Flood Disaster Management using SADM-SmartObject: An Agent-Based IoT Framework for Sensing, Data Classification, and Decision Making

Annually, the devastating impact of natural disasters (such as floods, earthquakes, and forest fires) and man-made incidents (including industrial explosions, oil pipeline leaks, and gas production mishaps) results in widespread damage, financial losses, injuries, and substantial loss of life. Recognizing the imperative to evolve beyond traditional monitoring systems, this paper advocates for adopting intelligent prediction systems that actively involve governmental bodies and the communities affected by these calamities. This paper introduces the SADM-SmartObject framework, a cutting-edge, agent-based conceptual and methodological IoT framework designed for self-adaptation and decision-making, specifically applied to the context of natural flood disasters. The primary objective of this framework is to monitor key climate indicators such as rainfall, humidity, temperature, pressure, and water levels and discern their temporal correlations to enhance flood prediction accuracy. It offers a strong layered architecture complemented by efficient tools for constructing IoT systems, integrating machine learning classifiers for data classification, and agent-based approaches for decision-making. In a compelling case study, we apply the SADM-SmartObject framework to proactively prevent and manage a natural flood disaster scenario.