Statistical survey on flood disasters based on large language model

Abstract

[Objective] The statistical survey on flood disasters is an important basis for regional disaster prevention, reduction and flood risk management under changing environmental conditions. The large language model(LLM) has shown potential in the field of hydraulic engineering. In order to utilize the capabilities of LLMs on semantic understanding and information extraction, the LLM-based method of statistical survey on flood disasters is proposed. All procedures of the statistical survey based on Internet data are completed by the LLM. [Methods] For the flood disasters on 21 July 2012 in Beijing(“7·21”), July 2023 in Beijing(“23·7”), on June 2022 in Pearl River Basin(“22·6”) and April 2024 in Southern China(“24·4”), the proposed LLM-based method is used to investigate the risk and loss. [Results] The accuracy of the statistical survey is the highest when the temperature of the LLM is 0 and decreases as the temperature increases. For the “7·21” flood disaster, the accuracy of the number of collapsed dwellings, the affected population and the direct economic losses is more than 90%. The proportion of no retrieved result of affected cropland area and peak discharge is more than 40%. For the “23·7” flood disaster, the accuracy is generally higher. The accuracy of the number of collapsed dwellings, the number of deaths and missing persons, the affected population, the affected cropland area and the peak discharge is more than 90%. For the “24·6” flood disaster in the Beijiang river basin, the accuracy of the maximum hourly precipitation, the average precipitation and the peak discharge is 83%, 61% and more than 90%. For the “22·4” flood disaster, the accuracy of the peak discharge at Shijiao station is 89%. the proportion of no retrieved result of the peak discharge at Feilaixia reservoir, the maximum hourly precipitation and the average precipitation exceeds 70%. [Conclusion] The LLM is suitable for the statistical survey of flood disasters and can provide data support for the management of flood and drought disasters.

Keywords

flood disaster / statistical survey / large language model / Internet data / information extraction / climate change / risk assessment / rainfall