# A Review on Rainfall Measurement Based on Commercial Microwave Links in Wireless Cellular Networks
## 5 Machine Learning for CML-Based Rainfall Measurement
### Application of Machine Learning  
Machine learning focuses on finding patterns in data and uses these patterns to make predictions [78; 79; 80]. Table 3 lists the machine learning algorithms applied in microwave-link-based rainfall measurement during recent years. For wet/dry classification, [PERSON] et al. [81] analyzed the variation of RSL data of C-band microwave links under the conditions of no rain, drizzle, light rain, and moderate rain; then the LSTM network was used to analyze the RSL data in different time scales to distinguish the rainfall period. [PERSON] et al. [82] established a CNN-based model by using the instantaneous RSL data collected from 3904 CMLs in Germany for wet/dry classification. Four months of data from 800 randomly selected CMLs are used to train, and two different months of data, one for all CMLs and one for the 3104 CMLs not included in the training, are used for model verification. Radar data adjusted by RGs are used as the reference method. The results show that the designed CNN model is superior to the current state-of-the-art model, which uses the rolling standard deviation of the CML signal as the detection criterion. \begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Ref.** & **Algorithms** & **Function** & **Data Source** & **Data for Training and Testing** & **Results** \\ \hline
[81] & LSTM & Wet/dry classification & Experimental data were collected from 1/11–31/12 except for 13/12–21/12 by using a C-band microwave link (7.7 GHz) & Data from 1/11–31/12 except for 7 GHz \\ \hline
[57] & SVM & Wet/dry classification & 15 microwave links (15–23 GHz) and 8 RGs & Half of the data from rainfall time over 2 h in 14 days were used as the training set and the remaining half as the test set. & The accuracy of wait/dry classification is higher than 60\%, and even higher than 98\% in some days. \\ \hline
[82] & CNN & Wet/dry classification & Data came from 3904 CMLs, and gauge-adjusted radar data are used as a reference & 4 months of data from 800 randomly selected CMLs were used for training and 2 different months of data for testing. & 76\% of rainfall and 9\% of non-rainfall periods can be detected, and more than 90\% of rainfall intensities that are greater than 0.6 mm\({}^{-1}\) can be detected. \\ \hline
[83] & LSTM & WAA quantization & Total attenuation data of 6 E-band full-duplex CMLs and 4 RGs data & Rain period data were divided into 12 subsets, of which 10 subsets were training sets and the remaining two for testing. & It has a good correlation with the RGs measured WAA, but the cumulative rainfall estimates based on LSTM are lower when the rainfall increases sharply. \\ \hline
[84] & LSTM & Rain rate estimation & A CML (22.715 GHz) and an OTT PARSIVEL disdrometer & The training group accounts for 80\% of the whole sequence, and the remaining 20\% is used as testing group. & The relative bias decreases from 7.39\% to 1.14\%, and the coefficient of determination (R\({}^{2}\)) increases from 0.71 to 0.82 compared with constant weighted average method. \\ \hline \end{tabular}
\end{table} 
Table 3: Machine learning algorithms applied in CML-based rainfall measurements. For WAA quantization, [PERSON] et al. [83] obtained the WAA values based on LSTM, which has a good correlation with the measured WAA, and the performance of the model in the 72.75 GHz link is better than that in the 82.75 GHz link. For accurate rain rate calculation, [PERSON] et al. [84] designed and trained a DL model by using RGs data and applied it to CML-based rainfall measurement, and the result is better than the constant weighted average method. [PERSON] et al. [85] used the measurement report (MR) data in a time-division-long-term-evolution (TD-LTE) network to retrieve rainfall, and support vector classification (SVC) and artificial neural network (ANN) were used to distinguish wet/dry weather and estimate rain rate, respectively. [PERSON] et al.