# Learning-Assisted Rain Attenuation Prediction Models
## 5 Comparison of LARA Models of Rain Attenuation
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Learning Model** & **Contribution** & **Optimization Technique** & **Optimization** & **Performance Criteria** & **Remarks** \\ \hline \hline \multirow{4}{*}{Single-layer FFBP [4]} & To develop a model that can predict & & & & \\  & attenuation from an unevenly & & & & The cumulative distance from \\  & distributed dataset with the aid of & & & & the center of each vector is \\  & divisive hierarchical clustering & & & & minimized. \\  & (DHC) algorithm. & & & & \\ \hline \multirow{4}{*}{FFRNN [6]} & This developed model can predict & & & & \\  & the strain attenuation in South Africa & & & & \\  & most accurately compared with the & & & & \\  & ITU-R, SAM model for different & & & & \\  & percent of time & & & & \\  & and 0.01). & & & & \\ \hline \multirow{4}{*}{SML [8]} & Develop an SML based rain & Apply hyper-parameters & & & \\  & attenuation model (with an & as a result of & & \\  & estimated based on the ITU-R & & & & \\  & model [22] & marginal likelihood & & & \\  & & & & \\ \hline \multirow{4}{*}{kNN+ANN [10]} & This model uses the rainfall map & & & & \\  & data along with the KNN and ANN & & & & \\  & approach. According to their result, & & & & \\  & when the training dataset is large & & & & \\  & & & & \\  & accurate than the ANN method. & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\ \hline \multirow{4}{*}{BPNN [11]} & Develop a BPNN based model that & Obtained optimized & For regression coefficient & & \\  & synaptic weights in & \(R^{2}=0.9109\) the actual & & & \\  & training period through & output and predicted & & \\  & gradient descent & output almost aggress & & \\  & & & & \\ \end{tabular}
\end{table} 
Table 4: Contributions, optimization, and evaluation criteria of LARA models. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Learning Model** & **Contribution** & **Optimization Technique** & **Optimization** & **Performance Criteria** & **Remarks** \\ \hline \multirow{4}{*}{FFMLP* supervised learning [12]} & The model agrees with the experimental attenuation with the rain rate for a rain rate up to 80 & Back-propagation algorithm-based delta rule. & \(1.62\times 10^{-4}\) for vertical polarization (\(\text{R}^{2}=0.9943\) & percentage of relative mean square error & Complete model \\  & & predicted attenuation is higher compared to the experimental attenuation. & rule. & with error \(4.677\times 10^{-3}\) for horizontal polarization) & \\ \hline \multirow{4}{*}{LS-SVM+BPNN [13]} & BPNN outperforms ITU-R in terms of accuracy and stability. & Multilayer-feed forward algorithm-based delta rule is used to optimize the node weights. & BPNN: pre-determined threshold LS-SVM+BPNN [13] & mean absolute error (MAE), maximum error (MAX), and mean square error (MSE) & Complete model \\ \cline{2-6}  & Develop a rain attenuation model composed of six input parameters single layer ANN network to predict the rain attenuation. & Gradient descent method & Not reported & Mean (\%), RMS (\%), and STD (\%) error experimented on the DBG3 plus tropical database & \\ \hline \multirow{4}{*}{AANN+ILA [14]} & Develop a rain attenuation prediction technique using a neural attenuation algorithm where measured attenuation is used to develop the model. & The LMS algorithm minimizes an instantaneous estimate of the overall cost function and tapped delay filter predicted the local minimization & A more accurate prediction is confirmed by reducing the observation interval. & Relative prediction error can be analyzed to get smoothed attenuation prediction. & Complete model (from data + short term) data + short term) \\ \hline RA [15] & Develop and train k and a values prediction for a frequency & unavailable & Not mentioned & Performance not validated & ITU-R companion model \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._