## 3 Results
### Multi-Source Data Fusion for Early Warning  
Luochongwei Station of Guangzhou Metro Line 13 is used as the research object. A risk fusion early-warning study of multiple data sources based on machine learning and improved cloud D-S fusion models is performed. The frequency of data collection during pit excavation is once a day. The measured values are used in the deformation prediction model. The predicted values for a day after excavation are used as an example to illustrate the early-warning process of pit collapse risk fusion.  
1. [label=(0)]
2. Delineate evaluation intervals and determine cloud model parameters.  
Following the interval classification in Table 4, the standard cloud parameters (Table 5) and corresponding cloud diagrams (Figure 7) are generated via Equation (1). Figure 7 demonstrates minimal intersection among rubric categories, confirming their suitability for multi-source data conversion.  
(2) BPA generation for different data sources  
A total of six monitoring points are selected in this paper. Each monitoring point has three primary indicators (surface settlement, groundwater level, and pile horizontal displacement), each of which corresponds to two secondary indicators (cumulative value and rate of change). The set of early-warning levels is denoted as {safe, yellow early-warning, orange early-warning, alarm, \(\Theta\)}, where \(\Theta\) denotes the uncertainty about the global. That is, there is no prejudgment in the early-warning level pool to match to the unknown level.  
The BPA generation process can be described as follows: According to Equation (1), the set of rubrics is converted into cloudy numerical features (Table 5), then the machine learning prediction results are substituted into Equation (2), then the numerical data is converted into affiliation, and finally the BPA is calculated. The input is the predicted value of the GPR model and the output is the BPA. Due to space constraints, only the BPA generated from the predicted values on day 21 is shown in the paper, and the results are shown in Table 6.  
Step (1). The predicted values for day 21 are obtained from the GPR (5,3) model trained above. For example, at monitoring point C1, the predicted result for the cumulative value of surface settlement is 29 mm.  
Step (2). The cloud digital features (Table 5) can be obtained through Equation (1). Also, since \(Enn\) obeys \(NORMINV\) (\(p\), \(En\), 0.001) where \(p\) is rand (0,1) and \(En\) takes 0.001, \(Enn\) can be obtained as [0.279, 0.045, 0.045, 0.236].  
Step (3). The predicted value of 29 \(mm\) is converted to \(k\) of 0.967. Substituting k into Equation (2), the degree of affiliation can be calculated. And BPA is [0.056, 0.0, 0.424, 0.23, 0.289].  
In the process of multi-source information fusion, the information sources need to be converted into BPAs for early-warning levels. The cloud model combines fuzzy mathematics and probability theory to map quantitative data to probabilistic values, which has a huge advantage in describing the uncertainty in the BPA construction process. Thus,  
Figure 7: Standard cloud for accuracy of target recognition. Green represents the cloud model corresponding to safe. Yellow represents the cloud model corresponding to the yellow early-warning. Orange represents the cloud model corresponding to the orange early-warning. Red represents the cloud model corresponding to the red early-warning.  
\begin{table}
\begin{tabular}{c c} \hline
**Evaluation Intervals** & **Cloud Numerical Characteristics (\(Ex\), \(En\), \(He\))** \\ \hline
[0, 0.7] & (0.35, 0.10, 0.001) \\
[0.7, 0.85] & (0.775, 0.02, 0.001) \\
[0.85, 1] & (0.925, 0.02, 0.001) \\
[1, 1.6] & (1.3, 0.10, 0.001) \\ \hline \end{tabular}
\end{table}
Table 5: Cloud numerical characteristics.  
the cloud model outputs probability values for early-warning levels that can be used for subsequent multi-data fusion analyses.