## 4 Results and Analysis
### KAN-BiLSTM Model Evaluation  
Five models--RNN, GRU, LSTM, BiLSTM, and KAN-BiLSTM--were employed to predict moisture and to compare their performance. Prior to training, the data were normalized to a range of 0 to 1 to optimize the neural network's performance. The dataset was split into training and test sets at a ratio of 70:30, with 30% data further reserved for model validation. The hyperparameter settings for each model are presented in Table 8. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Hyperparameter** & **RNN** & **GRU** & **LSTM** & **BiLSTM** & **KAN-BiLSTM** \\ \hline Input & 19 & 19 & 19 & 19 \\ Epochs & 50 & 30 & 20 & 15 & 10 \\ Batch size & 16 & 16 & 16 & 16 & 32 \\ Loss function & MSE & MSE & MSE & MSE & MSE \\ Optimizer & Adam & Adam & Adam & Adam \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Hyperparameters of different models. Figure 13: (a) Experimental process diagram; (b) UNet; (c) VGG-UNet; (d) VGG-UNet (pre-train). Table 9 summarizes the accuracy metrics of various models on the moisture sequence test dataset. The results demonstrate that the vanilla RNN yielded an RMSE of 0.1023, an MAE of 0.0687, and an R\({}^{2}\) of 0.8791, with a relatively long runtime and low efficiency. While GRU significantly improved training speed, the improvements in RMSE, MAE, and R\({}^{2}\) were modest. Overall, the deep learning models equipped with memory mechanisms exhibited markedly superior prediction accuracy. In particular, the BiLSTM model achieved the best performance, with the RMSE decreasing from 0.1023 (RNN) to 0.0679, the MAE reaching a minimum of 0.0404, and the R\({}^{2}\) rising to 0.9452. Table 9 also compares the performance of overall and sub-regional predictions within the same model, revealing that sub-regional prediction further enhances moisture content estimation. The incorporation of regional data significantly improves prediction accuracy, demonstrating a positive impact on moisture content forecasting. Figure 14 compares the actual values and predictions for test data using the four architectures (RNN, GRU, LSTM, and BiLSTM), with and without regional data. Each comparison diagram displays seven change curves that start simultaneously with the same initial moisture ratio. Among these, the red curves represent the true test data, while the blue curves correspond to the predicted values obtained from the model. The results indicate that the RNN-based model was less effective for long sequence-dependent tasks, with significant errors observed during periods of minimal moisture change. Models such as GRU and LSTM, which incorporate gating and memory mechanisms, better capture the dependence of moisture ratio on additional features, though some discrepancies remain during the critical period of weak moisture variation. The BiLSTM model, leveraging bidirectional processing, more comprehensively interprets the input data. For example, Figure 14d,h illustrate that incorporating regional data results in predictions that closely align with actual values, especially during phases of subtle moisture change, thereby confirming the beneficial impact of regional data on moisture content prediction. Figure 15 illustrates the error probability distribution curves for the different models. These curves indicate that the BiLSTM model exhibits superior performance compared to the other architectures, as its error distribution is highly concentrated around zero. This concentration implies that the BiLSTM model delivers exceptionally reliable and accurate moisture content predictions throughout the drying process. Consequently, BiLSTM was chosen as the foundational structure for moisture prediction. In the proposed architecture, a KAN layer serves as the feature extraction module supplying input to the BiLSTM, and an MHA mechanism is incorporated between the BiLSTM and the fully connected layers. Ultimately, these components form the final KAN-BiLSTM model.