## 4 Conclusions  
Assessing the vulnerability of the power system after an earthquake often begins with examining its components, such as high-voltage substations, transmission towers, and power plants. In the modeling process, it is necessary to calculate parameters like peak ground acceleration, peak ground displacement, field motion spectrum energy, etc., to establish a ground motion mechanism model [33; 34; 35]. This model is then integrated with the vulnerability of each power system component to predict the damage state of each element. This process may necessitate a significant amount of equipment test data and historical earthquake disaster scenario records. For instance, the LADWP recovery model requires 600 initial damage and system function scenarios as inputs to evaluate and restore the power system [36; 37]. Our cost savings stem from approaching this problem from a different angle, specifically examining the correlation between night light and the power system. In the research of disaster and luminous remote sensing, case studies occupy the majority, and the research is less focused on the topic of power systems. We compared the proposed model with a few relevant studies. [PERSON] et al. conducted a study on selected disaster cases to assess the impact on urban of natural disasters [4], and the best result was 78%. [PERSON] et al. proposed a method to measure the impact of earthquakes on urban brightness loss [38], which was applied to three earthquakes with magnitude 8.0 in Chile. The best result is 68.4% accuracy. The final accuracy of the model proposed in this study is 87.9%, so significant progress has been made in using luminous remote sensing to assess disaster losses. Figure 9: (a) PSVE map; the size of the buffer zone on the map is directly related to the earthquake’s magnitude, and the color mapping represents the post-earthquake light attenuation in the city. (b) Post-earthquake power system damage statistics of prefecture-level cities from 2013 to 2023. The y axis represents different prefecture-level cities, and the x axis represents the damage degree after standardization. In this study, samples of urban NTL attenuation after earthquakes based on all available NPP-VIIRS data in GEE from 2013 to 2022 were extracted, which were taken directly from city and town NTL, and the high correlation with predictors was verified in the GAM. By using the random forest method to train a large number of sample data and tuning hyperparameters, a PSVE model with high robustness was finally obtained. While PSVE may not provide all the details, and the proposed model is not intended to replace rigorous industry-specific application analysis of the power system, it can still highlight uneven damage to power systems caused by earthquakes based on night lights. Most importantly, the predictors are very carefully selected. The predictors which can be obtained immediately when the earthquake occurs, such as magnitude, depth, distance, etc., are chosen. The whole point is to make sure that the robustness and accuracy of the model on the premise can be applied to an emergency evaluation. The damage caused to the power system was investigated from both micro and macro spatio-temporal perspectives; the innovative value of this study is, firstly, that the prediction of the light attenuation value of the whole city after the earthquake by simple parameters is conducive to the rapid preliminary insight of the disaster damage. Secondly, on a large time scale, detailed investigation may require a lot of resources and time to fit data and build models [39]. PSVE can provide decision support under limited resources. Before conducting a more detailed investigation, the prediction of simple parameters can be preliminarily screened to determine which areas or time periods may have problems, such as cascading losses caused by earthquakes [40], so as to conduct more targeted follow-up detailed investigations.