<![CDATA[In this study, we present a comprehensive approach to address a critical challenge in environmental science: the accurate prediction of dissolved oxygen (DO) levels in river ecosystems. Leveraging advanced machine learning techniques, particularly Variational Autoencoders (VAEs), our research aims to overcome the limitations posed by sparse and incomplete environmental datasets. We meticulously curated a dataset from multiple water monitoring stations, capturing key indicators such as DO, ammonium ions, nitrites, nitrates, and biochemical oxygen demand. Following data standardization and quality assessment, we implemented a RandomForestRegressor to ascertain feature importance, utilizing GridSearchCV and RandomizedSearchCV for model optimization. This allowed for precise feature selection to inform the predictive model. Anomaly detection was performed using One-Class SVM and Isolation Forest methodologies, essential for purifying the dataset by removing outliers. Subsequently, VAEs were applied to augment the data, synthesizing new data points that were statistically coherent with the original set, thus enriching the dataset and potentially unveiling concealed patterns. The augmented data's impact was evaluated through a RandomForestRegressor model, comparing RMSE scores before and after data augmentation, revealing a notable improvement in predictive accuracy with the lowest RMSE observed for the model utilizing VAE-generated data. This underscores the VAE's value in enhancing the model's performance, indicating that the synthetic data provided additional variability and complexity that aided the model's learning process. Our findings indicate that integrating sophisticated data augmentation techniques like VAEs can significantly enhance the quality of environmental datasets and the accuracy of predictive models.]]>
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