# Learning-Assisted Rain Attenuation Prediction Models
## 3 Taxonomy  
The LARA models are somehow different compared to the traditional rain attenuation models. As the LARA models possess the property like a black-box, and physical significance represents the model input parameters to the output rain attenuation parameters, LARA models can take several input parameters. Whereas, it is complicated to handle many input parameters by an ordinary algorithm-based rain attenuation model. The LARA model can be classified in different perspectives, such as the model structure and training data set. Based on the model structure, the existing LARA model can be classified into two broad categories: artificial neural network (ANN) and machine learning approach. The ANN-based models can be further classified into two types: tailored and non-tailored model. The tailored model structure is built on a customization style whereas the non-tailored model is almost the basic structure as per its name in the literature. Figure 3 shows the LARA model's classifications based on the model structure. Within this work, we would like to define the taxonomy based on the number of parameters used by the model. In this way, we classified the model that it takes; one input parameter is considered as a one-factor model. Similarly, models that are associated with two factors are associated with a two-factors model, and input parameters having three or more models are grouped in the three-or-more-factors-model category. In this way, the complete taxonomy is presented in Figure 4. A mapping of the input parameters of the LARA models is shown in Table 2. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Ref.** & **Learning Models** & **Model Structure** \\ \hline
[4] & Single-layer feed-forward back-propagation (SL FFBP) model & Back-propagation single layer neural model with a single hidden layer. \\
[6] & Feedforward back-propagation neural network (FFBNN) & Feedforward back-propagation neural network \\
[8] & Supervised machine learning (SML) & SVM and SML-based regression algorithm that uses Gaussian process-compatible kernel functions. \\
[10] & Artificial neural network (ANN) and k-nearest neighbor (kNN) & kNN and ANN (recurrent neural network) \\
[11] & Back-propagation neural network (BPNN) & BPNN with a sigmoid input function \\
[12] & The network is the multilayer feed-forward (MFF) network; Input section: Bayesian regularization adaptive training algorithm; Activation function: Tangent sigmoid transfer functions (hidden layer) and pure linear transfer function (output layer). \\
[13] & Least squares-support vector machine (LS-SVM) and BPNN & BPNN: with M layers and N number of nodes \\
[5] & Single-layer artificial neural network (SL ANN) & LS-SVM: Using LS-SVM to develop a function that can predict attenuation with an unseen rain rate. SL ANN: It is a single hidden layer network. \\
[14] & In situ learning algorithm (ILA) and adaptive artificial neural network (AANN) & Activation function: hyperbolic-tangent activation function (hidden layer with 5–9 nodes), and linear activation function (output node) \\
[15] & Regression analysis (RA)* & Regression analysis \\ \hline \hline \end{tabular}  
* This technique determines specific attenuation coefficient (k and \(\alpha\)) rather than predicting the attenuation; consequently, we have limited its use in the later section.  
\end{table} 
Table 1: Learning algorithms used in currently published LARA model’s rain attenuation model. Figure 4: LARA model’s taxonomy based on the number of input parameters. Figure 3: LARA model’s taxonomy based on the model structure or formation.