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<p>This work presents the water quality monitoring and contamination tracing framework, which integrates Internet of Things (IoT) technologies, unmanned aerial vehicles (UAVs), blockchain, and artificial intelligence (AI) to address challenges in flood monitoring and water-quality assessment under Australian conditions. The framework provides a holistic approach to real-time, reliable, and secure flood-related data acquisition and analysis. Its architecture couples sensor-equipped UAVs for aerial observation with IoT devices for ground-level monitoring, while a blockchain layer preserves data integrity, provenance, and auditability across the end-to-end pipeline. AI algorithms analyse this multi-source data, offering predictive insights into flood patterns, potential risks, and water quality degradation. A proof of concept and stress testing simulations demonstrated the framework’s potential to enhance flood response strategies and water management practices. The results showcased the framework’s resilience in maintaining performance under challenging conditions, while also highlighting scalability considerations for AI processing and bandwidth utilisation in large-scale deployments.</p>

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An integrative framework for flood monitoring and water quality management enabled by emerging IoT, drone, blockchain, and AI technologies

  • 期刊论文

  • 发表日期：

    2026年1月6日

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  • Mahmoud Elkhodr,
  • Abdallah Al-Sabbagh

摘要

This work presents the water quality monitoring and contamination tracing framework, which integrates Internet of Things (IoT) technologies, unmanned aerial vehicles (UAVs), blockchain, and artificial intelligence (AI) to address challenges in flood monitoring and water-quality assessment under Australian conditions. The framework provides a holistic approach to real-time, reliable, and secure flood-related data acquisition and analysis. Its architecture couples sensor-equipped UAVs for aerial observation with IoT devices for ground-level monitoring, while a blockchain layer preserves data integrity, provenance, and auditability across the end-to-end pipeline. AI algorithms analyse this multi-source data, offering predictive insights into flood patterns, potential risks, and water quality degradation. A proof of concept and stress testing simulations demonstrated the framework’s potential to enhance flood response strategies and water management practices. The results showcased the framework’s resilience in maintaining performance under challenging conditions, while also highlighting scalability considerations for AI processing and bandwidth utilisation in large-scale deployments.