## 5 Experiments and Discussion
### Results  
We conducted tests to determine the optimal hyperparameter settings for training our RMSDC model. Our experiments found that using a hidden state dimension of 32 for both the encoder and decoder consistently produced superior results (m = p for simplicity). We employed the Adam optimizer with a batch size 256 and a learning rate of 0.011. To  
Figure 8: Training and loss validation for Convolutional LSTM-based model. Figure 7: Testing and forecasting for Convolutional LSTM-based day. prevent overfitting, a dropout layer with a rate of 0.3 was added after each LSTM layer and each model was trained for 60 epochs. For the observed friction value, water, air temperature, and sensors dataset, we performed trials to optimize the dimension reduction of the context vectors to q = 4 and set the input sequence length to Tx = 5. With these settings, the RMSDC model has approximately 24,833 trainable parameters. We utilized three metrics during the evaluation: mean absolute error (MAE), root-mean-square error (RMSE), and average. The models were trained using a NVIDIA Titan RTX GPU to ensure efficient processing. The empirical results for the dataset consisting of pollutants, building and friction value, water, air temperature, and sensors are presented in Tables 2 and 3. These tables provide an overview of the performance of each model, including their respective training time per epoch, which indicates the time required to train the model once on the entire training set. Two essential indicators are used for performance evaluation: root-mean-square error (RMSE) and mean absolute difference. Our real-world analyses repeatedly show that RMSDC outperforms other techniques, with a 0.26 reduction in RMSE. The quantitative findings highlight RMSDC's effectiveness in mitigating the effects of climate change on road networks. RMSDC improves traffic safety while simultaneously lowering costs and improving environmental sustainability by implementing proactive road repair measures. Furthermore, in developing countries with limited resources, climate adaptation of road networks is crucial, making RMSDC a sensible and practical solution for resilient road maintenance systems. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Performance** & & **RMSE** & **MAE** & **Average** & **time** \\ \hline \multirow{2}{*}{LSTM} & Training & \(4.04\times 10^{0}\) & \(5.43\times 10^{0}\) & \(7.78\times 10^{0}\) & 6.2 \\  & Testing & \(3.44\times 10^{0}\) & \(4.53\times 10^{0}\) & \(6.99\times 10^{0}\) & 6.3 \\ \hline \multirow{2}{*}{RNN} & Training & \(0.9216\times 10^{0}\) & \(1.8921\times 10^{0}\) & \(3.81\times 10^{0}\) & 2.5 \\  & Testing & \(0.8124\times 10^{0}\) & \(1.223\times 10^{0}\) & \(3.01\times 10^{0}\) & 2.3 \\ \hline \multirow{2}{*}{CNN} & Training & \(6.94\times 10^{0}\) & \(7.43\times 10^{0}\) & \(8.78\times 10^{0}\) & 1.9 \\  & Testing & \(5.83\times 10^{0}\) & \(6.95\times 10^{0}\) & \(9.88\times 10^{0}\) & 1.8 \\ \hline \multirow{2}{*}{
\begin{tabular}{c} CONV- \\ LSTM \\ \end{tabular} } & Training & \(1.52\times 10^{-1}\) & \(0.43\times 10^{0}\) & \(1.65\times 10^{0}\) & 1.5 \\  & Testing & \(1.31\times 10^{-1}\) & \(2.12\times 10^{-1}\) & \(5.27\times 10^{-1}\) & 1.6 \\ \hline \multirow{2}{*}{RMSDC} & Training & \(0.0814\times 10^{-1}\) & \(0.1721\times 10^{-1}\) & \(0.9410\times 10^{-1}\) & 0.98 \\  & Testing & \(0.8813\times 10^{-1}\) & \(0.1913\times 10^{-1}\) & \(0.8812\times 10^{-1}\) & 0.99 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Statistical error parameters of the proposed RMSDC technique for road maintenance based on the climate change in training and testing datasets at (Tx = 24, Ty = 4).