# Learning-Assisted Rain Attenuation Prediction Models
## 4 Insights of LARA Models
### LARA Model with Three or More Factors  
[PERSON] et al. [8] developed this model based on the SML algorithm applied to the ITU database. If the available data set is classified as discrete data, SML is used, whereas, for continuous data, the regression SML model is used. The theory of SML is combined with Gaussian processes (GPs) for regression, called GPR, to create a new approach for forecasting rain attenuation. This model was validated using experimental data from the ITU-R database for the line-of-sight (LOS) terrestrial link. To find out the rain attenuation in a specific geographical area, climatic zones or frequency bands need to train the algorithm to calculate the different interdependence of various parameters. The model is then able to predict the attenuation in that area of interest. Hence, this model, like other machine  
Figure 6: Adaptive ANN to predict short-term rain attenuation. learning models, cannot be applied directly in a climatic area, except before training with the local data set. **Advantages:** It is the first machine learning-based model without geographical boundness. **Disadvantages:** The tropical behaviors of attenuation have not been tested yet using this model[PERSON]  
**Future improvement:** For the rare climatic condition, it needs to train the algorithm with the specific dataset. **Application Area:** To predict rain attenuation on a terrestrial link with the millimeter frequency component. [PERSON] et al. [6] introduced the [PERSON] of artificial neural networks (ANNs) as a strategy for calculating rain-induced attenuation along with higher frequency satellite connections in South Africa. The proposed ANN model suggested an improved estimation efficiency in terms of the root mean square error and correlation coefficient. The model's five factors are considered as the input (rain intensity, the angle of elevation, the percentage of the time, frequency, a latitude), and the output is the rain attenuation. The weights are modified using the gradient descent method to keep the error in the output to a minimum. **Advantages:** The model considers five input parameters set to determine the rain attenuation. **Disadvantages:** To use this model, it needs a 5 min rainfall rate. Currently, numerous rain attenuation databases with a 1 min rain rate are normally available. **Future improvement:** Develop the same model for a 1 min rain rate. **Application area:** The model can predict rain attenuation of the 12 GHz frequency component on the earth-space link. [PERSON] et al. [5] proposed a model ANN network to predict the earth-space rain attenuation model. In the model, they used six parameters as input parameters of the ANN network, and attenuation is the output. Throughout this paper, the authors emphasized the significance of a big database of differences throughout various parameters. They consider an ANN model consists of six input nodes, a hidden layer (an unknown number of nodes), and the output layer. **Advantages:** A very simple ANN network can outperform the existing model. **Disadvantages:** Needs a huge task to prepare a database compatible with the model. **Future improvement:** The model's consistency of feature ideas can be tested for other factors to examine any consistency anomalies. **Application area:** The model can be used to predict rain attenuation prediction in the earth-space link. [PERSON] et al. [4] suggested a rain attenuation prediction model based on FFBP for earth-space links. Further, they used the anomaly of feeding features into the ANN network if low statistical data exists, and also proposed a mechanism to process low statistical data for feeding to the ANN network.