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@article{185841,
        author = {Swapnil Hengade and Yogesh Pagar and Nilam Thombare and Shivani Malusare},
        title = {Smart Environmental Monitoring With Blockchain Audit And ML Forecasting},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {5},
        pages = {3272-3281},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=185841},
        abstract = {Environmental monitoring is critical for managing pollution, predicting natural disasters, and ensuring public health and ecological sustainability. The convergence of Internet of Things (IoT), Blockchain, and Machine Learning (ML) technologies offers a robust framework for next-generation smart environmental monitoring (SEM) systems. This review explores how Blockchain ensures data integrity and auditability, while ML enables accurate forecasting based on large-scale sensor data. We analyze current advancements, integration models, and real-world applications, and identify key challenges and future research directions in this interdisciplinary domain.},
        keywords = {This review explores the convergence of smart environmental monitoring, blockchain technology, and machine learning forecasting to address challenges in real-time environmental data collection, data integrity, and predictive analytics. By leveraging iot sensors for continuous monitoring and blockchain for secure, auditable, and decentralized data storage, the system ensures environmental data transparency and compliance. Furthermore, machine learning models enable anomaly detection, trend prediction, and environmental risk forecasting, facilitating proactive decision-making.},
        month = {October},
        }