AI-Driven Water Quality Prediction For Aquatic Ecosystem Using Deep Learning and Automated Hyperparameter Optimization

Accurate water quality assessment is critical for environmental monitoring and public health. This research proposes a deep learning-based classification model for water quality prediction for aquatic ecosystems, leveraging automated hyperparameter optimization via Optuna to enhance accuracy and computational efficiency. The dataset undergoes rigorous preprocessing, including feature standardization with StandardScaler and stratified train-test splitting to maintain class distribution integrity. The neural network architecture consists of three fully connected hidden layers with ReLU activation, batch normalization for training stability, and dropout regularization to mitigate overfitting. Model training employs the Adam optimizer with a dynamic learning rate scheduler (ReduceLROnPlateau). The optimization process tunes key hyperparameters, including neuron allocation per layer, dropout rate, and learning rate, yielding an optimal configuration of 179-104-36 neurons, 0.2586 dropout, and a 0.00999 learning rate. Experimental evaluation using stratified k-fold cross-validation achieves a test accuracy of 98.02%, with high precision, recall, and F1-scores, ensuring robust predictive performance. The model that has been trained has been successfully implemented and has a well-defined and standardized process for handling input. This allows the model to generalize smoothly over a wide range of extremely dissimilar datasets that are specifically pertinent to water quality measurements. In comparison to the traditional models that have been applied in the past, this new AI-based method has been shown to have an unprecedented level of enhanced generalization capabilities. It also contains significantly reduced levels of computational overhead, which is a major advantage, as well as improved reliability when conducting classification tasks. These exemplary features make it an extremely scalable solution that is highly suited for the application of real-time water quality monitoring, particularly in the specific application of IoT-based environmental monitoring systems.