Evaluation of Deep Learning Model Performance for Water Quality Classification: Comparison of CNN, LSTM, and Hybrid CNN-LSTM

Abstract Water quality monitoring is crucial for protecting public health and ensuring environmental sustainability. This study uses a high-frequency time-series dataset from Brisbane, Australia, to classify water quality as either 'Suitable' or 'Unsuitable' using three deep learning models: CNN, LSTM, and a hybrid CNN-LSTM. To counter the dataset's significant class imbalance, a class weighting strategy was used during training. The models were evaluated using metrics like accuracy, precision, recall, and F1-score. The hybrid CNN-LSTM model outperformed the others, achieving superior and more balanced results with an accuracy of 97.1%, a precision of 97.1%, a recall of 97.0%, and an F1-score of 97.0%. These results demonstrate the hybrid model's enhanced ability to handle complex temporal data and extract relevant features, making it a promising solution for robust, real-time water quality warning systems for water quality management.