Redai dili (Jun 2024)
Multi-Source Data-Driven Modeling of Typhoon Dynamic Risk Assessment
Abstract
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.
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