Deep Learning Models for Predicting and Mitigating Environmental Impact of Industrial Processes in Real-Time - Jessica Obianuju Ojadi; Olumide Akindele Owulade; Chinekwu Somtochukwu Odionu; Ekene Cynthia Onukwulu (International Journal of Scientific Research in Science, Engineering and Technology, 2025)
Industrial processes contribute significantly to environmental degradation through emissions, waste, and resource depletion. The need for real-time monitoring and mitigation strategies has led to the adoption of deep learning (DL) models for predictive analytics and automated decision-making. This study explores the application of deep learning techniques in predicting and mitigating the environmental impact of industrial activities. We review state-of-the-art deep learning architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTM) networks, and transformers, in processing large-scale environmental data. These models analyze real-time sensor data, satellite imagery, and industrial parameters to forecast pollution levels, detect anomalies, and optimize industrial operations for sustainability. Key advancements in deep learning, such as hybrid architectures integrating deep reinforcement learning (DRL) and generative adversarial networks (GANs), enhance predictive accuracy and robustness in environmental monitoring systems. Transfer learning and federated learning approaches facilitate scalable and adaptive solutions across diverse industrial sectors. The study highlights the role of DL in early detection of air and water pollution, energy consumption optimization, and emission control through predictive maintenance and process adjustments. Moreover, integrating explainable artificial intelligence (XAI) ensures model interpretability, fostering trust among policymakers and industry stakeholders. Challenges in deploying deep learning models include data heterogeneity, computational complexity, and model interpretability. To address these issues, we discuss techniques such as data augmentation, adversarial training, and edge AI implementation for real-time processing. Ethical and regulatory considerations surrounding AI-driven environmental monitoring are also examined to ensure compliance with sustainability standards. This research underscores the transformative potential of deep learning in industrial sustainability, emphasizing its role in real-time decision support systems. Future directions involve integrating quantum computing and neuromorphic computing for enhanced model efficiency and expanding interdisciplinary collaborations for AI-driven environmental governance. By leveraging deep learning for predictive environmental impact assessment, industries can transition toward greener and more efficient operational frameworks.