## 3 Results and Discussion
### Model Calibration and Verification
#### 3.3.1 Historical Data  
Validating the model's performance is primarily achieved through the utilization of historical data. The ability of a model to accurately replicate past data is vital, as it serves as a foundation for future predictions. Once the model is constructed, its outputs are compared and aligned with actual historical data. The presence of a strong correlation between the model's outputs and the real data instills confidence in the model's results, affirming its capacity to accurately forecast future events. By calibrating the model with these historical datasets, it becomes possible to discern complex relationships and dynamics between dumping sites, water pollution, fishing resources, the total economy, and the population. The interpretation of this calibrated model provides a holistic understanding of the system, enabling informed decision-making, policy formulation, and the identification of potential areas for intervention to achieve sustainable development and environmental protection goals. During the calibration phase, the model is adjusted to ensure that it accurately represents the historical data and captures the relationships between different variables. This process fine-tunes the model's parameters and equations, enabling it to replicate past events and patterns effectively. By achieving a close match between the model's outputs and the historical data, confidence is gained in the model's ability to capture the underlying dynamics of the system. The calibration process serves as a critical step in verifying the model's reliability and suitability for simulating real-world scenarios. It involves comparing the model's simulated outputs with actual data collected from past events or experimental studies. During this process, discrepancies between the model's outputs and the observed data are carefully analyzed, and adjustments are made to minimize these differences. Ultimately, a well-calibrated model serves as a powerful tool for understanding the dynamics of waste dumping in the sea and predicting the potential outcomes of various policy interventions and management strategies. The calibration process's comprehensive nature contributes to building confidence in the model's outcomes, facilitating informed decision-making, and supporting sustainable solutions for addressing water pollution challenges. Figures 4-8 represent the calibration of the model simulation with historical data for various variables related to dumping sites, water pollution, fishing resources, the total economy (GDP), and population. These calibration figures showcase the comparison between the model's simulated outputs and the observed historical data for each respective variable. As shown in these figures, the present model is properly verified with actual historical data, and the model's performance and predictive capabilities can be assessed and validated. Figure 4: Calibration of the simulation with historical data for dumping sites. Figure 5: Calibration of the simulation with historical data for water pollution. Figure 6: Calibration of the simulation with historical data for fishing resources. Figure 7: Calibration of the simulation with historical data for the total economy (GDP). Table 1 presents the calibration results of the system dynamics model, where simulation outputs are compared with historical data related to dumping sites. The present calibration is a crucial step in validating the model's performance. It ensured that the model aligned with historical trends and reproduced past events accurately, increasing confidence in its ability to make reliable forecasts for future scenarios. With an average error of 8.94%, it demonstrates that the model's outputs are relatively close to the historical data, indicating a reasonable level of accuracy in capturing the dynamics of the dumping sites. Given the complexities and uncertainties often associated with environmental systems, achieving an average error of less than 10% is often considered acceptable and indicative of a well-calibrated model.