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@article{177624,
        author = {C.Prema and M.devasurya and M.Dhanush and V.Dhanushraj and R.Parasuraman},
        title = {Multi-Sensor Digital Twin Framework for Safe Water Resource Allocation and Early Contamination Detection in Underserved Regions},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {1128-1135},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=177624},
        abstract = {In response to the increasing challenges in modern water management—such as contamination, leakage, scarcity, and inefficient distribution—this work proposes a comprehensive multi-sensor digital twin framework designed to enhance the safety, sustainability, and efficiency of water resource allocation. With water becoming an ever more critical resource, especially in urban environments, the need for smarter systems that can anticipate and respond to issues in real time is urgent. The proposed digital twin framework integrates intelligent data processing techniques and advanced machine learning algorithms to detect anomalies, predict system failures, and dynamically optimize water distribution across networks. By analyzing real-time data from multiple sensors monitoring flow rate, water quality, consumption patterns, and pressure variations across various zones, the system can forecast potential issues such as pipeline leaks, contamination events, or demand surges, and recommend timely, proactive interventions to mitigate risk. Furthermore, the digital twin adapts to changing environmental conditions by incorporating external data sources like weather forecasts, rainfall data, and user-defined safety thresholds. This helps ensure that water is allocated efficiently, responsibly, and equitably, even in the face of variability and uncertainty. Designed as a scalable, cloud-based solution, the system supports real-time visualization, continuous learning, and predictive analytics. It empowers utilities, municipalities, and decision-makers to improve service reliability, reduce water loss, and safeguard public health through smarter, data-driven water resource management strategies.},
        keywords = {Water management, contamination, leakage, inefficient distribution, multi-sensor, digital twin, framework, safety, sustainability, water resource allocation, intelligent data processing},
        month = {May},
        }