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This research investigates using Convolutional Neural Networks (CNN) and Water Wave Optimization (WWO) to improve predictive modeling in water treatment operations. The method utilizes Convolutional Neural Networks (CNN) for their strong prediction skills and integrates them with Weather-Water-Ocean (WWO) data to enhance the selection of relevant features, aiming to improve the accuracy and efficiency of forecasting water quality indicators. The research systematically compares the performance of the CNN-WWO model with standalone CNN models, specifically evaluating parameters such as accuracy, precision, recall, and F1-score. The results demonstrate that the CNN-WWO model significantly surpasses the solo CNN, exhibiting an accuracy boost of about 2%. Additionally, there are noticeable improvements in precision and recall. This underscores the efficacy of the integrated strategy in reducing the occurrence of false positives and false negatives, which is crucial for optimizing the efficiency of water treatment operations. The conclusion highlights the model’s capacity to transform water treatment procedures while also recognizing constraints associated with computing requirements and applicability to diverse environmental situations. The results emphasize the possibility of using sophisticated machine learning methods to improve the sustainability and effectiveness of water treatment systems, establishing a basis for further study to broaden the model’s usefulness. |
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