proposed system can support policymakers, environmental agencies, and smart city administrators in reducing pollution-related risks and promoting sustainable urban development. Future research may explore the integration of Internet of Things (IoT) infrastructures and edge computing to further improve system efficiency and responsiveness.
Keywords: Deep Learning, Sensor Networks, Pollution Control, Environmental Monitoring, Smart Cities, Artificial Intelligence, Sustainable Development.
https://doi.org/10.5281/zenodo.20751292
