We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
Role of AI Technique for Controlling Micro Plastic on Marine Eco System
Summary
This paper developed a machine learning system using Support Vector Machine (SVM) algorithms to classify microplastic density in ocean water based on oceanographic sensor data, achieving 93% accuracy. The system is proposed as a scalable, automated alternative to labor-intensive manual microplastic sampling in marine environments. AI-driven monitoring tools like this could make it practical to track plastic pollution across vast ocean areas where manual surveys are infeasible.
Microplastics in marine environments are a growing ecological concern due to their small size, wide distribution, and harmful effects on aquatic life. Traditional methods for identifying these particles are labor-intensive and not suited for large-scale monitoring. This paper presents a smart detection framework using machine learning, specifically Support Vector Machine (SVM), to classify microplastic density based on oceanographic data. A publicly accessible dataset was used for training and testing, with preprocessing steps including normalization and encoding. The SVM model demonstrated superior classification accuracy of 93% compared to other algorithms like Logistic Regression and Random Forest. These results suggest that AI-driven approaches, particularly those using SVM, can significantly enhance marine pollution surveillance.