## 4 Discussion
It leverages real visual data from Shatt al-Arab and provides versatile real-time pollution detection using readily available visual data and efficient algorithms. The novelty of this research lies in the use of unmanned aerial vehicles for extensive aerial surveys of river ecosystems. By employing drones equipped with cameras, the study captures high-resolution imagery of the Shatt al-Arab River and its tributaries, enabling a detailed examination of pollution sources and environmental conditions. The application of machine learning algorithms, including SVM, KNN, and RF, for pollution detection in river ecosystems is novel because we observed three pollutants, namely, oil spills, wastewater, and debris. These algorithms analyze the collected imagery data to automatically identify and classify pollutants, providing valuable insights for environmental management strategies. The use of color-based features extracted from images using the HSV color space represents an innovative approach to pollution detection. By decoupling hue, saturation, and value information, this technique enables targeted feature engineering, enhancing the models' ability to detect distinctive color patterns associated with different types of pollutants on the water surface. The current study conducted an evaluation of model performance using confusion matrices and sensitivity and specificity metrics, providing a granular view of each model's classification capabilities. This comprehensive assessment ensures robustness and reliability in pollution detection, facilitating informed decision-making for environmental management. The high accuracy of our tested SVM, RF, and KNN models holds significant implications for real-world applications in environmental management. For instance, the ability to accurately identify and classify pollutants in aerial imagery can facilitate the early detection of environmental hazards, allowing for timely intervention and mitigation measures. This can help protect the ecosystem of the Shatt al-Arab River and its surrounding areas, safeguarding the health and well-being of local communities reliant on the river for various activities such as fishing, agriculture, and recreation. Furthermore, these models can support decision-making processes for environmental agencies and policymakers by providing valuable insights into pollution hotspots, trends over time, and potential sources of contamination. By leveraging the predictive capabilities of these models, stakeholders can implement targeted interventions to reduce pollution levels, restore ecosystem health, and ensure the sustainable management of the river ecosystem. Despite the promising performance of the models, several limitations were encountered during development and evaluation. One notable challenge was the availability and quality of the training data, which can introduce biases or inconsistencies in model predictions. Additionally, the models struggled to generalize to unseen environmental conditions or respond effectively to changes in pollutant characteristics or distribution patterns. Moreover, the computational resources required for the real-time application of these models in large-scale monitoring operations could pose practical challenges, particularly in resource-constrained settings. Addressing these limitations will require ongoing efforts as future work to refine the model algorithms, improve data collection methods, and enhance collaboration among stakeholders to ensure the models' relevance and reliability in real-world applications. Addressing potential challenges in scaling or adapting the pollution detection system to other regions is crucial for understanding its broader applicability. Different regions may exhibit varying environmental characteristics, such as water turbidity, vegetation cover, and land use patterns. These variations can impact the performance of pollution detection models trained on data from a specific region.