Artificial Intelligence for Marine Disease Surveillance and Prevention

Artificial intelligence technologies exhibit major opportunities for strengthening the process of marine disease observation and with prevention strategies. Classic methods that monitor diseases along with their management prove to be both ineffective and resource-consuming as well as delayed in their responses. The research establishes an investigation to build AI-based strategies which would enhance detection of marine diseases while forecasting outbreaks. The Environmental Protection Interactive Centre in Hong Kong provided historical data regarding environmental conditions like temperature, pH, salinity, turbidity, dissolved oxygen and nitrate nitrogen parameters. The prediction of disease outbreaks relied on the implementation of Random Forest alongside Support Vector Machine (SVM) and Logistic Regression and Long Short-Term Memory (LSTM) and XGBoost machine learning models which processed these parameters. The classification system followed rules that identified diseases as either coral disease or parasitic disease or environmental stress disease or bacterial infection. The model performance evaluation relied on accuracy scores together with confusion matrices as well as feature correlation heat maps and ROC curves. The disease outbreak prediction results showed promise with the use of LSTM allowing it to work with both time-related connections and sequential data. Early ecological incident prevention measures become achievable due to AI systems monitoring marine ecosystems. The study demonstrates AI's ability to handle marine disease observation which helps protect on-going marine ecosystems and their biodiversity.