## 3 Experiments and Analysis
### Performance Testing and Comparative Analysis  
To predict the capability level of agencies that haven't undergone evaluation, this paper's developed model requires training using meteorological disaster emergency agencies' capability data. Data acquisition stems from three primary sources: the government agency information publication website, the third-party capability information query platform, and records from the government oversight process. The capability evaluation results for the  
Figure 4: Modeling process of weather disaster emergency response capacity evaluation model. meteorological disaster emergency response agencies were sourced from relevant websites, using the data collection methodology described in a previous paper. These findings were cross-referenced with the capability characteristic data gathered earlier to filter out agencies with numerous missing indicators or without any capability levels. Figure 5 visualizes these results. Figure 5 vividly illustrates the capability levels of meteorological disaster emergency agencies from 2010 to 2017, as determined by different evaluations. Using a color-coded bar graph representation, the varying capability levels, categorized as AAA, AA, AA, A, BBB, and CCC, are distinctly showcased. A cursory look at the graph reveals that 2016 witnessed a significant surge in agencies achieving the 'AAA' rating, indicating a pinnacle in capability and preparedness. Conversely, 'CCC'-rated agencies, symbolizing lower capabilities, remained relatively stable and low across the years. The spikes and troughs observed across the years provide a clear insight into the evolving competency of these agencies over time. This data visualization is pivotal for comprehending the shifts in agency capabilities and for ensuring continuous improvements in the face of meteorological disasters. Before 2015, few departments undertook capability evaluations, and even fewer received ratings below an 'A'. The trends suggest that earlier capability rating systems and technical means were somewhat rudimentary, resulting in many meteorological departments receiving inflated capability scores [32]. However, as the capability system evolved over the years, there's been an increase in agencies receiving \"BBB\" and \"CCC\" ratings. This trend signifies effective government oversight while also pointing to gaps in meteorological disaster defense, which pose challenges for emergency management and decision-making. Figure 5 further highlights the ongoing disparities in capability levels. The bar colors represent different ratings, with blue indicating 'AAA', orange 'AA', gray 'A', yellow 'BBB', and light blue 'CCC'. The 4565 competency evaluations collated were grouped based on their respective institutions, yielding a total of 2384 institutions with time series evaluation results \(y_{t}\). Together with the competency feature extraction results outlined earlier, they form the sample set. Data collected in this paper, chiefly from the main capability information platforms, encompass capability characteristic data, behavior timing information, subject capability level data, and evaluation data. Combined, these elements form a comprehensive time series sample. Table 3 showcases statistics for various capability features after data pre-processing. The capability rating model crafted in this research screens out samples with incomplete or missing data, necessitating that each time series sample offers extensive capability features and maintains at least two records of capability ratings [33]. Figure 5: Annual distribution of competence levels.