![]() ![]() The developed transparent soft sensor can offer a trustworthy tool for the decision-makers to monitor TN and TP in waterbodies, avoiding the expensive acquisition cost and controlling the eutrophication risk. ![]() SHAP analysis provided a clear explanation of the proposed CAE-DFC model outcomes in the prediction of TN and TP, enhancing the reliability and transparency of the model. Whether you are logged on to Windows as an administrator. The explainable CAE-DFC model-based soft sensor showed a superior online prediction of TN and TP concentrations with R 2 = 0.9607 and 0.9137, respectively. Whether Windows XP, Windows Vista, or Windows 7 is running in a 32-bit or a 64-bit operating environment. The explainable stacked CAE-DFC-based soft sensor model can effectively capture complex nonlinear relationships among water quality parameters and provide superior prediction and interpretability using the explainable SHapley Additive exPlanations (SHAP). This study aims to develop a new transparent high-frequency soft sensing model for the online estimation of TN and TP concentrations, based on explainable artificial intelligence (XAI) and convolutional autoencoder (CAE) integrated with deep fully connected layers (DFC) and robust wrapper feature selection. However, the online measurements of TN and TP are difficult due to the high costs, uncertainties, and the time lag associated with real-time monitoring. Monitoring total nitrogen (TN) and total phosphorus (TP) concentrations in rivers is essential to assess the water quality and control the eutrophication risk. ![]()
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