A Cost-Effective Earthquake Disaster Assessment Model for Power Systems Based on Nighttime Light Information

Abstract

The power system is one of the most important urban lifeline engineering systems. Identifying the damage to the power system is an important task in earthquake disaster assessments. Considering the importance of timeliness and accessibility, a hyperparameter optimization model is proposed to address the assessment of disaster losses in power systems on earthquakes. The power system vulnerability on earthquakes, PSVE, is assessed by the hyperparameter optimization model based on nighttime light information. Through the utilization of the computational resources provided by Google Earth Engine, the accuracy of the baseline model has been significantly improved to 87.9%; meanwhile, the cost-effectiveness in the evaluation process is maintained. The PSVE-based damage evaluation has the potential to aid in assessing earthquake damage to cities’ energy supply, power infrastructure, and lighting. Furthermore, the PSVE-based damage evaluation can provide valuable guidance for prioritizing and efficiently allocating resources for rapid repair and reconstruction efforts.

Keywords

• earthquake disaster assessment
• power system vulnerability
• machine learning
• nighttime light information
• GEE