Volume 15 Issue 4
Aug.  2024
Turn off MathJax
Article Contents
Chenchen Qiu, Lijun Su, Congchao Bian, Bo Zhao, Xueyu Geng. An AI-Based Method for Estimating the Potential Runout Distance of Post-Seismic Debris Flows[J]. International Journal of Disaster Risk Science, 2024, 15(4): 608-621. doi: 10.1007/s13753-024-00575-5
Citation: Chenchen Qiu, Lijun Su, Congchao Bian, Bo Zhao, Xueyu Geng. An AI-Based Method for Estimating the Potential Runout Distance of Post-Seismic Debris Flows[J]. International Journal of Disaster Risk Science, 2024, 15(4): 608-621. doi: 10.1007/s13753-024-00575-5

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

doi: 10.1007/s13753-024-00575-5
Funds:

the Science and Technology Development Fund (Grant No. 001/2024/SKL)

odowska-Curie Actions Research and Innovation Staff Exchange (RISE) (Grant No. 778360)

This work was financially supported by the European Union’s Horizon 2020 research and innovation program Marie Skł

the National Natural Science Foundation of China (Grant No. U22A20603)

and the State Key Laboratory of Internet of Things for Smart City (University of Macau) (Ref. No. SKL-IoTSC(UM)-2024-2026/ORP/GA09/2023).

  • Accepted Date: 2024-07-23
  • Available Online: 2024-10-26
  • Publish Date: 2024-08-29
  • 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 Earthquake reaches 0.77 km when intraday rainfall is 100 mm, while the minimum distance value is only 0.06 km. Overall, the developed model offers a scientific support for decision makers in taking reasonable measurements for loss reduction caused by post-seismic debris flows.
  • loading
  • [1]
    Bergen, K.J., P.A. Johnson, M.V. de Hoop, and G.C. Beroza. 2019. Machine learning for data-driven discovery in solid Earth geoscience. Science 363(6433): Article eaau0323.
    [2]
    Brouder, S.M., B.S. Hofmann, and D.K. Morris. 2005. Mapping soil pH: Accuracy of common soil sampling strategies and estimation techniques. Soil Science Society of America Journal 69(2): 427-442.
    [3]
    Chai, T., and R.R. Draxler. 2014. Root mean square error (RMSE) or mean absolute error (MAE). Geoscientific Model Development Discussions 7(1): 1525-1534.
    [4]
    Cheema, M.J.M., and W.G.M. Bastiaanssen. 2012. Local calibration of remotely sensed rainfall from the TRMM satellite for different periods and spatial scales in the Indus Basin. International Journal of Remote Sensing 33(8): 2603-2627.
    [5]
    Chen, W., and Y. Li. 2020. GIS-based evaluation of landslide susceptibility using hybrid computational intelligence models. Catena 195: Article 104777.
    [6]
    Chen, F.R., Y.Q. Gao, Y.G. Wang, and X. Li. 2020. A downscaling-merging method for high-resolution daily precipitation estimation. Journal of Hydrology 581: Article 124414.
    [7]
    Crozier, M.J., and R.J. Eyles. 1980. Assessing the probability of rapid mass movement. In Proceedings of the 3rd Australia-New Zealand Conference on Geomechanics, ed. Technical groups, 2-47. Wellington, NZ: Institution of Professional Engineers New Zealand
    [8]
    Dahlquist, M.P., and A.J. West. 2019. Initiation and runout of post-seismic debris flows: Insights from the 2015 Gorkha Earthquake. Geophysical Research Letters 46(16): 9658-9668.
    [9]
    Falconi, L.M., L. Moretti, C. Puglisi, and G. Righini. 2023. Debris and mud flows runout assessment: A comparison among empirical geometric equations in the Giampilieri and Briga basins (east Sicily, Italy) affected by the event of October 1, 2009. Natural Hazards 117(3): 2347-2373.
    [10]
    Fan, L.F., P. Lehmann, B. McArdell, and D. Or. 2017. Linking rainfall-induced landslides with debris flows runout patterns towards catchment scale hazard assessment. Geomorphology 280: 1-15.
    [11]
    Gao, J.M., and Y.H. Sang. 2017. Identification and estimation of landslide-debris flow disaster risk in primary and middle school campuses in a mountainous area of Southwest China. International Journal of Disaster Risk Reduction 25: 60-71.
    [12]
    Glade, T., M. Crozier, and P. Smith. 2000. Applying probability determination to refine landslide-triggering rainfall thresholds using an empirical “Antecedent Daily Rainfall Model”. Pure and Applied Geophysics 157: 1059-1079.
    [13]
    Guo, X.J., P. Cui, and Y. Li. 2013. Debris flow warning threshold based on antecedent rainfall: A case study in Jiangjia Ravine, Yunnan, China. Journal of Mountain Science 10: 305-314.
    [14]
    Guo, C.B., Y.S. Zhang, D.R. Montgomery, Y. Du, G.Z. Zhang, and S.H. Wang. 2016. How unusual is the long-runout of the earthquake-triggered giant Luanshibao landslide, Tibetan Plateau, China?. Geomorphology 259: 145-154.
    [15]
    de Haas, T., and A.L. Densmore. 2019. Debris-flow volume quantile prediction from catchment morphometry. Geology 47(8): 791-794.
    [16]
    Harris, R., A. Singleton, D. Grose, C. Brunsdon, and P. Longley. 2010. Grid-enabling geographically weighted regression: A case study of participation in higher education in England. Transactions in GIS 14(1): 43-61.
    [17]
    Hodson, T.O., T.M. Over, and S.S. Foks. 2021. Mean squared error, deconstructed. Journal of Advances in Modeling Earth Systems 13(12): Article e2021MS002681.
    [18]
    Horton, A.J., T.C. Hales, C.J. Ouyang, and X.M. Fan. 2019. Identifying post-earthquake debris flow hazard using mass flow. Engineering Geology 258: Article 105134.
    [19]
    Hürlimann, M., R. Copons, and J. Altimir. 2006. Detailed debris flow hazard assessment in Andorra: A multidisciplinary approach. Geomorphology 78(3-4): 359-372.
    [20]
    Hürlimann, M., D. Rickenmann, V. Medina, and A. Bateman. 2008. Evaluation of approaches to calculate debris-flow parameters for hazard assessment. Engineering Geology 102(3-4): 152-163.
    [21]
    Iverson, R.M. 2014. Debris flows: Behaviour and hazard assessment. Geology Today 30(1): 15-20.
    [22]
    Iverson, R.M., M.E. Reid, and R.G. LaHusen. 1997. Debris-flow mobilization from landslides. Annual Review of Earth and Planetary Sciences 25(1): 85-138.
    [23]
    LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. Nature 521(7533): 436-444.
    [24]
    Lee, M. 2023. Mathematical analysis and performance evaluation of the GELU activation function in deep learning. Journal of Mathematics 2023: Article 4229924.
    [25]
    Legros, F. 2002. The mobility of long-runout landslides. Engineering Geology 63: 301-331.
    [26]
    Ma, Z.J., and G. Mei. 2021. Deep learning for geological hazards analysis: Data, models, applications, and opportunities. Earth-Science Reviews 223: Article 103858.
    [27]
    Mahmud, M.R., M. Hashim, and M.N.M. Reba. 2017. How effective is the new generation of GPM satellite precipitation in characterizing the rainfall variability over Malaysia?. Asia-Pacific Journal of Atmospheric Sciences 53: 375-384.
    [28]
    Marc, O., N. Hovius, P. Meunier, T. Gorum, and T. Uchida. 2016. A seismologically consistent expression for the total area and volume of earthquake-triggered landsliding. Journal of Geophysical Research: Earth Surface 121(4): 640-663.
    [29]
    Parker, R.N., A.L. Densmore, N.J. Rosser, M. De Michele, Y. Li, R.Q. Huang, S. Whadcoat, and D.N. Petley. 2011. Mass wasting triggered by the 2008 Wenchuan Earthquake is greater than orogenic growth. Nature Geoscience 4(7): 449-452.
    [30]
    Puglisi, C., L. Falconi, C. Gioè, and G. Leoni. 2015. Contribution to the runout evaluation of potential debris flows in Peloritani Mountains (Messina, Italy). In Engineering geology for society and territory—Volume 2: Landslide processes, ed. G. Lollino, D. Giordan, G.B. Crosta, J. Corominas, R. Azzam, J. Wasowski, and N. Sciarra, 509-513. Cham: Springer.
    [31]
    Qiu, C.C., L.J. Su, Q. Zou, and X.Y. Geng. 2022. A hybrid machine-learning model to map glacier-related debris flow susceptibility along Gyirong Zangbo watershed under the changing climate. Science of the Total Environment 818: Article 151752.
    [32]
    Reichstein, M., G. Camps-Valls, B. Stevens, M. Jung, J. Denzler, N. Carvalhais, and F. Prabhat. 2019. Deep learning and process understanding for data-driven Earth system science. Nature 566(7743): 195-204.
    [33]
    Rickenmann, D. 1999. Empirical relationships for debris flows. Natural Hazards 19(1): 47-77.
    [34]
    Sachindra, D.A., F. Huang, A. Barton, and B.J.C. Perera. 2013. Least square support vector and multi-linear regression for statistically downscaling general circulation model outputs to catchment streamflows. International Journal of Climatology 33(5): 1087-1106.
    [35]
    Shieh, C.L., Y.S. Chen, Y.J. Tsai, and J.H. Wu. 2009. Variability in rainfall threshold for debris flow after the Chi-Chi earthquake in central Taiwan China. International Journal of Sediment Research 24(2): 177-188.
    [36]
    Tang, C., T.W.J. Van Asch, M. Chang, G.Q. Chen, X.H. Zhao, and X.C. Huang. 2012. Catastrophic debris flows on 13 August 2010 in the Qingping area, southwestern China: The combined effects of a strong earthquake and subsequent rainstorms. Geomorphology 139-140: 559-576.
    [37]
    Tang, C., J. Zhu, M. Chang, J. Ding, and X. Qi. 2012. An empirical-statistical model for predicting debris-flow runout zones in the Wenchuan Earthquake area. Quaternary International 250: 63-73.
    [38]
    Tang, C., J. Zhu, J. Ding, X.F. Cui, L. Chen, and J.S. Zhang. 2011. Catastrophic debris flows triggered by a 14 August 2010 rainfall at the epicenter of the Wenchuan Earthquake. Landslides 8: 485-497.
    [39]
    Tang, C., J. Zhu, W.L. Li, and J.T. Liang. 2009. Rainfall-triggered debris flows following the Wenchuan Earthquake. Bulletin of Engineering Geology and the Environment 68: 187-194.
    [40]
    Tanyaş, H., D. Kirschbaum, T. Görüm, C.J. van Westen, C.X. Tang, and L. Lombardo. 2021. A closer look at factors governing landslide recovery time in post-seismic periods. Geomorphology 391: Article 107912.
    [41]
    Trenberth, K.E., and D.J. Shea. 2005. Relationships between precipitation and surface temperature. Geophysical Research Letters 32: 1-4.
    [42]
    Vegliante, G., V. Baiocchi, L.M. Falconi, L. Moretti, M. Pollino, C. Puglisi, and G. Righini. 2024. A GIS-based approach for shallow landslides risk assessment in the Giampilieri and Briga catchments areas (Sicily, Italy). GeoHazards 5(1): 209-232.
    [43]
    Wang, H., F. Zang, C.Y. Zhao, and C.L. Liu. 2022. A GWR downscaling method to reconstruct high-resolution precipitation dataset based on GSMaP-Gauge data: A case study in the Qilian Mountains, Northwest China. Science of the Total Environment 810: Article 152066.
    [44]
    Wilford, D.J., M.E. Sakals, J.L. Innes, R.C. Sidle, and W.A. Bergerud. 2004. Recognition of debris flow, debris flood and flood hazard through watershed morphometrics. Landslides 1: 61-66.
    [45]
    Xiao, Z.K., C. Xu, Y.D. Huang, X.L. He, X.Y. Shao, Z.N. Chen, C.C. Xie, T. Li, and X.W. Xu. 2023. Analysis of spatial distribution of landslides triggered by the Ms 6.8 Luding Earthquake in China on September 5, 2022. Geoenvironmental Disasters 10(1): 1-15.
    [46]
    Xiong, J., H.Y. Chen, L. Zeng, F.H. Su, L.F. Gong, and C.X. Tang. 2023. Coseismic landslide sediment increased by the “9.5” Luding Earthquake, Sichuan China. Journal of Mountain Science 20(3): 624-636.
    [47]
    Yao, J.Q., Q. Yang, W.Y. Mao, Y. Zhao, and X.B. Xu. 2016. Precipitation trend-elevation relationship in arid regions of the China. Global and Planetary Change 143: 1-9.
    [48]
    Zhang, J.Q., Z.J. Yang, Q.K. Meng, J. Wang, K.H. Hu, Y.G. Ge, F.H. Su, and B. Zhao et al. 2023. Distribution patterns of landslides triggered by the 2022 Ms 6.8 Luding Earthquake, Sichuan China. Journal of Mountain Science 20(3): 607-623.
    [49]
    Zhang, S., L.M. Zhang, H.X. Chen, Q. Yuan, and H. Pan. 2013. Changes in runout distances of debris flows over time in the Wenchuan Earthquake zone. Journal of Mountain Science 10: 281-292.
    [50]
    Zhou, W., J.Y. Fang, C. Tang, and G.Y. Yang. 2019. Empirical relationships for the estimation of debris flow runout distances on depositional fans in the Wenchuan Earthquake zone. Journal of Hydrology 577: Article 123932.
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Article Metrics

    Article views (6) PDF downloads(0) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return