Raw Datasets for I-AMPPM Framework Validation: Multi-Scenario ROC Metrics and 90-Run Simulation Results

This repository contains the raw empirical data and performance validation metrics supporting the research article: "An Intelligent and Adaptive Environmental Governance Framework for Sustainable Oceans: Policy-Oriented Integration of Monitoring and Response Systems for Ports and Offshore Pollution." To ensure full scientific transparency and reproducibility, we have provided the following two primary datasets: 1. Supplementary Data Sheet 1: ROC Analysis Metrics (roc_data_combined.csv) This dataset contains the performance evaluation results for the AI-driven sensing layer across three critical maritime pollution scenarios: Port-area Oil Spill, Offshore Vessel Leakage, and Illegal Discharge. Metrics included: False Positive Rate (FPR), True Positive Rate (TPR), Youden’s J Index, and Area Under the Curve (AUC). 2. Supplementary Data Sheet 2: Simulation Raw Dataset (I-AMPPM_simulation_dataset.csv) This dataset provides the complete output records for 90 independent simulation runs (30 runs per scenario). Variables included: run_id, scenario type, area_true_m2, area_pred_m2, latency_ms, precision, recall, and RMSE. Methodological Summary: The data was generated using a high-fidelity maritime simulation environment. AI detection was validated against varied environmental noise. All statistical analyses and 95% confidence intervals reported in the study were derived from these raw datasets.