Predictive analysis of maritime accident hotspots using capsule neural network optimized by modified orangutan optimization algorithm

Maritime accidents are lethal threats to lives, economies, and the environment as a result of which there is a need to develop advanced prediction models for early risk identification. In this paper, a novel framework integrated with Capsule Neural Networks (CapsNets) and a Modified Orangutan Optimization (MOO) algorithm is proposed to predict maritime accident hotspots. The CapsNet model captures spatio-temporal dependencies from the Global Maritime Distress and Safety System (GMDSS) dataset, while the MOO fine-tunes hyperparameters toward maximizing model accuracy and generalization. Experimental results suggest that the framework works exceedingly well against the baseline models by achieving an accuracy of 91.2%, while improving precision and recall, and reducing error rates on the contrary. Geospatial heatmaps and decision boundary visualizations strengthen the claim regarding the model’s capacity to identify high-hazard zones and clearly categorize incident types. Compelling case studies illustrate its potential for reducing response time through proactive monitoring and preparedness, which is possible only through integrating information with prediction methods. The study takes maritime safety analytics into a very intelligent and data-driven domain by overcoming shortcomings of existing predictive methods. The framework opens the door to the future integration into rescue resource planning systems, where predicted risk zones will inform strategies for asset deployment.