## 4 Experiments  
### Data Collection and Preprocessing  
Data collection and preprocessing are the initial stages of the data mining process. Because only valid data will produce accurate output, data preprocessing is the key stage. For this study, we use the one-year ground-based meteorological data from Nanjing Station (ID: [58238]). The dataset contains atmospheric pressure, sea level pressure, wind direction, wind speed, relative humidity and precipitation. Data is collected every 3 h. We just consider related information and ignore the rest. The method of principal component analysis (PCA) is used to reduce the dimensionality of the data, thus reducing the data processing time and improving the efficiency of the algorithm. We performed data transformation on rainfall.  
By observing the original data set, we find the incorrect data in the data set, which does not correspond to the fact. For example, if the pressure value of a sample is 999,017 or 999,999 and it appears repeatedly, it indicates that such erroneous data is abnormal data returned when data is acquired, so we reject samples containing such data. The error data is the data returned when the abnormality is collected. Therefore, we exclude the samples containing such data and check other elements until there is no data value that exceeds the normal threshold.  
As shown in Table 1, the first line is the attributes of the original meteorological data set; PRS represents atmospheric pressure, PRS_Sea is the sea level pressure, WIN_D is the wind direction, WIN_S is the wind speed, TEM is the temperature, RHU is the relative humidity, and PRE_1h is the hourly precipitation.  
The statistical performance of the classification model can be evaluated by comparing the labeled data sets. The label attribute stores the actual observed values, and the prediction attribute stores the values of labels predicted by the classification model. For the assessment of the algorithms' results, calibration, cross-validation and external testing were carried out. For each one of the experiments, its datasets were divided into two subsets, training and test, comprising 80% and 20% of the original samples, respectively. Thus, we randomly classify datasets into training sets and test sets.  
1. Training set--take 80% of the samples randomly from the dataset as the training set.
2. Text set--for the text data set, we used other data remaining in the data set, which contained all the attributes except the rainfall data that the model is supposed to predict. The test set was never used for the training of any of the models.  
In both two sets, the attributes and their types remained the same.  
While the data are classified according to different amounts of rainfall, the data is divided into 0 categories when the data of the rainfall is 0, and the rainfall of data is non-0 divided into one category. There is a total of 2214 pieces of valid data, of which 1974 of data belong to 0 categories, accounting for 90% of the total data, 240 pieces of data belong to one category, accounting for 10% of the total data.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**PRS (hPa)** & **PRS\_Sea (hPa)** & **WIN\_D (\({}^{\circ}\))** & **WIN\_S (0.1 m/s)** & **TEM (\({}^{\circ}\)C)** & **RHU (1\%)** & **PRE\_1h (mm)** \\ \hline
1031.2 & 1035.8 & 89 & 2.5 & 77 & 2 & 0 \\
1030.8 & 1035.4 & 113 & 2.9 & 61 & 6.4 & 0 \\
1027.3 & 1031.9 & 153 & 2.1 & 49 & 8.3 & 0 \\
1026.2 & 1030.8 & 122 & 2 & 55 & 7.1 & 0 \\ � � & � & � & � & � & � \\
1027.1 & 1031.7 & 121 & 0.7 & 71 & 4.1 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Original meteorological data.