Prevention of Boat Capsizing and Boat Maintenance

The trend of increasing inland water transport and tourism has been forcing the importance of enhancing safety measures that could prevent such incidents like boat capsizing. It introduces an innovative technique to predict capsizing dangers with the help of real-time weather conditions through a Heeling Test Simulator. The system considers such parameters like wind speed, sea rise, and visibility besides vessel-specific parameters and calculates the heeling angle and heeling moment so that dynamic and accurate stability assessments can be performedunder varied scenarios. A Random Forest classifier machine learning model improves risk estimation by processing real-time inputs. The system also has the appreciation of a chatbot aspect that allows for instant provision of safety recommendations from experiment results that could guarantee timely decisions for operators. Experimentation results establish the cutting edge of the proposed system in eliminating capsizing risks; thus, it can significantly contribute to such technological advancement. Future research will focus on enhancing predictive models and broadening the system's relevance to various categories of vessels and aquatic environments.