<![CDATA[Flooding remains one of the most devastating natural hazards in developing countries, with significant impacts on human lives, infrastructure, and livelihoods. In Nigeria, particularly in Bayelsa State, recurrent flooding events highlight the need for effective and accessible flood risk communication systems. However, existing approaches largely rely on static and non-interactive dissemination channels, limiting timely public engagement and response. This study addresses this gap by designing and implementing a conversational agent capable of providing real-time responses to frequently asked flood-related questions. The proposed system adopts a rule-based conversational framework supported by natural language preprocessing techniques, including tokenization and normalization, for query interpretation. A structured knowledge base containing flood preparedness and response information was developed for the study area. The system was evaluated using a set of 120 representative flood-related queries derived from domain-specific scenarios. Experimental results show that the chatbot achieved a response accuracy of 87.5% and a successful query handling rate of 90.8%. These findings demonstrate the feasibility of conversational agents as effective tools for enhancing flood risk communication and public awareness. The study contributes to the integration of artificial intelligence-driven solutions into disaster risk management and highlights the potential of chatbot systems in improving access to critical environmental information in resource-constrained settings.]]>
