Frameworks for AI and IoT-Driven Early Warning Systems to Predict and Mitigate the Impact of Chemical Spills in Public Water Supplies
Keywords:
AI-Driven Predictive Analytics, IoT Sensor Networks, Early Warning Systems, Chemical Spill Detection, Water Quality Monitoring, Data Security and PrivacyAbstract
Chemical spills in public water supplies pose significant threats to public health and the environment. To address these challenges, this paper presents a comprehensive framework for AI and IoT-driven early warning systems designed to predict and mitigate the impact of such spills. The framework integrates advanced AI-driven predictive analytics with extensive IoT sensor networks to enhance real-time monitoring and early detection capabilities. Key findings highlight the importance of system scalability, real-time data processing, and robust design principles to ensure continuous operation and data integrity. The paper also addresses critical data security and privacy concerns, emphasizing the need for strong encryption, secure authentication, and regulatory compliance. Technical and operational challenges, such as sensor reliability and the integration of heterogeneous devices, are discussed, alongside strategies for overcoming these obstacles, including standardization and predictive maintenance. Recommendations for stakeholders and policymakers focus on fostering collaboration, investing in reliable technologies, enhancing data security measures, and raising public awareness. The proposed framework aims to provide a resilient and efficient solution for safeguarding public water supplies against chemical spills, ultimately protecting public health and the environment.
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