## 4 Discussion  
The study identified an accurate algorithm for pollution detection in the Shatt al-Arab region based on real-world data. In addition, the findings have significant implications for environmental conservation efforts, offering a technological solution to support the preservation of the Shatt al-Arab ecosystem. The evaluation of SVM, k-NN, and RF models underscores the importance of a tailored approach in pollution detection. While each model exhibits strengths, the Random Forest model emerges as the top performer, demonstrating accuracy and reliability. These results not only pave the way for practical applications in environmental monitoring, but also contribute to the ongoing preservation and enhancement of the health of aquatic ecosystems. The continuous refinement and optimization of these machine learning models are anticipated to further enhance their effectiveness in real-world scenarios, marking a significant step forward in environmental stewardship. In summary, the Random Forest model clearly emerges as the top technique for this river pollution classification task based on the empirical evaluation across multiple performance metrics. The automated image-based river pollution monitoring system should be built using the Random Forest algorithm to ensure robust and reliable detection. The SVM also holds potential as a strong backup technique. Further research should explore combining these supervised learning models with deep neural networks to potentially improve generalizability. Comparing the results from [6] to our experimental evaluation, there are some notable similarities and differences. The previous paper obtained 68.7% precision for plastic detection using an optimized deep learning model, while our RF model achieved a higher accuracy of 94% on the river pollution image dataset. The paper model's precision dropped significantly when tested on new locations. Our other models achieved perfect accuracy. Our controlled dataset contributed to higher accuracy. In paper [14], drone hyperspectral imagery was used to estimate the TN concentration in water bodies, achieving an R2 of 0.99 with their model. Our system uses 4k resolution images from Shatt al-Arab River to detect multiple pollution types, attaining 92-94% accuracy with SVM and Random Forest models. While the previous paper focuses specifically on nitrogen, our system detects various pollutants like plastics, oil, and waste. Our system obtained comparable accuracy levels--SVM with 92% accuracy and 0.96 AUC, and Random Forest achieving 94%. The previous paper uses machine learning on image features, while we employ deep learning on raw images for end-to-end classification. Our system's strengths are as follows: it uses real-world images from Shatt al-Arab rather than drone data; it detects diverse pollution types beyond just plastics; the deep learning leverages are for end-to-end learning directly from images; it is a computationally efficient deployable system rather than using offline analysis; it achieves high accuracy without requiring hyperspectral data. In addition, deep learning algorithms benefit from a large dataset, as the weights in each layer are better tuned with more data points (training data), expectedly leading to higher accuracy compared to traditional machine learning models; thus, in the pilot study of the current paper and with the size of data that we used, utilizing SVM, KNN, and RF was more appropriate than using a deep learning model [30]. In paper [13], SVM classification was used to achieve segmentation accuracy ranging from 92.5 to 99.5% for oil detection and 85.7% for trash detection. The dataset was limited and unbalanced, so it is unclear how the system will perform when testing with a larger dataset. Our system attained 92-94% accuracy for pollution classification using SVM, RF, and KNN with a large amount of data. Our system is applicable to different pollution types--debris, oil, sewage water, etc.