| Summary: | This research addresses the limitations of current Multi-Agent Systems (MAS) in Flood Early Warning and Response Systems (FEWRS), focusing on gaps in risk knowledge, monitoring, forecasting, warning dissemination, and response capabilities. These shortcomings reduce the system's reliability and public trust, highlighting the need for better flood preparedness and learning mechanisms. To tackle these issues, this study proposes a new conceptual framework combining Case-Based Reasoning (CBR) with MAS, aimed at enhancing flood prediction, learning, and decision-making. CBR enables the system to learn from past flood events by retrieving and adapting cases to improve future predictions and responses, while MAS allows for decentralized and collaborative decision-making among various agents within the system. This integration fosters a dynamic, real-time system that adapts to changing conditions and improves over time through continuous feedback. The framework's effectiveness is evaluated using the quadruple helix model, addressing social, economic, environmental, and governance aspects. Socially, the system increases community resilience through improved early warnings. Economically, it reduces flood impacts by enabling faster and more accurate responses. Environmentally, it enhances monitoring and preservation of ecosystems. In governance, the framework improves coordination between agencies and the public. The CBR-MAS framework significantly improves intelligent detection, decision-making speed, and community resilience, offering substantial improvements over traditional FEWRS. This adaptive approach promises to build a more reliable, trust-worthy system capable of handling the complexities of flood risks in the future. © (2024), (Science and Information Organization). All Rights Reserved.
|
|---|