## 4 Propagation Loss Calculation Using the Improved TWPE Model
### Deep Learning Model Predicts Land-Based Ducting Propagation Using Geomorphology Data
It can be seen from Figures 29b and 30b that the prediction accuracy function of the test set of the DMLP model simulation results is mostly kept between 0.8 and 1.0, and a good prediction effect was achieved. We selected two groups of DMLP predictions with relatively good test results and compared them with the TWPE simulation results. Figures 29a and 30a show that the DMLP prediction make large errors after 80 km, and the DMLP prediction results fluctuate greatly. To improve the prediction effect of the model, we introduced the long- and short-term memory (LSTM) network model to perform deep learning modeling on the original 1244 sets of data, and used two layers of LSTM networks with 80 and 60 neural units, respectively. The LSTM has requirements for the network input dimension. It is a matrix  
Figure 28: The schematic diagram of the deep learning forward-prediction model. form of [number of samples, number of expansions in time steps, number of sample features at each time step]. To meet the requirement of this parameter dimension, a total of 1244 groups of training and testing data of the original fully-connected layer were rearranged according to the four input features, which was distance, terrain fluctuation, the real part, and imaginary part of the dielectric parameters of the corresponding land covers. Then the input parameter dimensions were converted into the three-dimensional matrix. After the deep training using LSTM network, the prediction range-dependent PLs at antenna heights of 60 and 40 m were obtained, as shown in Figures 31 and 32. The LSTM model still maintains a good prediction effect. The MSE of LSTM prediction of PLs at an antenna height of 60 m was 93.749120. Moreover, the MSE of LSTM prediction of PLs at an antenna height of 40 m was 90.245694. According to the loss value, the MSE of LSTM was smaller than that of DMLP. The prediction accuracy of LSTM remained mostly between 0.8 and 1.0. However, the shortcoming of the rapid fluctuation and the large error after 80 km of DMLP were greatly improved in LSTM. Figure 30: DMLP prediction at a height of 40 m and accuracy distribution. (**a**) PL prediction using DMLP at an antenna height of 40 m. (**b**) DMLP of PL at a height of 40 m accuracy on test samples. Figure 29: DMLP prediction at a height of 60 m and accuracy distribution. (**a**) PL Prediction using DMLP at an antenna height of 60 m. (**b**) DMLP of PL at a height of 60 m of accuracy on the test samples. The terrain and land cover datasets used in the deep learning model were randomly generated. There might have been some random terrain and LCs that did not conform to the actual continuous distribution, so the model training results have certain errors. However, it can be seen that the topography and LC types have great influence on the PL distribution over the horizon. Therefore, it is effective and efficient to use the deep learning model to predict the over-the-horizon PL of the intercity link in the ducting environment with different landforms. The results also show that topography and LC types have great influence on the over-the-horizon propagation when a terrestrial duct happens. Subsequent continuous improvements of parameters to optimize the deep learning model may improve the prediction accuracy.