Water quality PH prediction study based on t-CAOA-BiLSTM-attention modeling

In response to the challenges of difficult feature extraction and insufficient accuracy in huge data analysis and prediction of sewage indicators, this study establishes an improved LSTM model for predicting the PH value of sewage indicators. Initially, the BiLSTM bidirectional long short-term memory network is employed, followed by parameter optimization using the arithmetic optimization algorithm t-CAOA improved with adaptive t-distribution and dynamic boundary strategy, and the incorporation of a single-head self-attention mechanism. The experiments demonstrate that the improved model established in this study outperforms traditional water quality prediction models in terms of MSE, RMSE, MAE, MAPE, and R², effectively enhancing the accuracy of water quality prediction and possessing certain application value. Finally, the robustness and generalization ability of the model was verified after testing in a variety of different environments in the watershed.