<![CDATA[Floods are among the natural disasters that can cause substantial damage, particularly in tourist locations with high visitor traffic. This paper proposes the implementation of an IoT-based predictive method for early flood disaster warnings in tourist areas. The proposed system utilizes IoT sensors to monitor environmental conditions in real-time and employs machine learning-based predictive models to forecast the likelihood of flooding. By continuously collecting and analyzing data such as rainfall, river water levels, and soil moisture, the system can predict potential flood events with a relatively high degree of accuracy. The research involved developing and testing the system in a controlled environment to evaluate its performance. The results demonstrated that the system could provide timely early warnings, allowing tourist site managers to take necessary preventive measures to protect visitors and infrastructure. The implementation of such a system can significantly reduce the impact of floods by providing actionable information well in advance of potential disasters. This early warning capability is crucial in tourist areas where rapid response is necessary to ensure the safety and well-being of visitors. Overall, the study highlights the effectiveness of combining IoT technology with predictive analytics in disaster management and risk mitigation]]>
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