Accurate prediction of total nitrogen (TN) concentration in dam reservoirs is recognized as one of the significant challenges in water resources management.
The results of this study suggest that the proposed framework has a significant advantage over conventional models, as the analysis of the indices indicated that, with each layer of complexity added to the model, prediction accuracy improved significantly.
This significant improvement in accuracy demonstrated that the intelligent combination of data preprocessing, an attention mechanism, and hyperparameter optimization can yield models with greater reliability in water quality prediction.
The use of interaction terms such as turbidity × pH corrected the systematic bias of the model, which is hydrologically justified.
In summary, the proposed framework shows promise as an accurate prediction tool and provides a useful analytical basis for better understanding water quality dynamics in the Mahabad reservoir, subject to further validation with larger datasets.
Accurate prediction of total nitrogen (TN) concentration in dam reservoirs is recognized as one of the significant challenges in water resources management. In this regard, the present study, to develop an intelligent and robust modeling framework, introduced the integrated model, which consists of the combination of the improved variational mode decomposition algorithm (IVMD), the attention mechanism-based temporal convolutional neural network (AttTCN), the bidirectional gated recurrent unit (BiGRU), and the grasshopper optimization algorithm (GOA), which is used to predict the total nitrogen of Mahabad Dam. The results of this study suggest that the proposed framework has a significant advantage over conventional models, as the analysis of the indices indicated that, with each layer of complexity added to the model, prediction accuracy improved significantly. The 80% reduction in the mean absolute error value from the basic BiGRU model to the final IVMD-GOA-AttTCN-BiGRU model, along with the improvement of Willmott’s index of agreement from 0.75 to 0.99, confirms the superiority of the proposed integrated approach. This significant improvement in accuracy demonstrated that the intelligent combination of data preprocessing, an attention mechanism, and hyperparameter optimization can yield models with greater reliability in water quality prediction. The use of interaction terms such as turbidity × pH corrected the systematic bias of the model, which is hydrologically justified. In summary, the proposed framework shows promise as an accurate prediction tool and provides a useful analytical basis for better understanding water quality dynamics in the Mahabad reservoir, subject to further validation with larger datasets.
