摘要
灾害动态风险评估是灾前风险管理的重要依据。文章基于2009—2022影响中国东南5省的108个台风案例近4000个县级灾情,融合30类多源风险要素指标,建立台风动态风险评估样本库,并利用随机森林算法建立了6个台风灾害风险评估模型,用于灾害事件发生前对受灾人口、紧急转移安置人口、农作物受灾面积、倒塌和严重损坏房屋、直接经济损失等风险等级以及综合风险等级进行评估。通过实际灾情与模型结果进行验证,灾害风险评估结果准确率整体达到80%以上,表明该模型具有较好的泛化能力,可用于实际灾害评估工作。实验对比发现,训练样本量提高1~2个数量级能使模型评估准确率提升3%~14%,表明灾害风险大数据的积累对灾害风险评估研究具有重要意义。
The dynamic risk assessment of typhoon disasters is an important decision-making basis for disaster response in the event of a major typhoon.Its goal is to dynamically predict the expected loss and disaster risk level caused by a typhoon so as to provide a basis for disaster risk early warning and emergency response.The traditional risk assessment model mainly fits the vulnerability curves of the hazard-affected bodies using historical disaster losses,and then establishes a disaster risk assessment model by coupling the risk of disaster factors,exposure,and vulnerability.However,the vulnerability curves generated by this method have problems of regional applicability,particularly in small-scale regions with small sample sizes available for fitting,leading to insufficient generalizability of the model.In addition,such models are complex and require phased hazard and vulnerability of the hazard-affected bodies research.Moreover,when employing the 3-element coupling process,it is difficult to consider other risk factors in the disaster system,such as hazard-formative environment and disaster prevention and mitigation capability.With the development of information technology,the availability of disaster risk factor data has been significantly improved,affording conditions for the fusion and application of disaster risk multi-source data.In recent years,many data-driven machine-learning models have been used to establish disaster risk assessment models.These models have the advantage that they can use large sample to improve the adaptability of the model,whereby the modeling process can consider more risk factors,the concepts of hazard and vulnerability are diluted,and the steps of model building are simplified.The integrated learning algorithm can not only improve the prediction accuracy,but more importantly,can be used to effectively evaluate the contribution value of the index to the final evaluation result.At present,China has established a six-level disaster reporting system at the national,provincial,municipal,county,township,and village levels,forming a long-term,high-precision database of disaster event cases since 2009,providing rich disaster loss information for the data fusion of risk elements.This study was based on 108 typhoon cases affecting five provinces in southeast China during 2009-2022.Nearly 4,000 county-level typhoon disaster loss samples were used to establish a dynamic typhoon risk assessment sample database that integrates 30 types of multi-source risk factor indicators.Six typhoon disaster risk assessment models were developed using the random forest algorithm to evaluate the affected population,emergency relocation population,crop-affected areas,collapsed and severely damaged houses,direct economic losses,and comprehensive risk level.Through the verification of actual disaster situations and model results,the overall accuracy of the disaster risk assessment results was found to be greater than 80%,indicating that the model has good generalizability and can be used for actual disaster assessment work.The experimental comparison shows that increasing the training sample size by 1-2 orders of magnitude can improve the accuracy of the model assessment by 3%-14%,indicating that the accumulation of disaster risk big data is of great significance in the study of disaster risk assessment.This study is expected to constitute a scientific reference for the quantitative analysis of the multiple impact factors of typhoon disaster damage and explore technical ideas for the application of disaster big data in risk management.
作者
刘蓓蓓
赵飞
王曦
闫雪
林森
Liu Beibei;Zhao Fei;Wang Xi;Yan Xue;Lin Sen(National Disaster Reduction Center of the Emergency Management Department,Beijing 100124,China)
出处
《热带地理》
CSCD
北大核心
2024年第6期1102-1112,共11页
Tropical Geography
基金
国家自然科学基金委员会面上项目(42271089)。
关键词
台风
灾害风险评估
灾害大数据
数据融合
评估指标
随机森林
台风“暹芭”
typhoon
disaster risk assessment
disaster big data
data fusion
assessment index system
random forests
Typhoon Chaba
