An AI‑Based Method for Estimating the Potential Runout Distance of Post‑Seismic Debris Flows  

作　　者：Chenchen Qiu Lijun Su Congchao Bian Bo Zhao Xueyu Geng 

机构地区：[1]School of Engineering,University of Warwick,Coventry CV47AL,UK [2]Key Laboratory of Mountain Hazards and Earth Surface Process/Institute of Mountain Hazards and Environment(IMHE),Chinese Academy of Sciences(CAS),Chengdu 610041,China [3]School of Computer and Information,Hohai University,Nanjing 211100,China

出　　处：《International Journal of Disaster Risk Science》2024年第4期608-621,共14页国际灾害风险科学学报（英文版）

基　　金：supported by the European Union’s Horizon 2020 research and innovation program Marie Skłodowska-Curie Actions Research and Innovation Staf Exchange(RISE)(Grant No.778360);the National Natural Science Foundation of China(Grant No.U22A20603);the Science and Technology Development Fund(Grant No.001/2024/SKL);the State Key Laboratory of Internet of Things for Smart City(University of Macao)(Ref.No.SKL-IoTSC(UM)-2024-2026/ORP/GA09/2023).

摘　　要：The widely distributed sediments following an earthquake presents a continuous threat to local residential areas and infrastructure. These materials become more easily mobilized due to reduced rainfall thresholds. Before establishing an effective management plan for debris flow hazards, it is crucial to determine the potential reach of these sediments. In this study, a deep learning-based method-Dual Attention Network(DAN)-was developed to predict the runout distance of potential debris flows after the 2022 Luding Earthquake, taking into account the topography and precipitation conditions. Given that the availability of reliable precipitation data remains a challenge, attributable to the scarcity of rain gauge stations and the relatively coarse resolution of satellite-based observations, our approach involved three key steps. First, we employed the DAN model to refine the Global Precipitation Measurement(GPM) data, enhancing its spatial and temporal resolution. This refinement was achieved by leveraging the correlation between precipitation and regional environment factors(REVs) at a seasonal scale. Second, the downscaled GPM underwent calibration using observations from rain gauge stations. Third,mean absolute error(MAE), mean square error(MSE), and root mean square error(RMSE) were employed to evaluate the performance of both the downscaling and calibration processes. Then the calibrated precipitation, catchment area, channel length, average channel gradient, and sediment volume were selected to develop a prediction model based on debris flows following the Wenchuan Earthquake. This model was applied to estimate the runout distance of potential debris flows after the Luding Earthquake. The results show that:(1) The calibrated GPM achieves an average MAE of 1.56 mm, surpassing the MAEs of original GPM(4.25 mm) and downscaled GPM(3.83 mm);(2) The developed prediction model reduces the prediction error by 40 m in comparison to an empirical equation;(3) The potential runout distance of debris flows after the Luding

关 键 词：Deep learning GPM downscaling Post-seismic debris fows Runout distance prediction 2022 Luding earthquake 

分 类 号：P642.23[天文地球—工程地质学] P315.9[天文地球—地质矿产勘探]