# Learning-Assisted Rain Attenuation Prediction Models
## 5 Comparison of LARA Models of Rain Attenuation  
This section compares the performance of current LARA models in terms of factors/parameters/features, satellite or terrestrial connections, supporting polarization, and frequency components (Table 3). All the relevant inputs, optimization, and performance parameters of all available for LARA models are tabulated in Table 4. Figure 8 shows a radar map representing the major features of the available LARA models. As we can see, four LARA models [5; 8; 13; 15] support the polarization while the LARA model [6] includes the maximum number of parameters, which is six. The LARA model [8; 12; 15] supports the frequency range of 137, 100, and 100 GHz, respectively. Besides, the models [12; 13; 15] support both the earth-space and terrestrial links. The model-specific contributions, optimization and evaluation criteria, accuracy level, and input-output parameter correlation (R\({}^{2}\)) are compared in Tables 4 and 5, respectively. Figure 9 (Root-mean-square error (RMSE) versus model) shows that RA [15] has the least RMSE value following the minimum RMSE models, which are AANN+ILA [14] and SL FFBP [4]. Regarding model FFMLP+SL [12], we cannot get a comparative result, but following the remarks about Relative RMSE (RRMSE) accuracy [16], RRMSE = 8.5% can be considered as an excellent result, concerning the models' applicability. Figure 7: LARA model’s development timeline. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Learning Model** & **Features and Training Data Set** & **Sat.** & **Ter.** & **Pol.** & **Dataset** & **Frequency** & **Filter?** & **Data Interval** \\ \hline \multirow{2}{*}{Single-layer FFBP [4]} & rainfall intensity, latitude, & & & & & & & \\  & elevation angle, polarization, & & & & & & & \\  & station height, and frequency & & & & & & & \\ \hline \multirow{3}{*}{FFBNN [6]} & rainfall intensity, the percentage & & & & & & & \\  & of time exceedance, frequency, & & & & & & & \\  & elevation angle, rain height, and & & & & & & & \\  & azimuth & & & & & & & \\ \hline \multirow{3}{*}{SML [8]} & distance between transmitter and & & & & & & & \\  & receiver (0.5 km to 58 km), & & & & & & & \\  & frequency, rainfall intensity & & & & & & & \\  & (9 rain rate), and polarization & & & & & & \\  & (0\({}^{\circ}\) to 90\({}^{\circ}\)) & & & & & & \\ \hline \multirow{3}{*}{kNN, ANN [10]} & rainfall intensity, and frequency & & & & & & & \\  & & & & & & & \\  & & & & & & & \\  & & & & & & & \\ \hline \multirow{3}{*}{BPNN [11]} & rainfall intensity &? & \(\times\) &? & \
\begin{tabular}{c} The University of \\ Kwazulu-Natal, Durham \\ \end{tabular} &? & N & 30 s \\ \cline{1-1} \cline{2-6}  & & & & & & & & \\ \hline \multirow{3}{*}{FFMLP+ supervised learning [12]} & rainfall intensity & & & & & & \\  & learning [12] & & & & & & \\ \cline{1-1} \cline{2-6}  & & & & & & & \\ \cline{1-1} \cline{2-6}  & & & & & & & \\ \cline{1-1} \cline{2-6}  & & & & & & & \\ \hline \multirow{3}{*}{LS-SVM+BPNN [13]} & rainfall intensity, frequency, \(L\) 1, & & & & & & \\  & latitude, elevation angle, and & & & & & & \\  & azimuth & & & & & & \\ \hline \multirow{3}{*}{AANN+HLA [14]} & \multirow{3}{*}{attenuation (recorded)} & \multirow{3}{*}{�} & \multirow{3}{*}{�} &
\begin{tabular}{c} Collected dataset \\ (INRAPFL, Kolkate; IIT, \\ Kharagpur, MCF, Hassan) \\ \end{tabular} &? & Y & 5 s \\ \cline{1-1} \cline{2-6}  & & & & & & & \\ \hline \multirow{3}{*}{RA [15]} & Rainfall intensity, the distance & & & & & & \\  & between transmitter and receiver & & & & & \\ \cline{1-1} \cline{2-6}  & & & & & & \\ \hline \hline \end{tabular}  
* refer to [7] (Table I), 2 Supports, 3 Information unavailable, 4 unavailable, 5 TRMM Multi-satellite Precipitation Analysis (TMPA), near Real-Time (RT), 6 inherent with k and a (RA [15] case only).  
\end{table} 
Table 3: Few characteristics of the LARA models.