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Rapid prediction of landslide dam stability considering the missing data using XGBoost algorithm

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Abstract

The stability prediction of landslide dams is the basis for reducing and even eliminating the damage caused by landslide dam failures. However, the stability of landslide dams with missing data cannot be predicted by the existing rapid evaluation methods. A landslide dam database containing 2783 historical cases from 49 countries worldwide is created. Considering 8 variables and the missing data, the XGBoost model for predicting the stability of landslide dams is established, and the impact of missing data on the prediction accuracy of the model is also discussed. The XGBoost prediction model can not only predict the landslide dam cases with missing data, but the accuracy of the XGBoost model has also been improved compared with that of the widely used rapid evaluation methods. The characteristic analysis framework of landslide dam stability prediction is proposed based on SHapley Additive exPlanations attribution theory, and the results show the importance of each characteristic and its influence on landslide dam stability from both overall and individual perspectives. The Meilonggou, Usoi, and Mount St. Helens landslide dams are introduced to demonstrate the applicability and accuracy of the XGBoost prediction model proposed in this study.

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Data availability

The electronic supplementary file containing 2783 landslide dam cases is available to authorized users.

References

  • Canuti P, Casagli N, Ermini L (1998) Inventory of landslide dams in the Northern Apennine as a model for induced flood hazard forecasting. Managing Hydro-Geological Disasters in a Vulnerable Environment. CNR-GNDCI and UNESCO IHP, Perugia, 189–202

  • Casagli N, Ermini L (1999) Geomorphic analysis of landslide dams in the Northern Apennine. Trans Jpn Geomorphol 20(3):219–249

    Google Scholar 

  • Chen TQ, Guestrin C (2016) XGBoost: a scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, USA. ACM, pp 785–794

  • Costa JE, Schuster RL (1988) The formation and failure of natural dam. Geol Soc Am Bull 100:1054–1068

    Article  Google Scholar 

  • Costa JE, Schuster RL (1991) Documented historical landslide dams from around the world. U.S. Geological Survey open-file report 91–239, Washington, USA

  • Dong JJ, Li YS, Kuo CY, Sung RT, Li MH, Lee CT, Chen CC, Lee WR (2011a) The formation and breach of a short-lived landslide dam at Hsiaolin Village, Taiwan-part I: post-event reconstruction of dam geometry. Eng Geol 123(1–2):40–59

    Article  Google Scholar 

  • Dong JJ, Tung YH, Chen CC, Liao JJ, Pan YW (2011b) Logistic regression model for predicting the failure probability of a landslide dam. Eng Geol 117(1–2):52–61

    Article  Google Scholar 

  • Ermini L, Casagli N (2003) Prediction of the behavior of landslide dams using a geomorphological dimensionless index. Earth Surf Process Landf 28(1):31–47

    Article  Google Scholar 

  • Fan XM, Dufresne A, Subramanian SS et al (2020) The formation and impact of landslide dams-state of the art. Earth Sci Rev. https://doi.org/10.1016/j.earscirev.2020.103116

    Article  Google Scholar 

  • Fielding AH, Bell JF (1997) A review of methods for the assessment of prediction errors in conservation presence/absence models. Environ Conserv 24(1):38–49. https://doi.org/10.1017/s0376892997000088

    Article  Google Scholar 

  • Hu KH, Zhang XP, Luo H, Liu BT, Chen HY (2020) Investigation on “6.17” debris flow disaster chain in Meilonggou, Danba County. J Mt Sci 38(06):945–951 (in Chinese)

    Google Scholar 

  • Hussain MA, Chen ZL, Kalsoom I, Asghar A, Shoaib M (2022) Landslide susceptibility mapping using machine learning algorithm: a case study along Karakoram Highway (KKH). J Indian Soc Remote Sens, Pakistan. https://doi.org/10.1007/s12524-021-01451-1

    Book  Google Scholar 

  • Iqbal J, Dai FC, Xu L, Tu XB (2013) Characteristics of large-sized landslide dams around the world. EGU General Assembly Conference Abstracts, p 15

  • Korup O (2004) Geomorphometric characteristics of New Zealand landslide dams. Eng Geol 73(1–2):13–35

    Article  Google Scholar 

  • Lehre AK, Collins B, Dunne T (1982) Preliminary post-eruption sediment budget for the North Fork Toutle River drainage, June 1980-May 1981. Mount St Helens, Washington

    Google Scholar 

  • Liang WZ, Luo SZ, Zhao GY, Wu H (2020) Predicting hard rock pillar stability using GBDT, XGBoost, and LightGBM algorithms. Mathematics 8(5):765. https://doi.org/10.3390/math8050765

    Article  Google Scholar 

  • Liu N, Cheng ZL, Cui P, Chen NS (2013) Dammed lake and risk management. Science Press, Beijing (in Chinese)

    Google Scholar 

  • Liu N, Yang QG, Chen ZY (2016) Hazard mitigation for barrier lakes. Changjiang River Publication, China (in Chinese)

    Google Scholar 

  • Lundberg SM, Lee SI (2017) A unified approach to interpreting model predictions. Conference on Neural Information Processing Systems, pp 4765–4774

  • Marmion M, Hjort J, Thuiller W, Luoto M (2008) A comparison of predictive methods in modelling the distribution of periglacial landforms in Finnish Lapland. Earth Surf Process Landf 33(14):2241–2254

    Article  Google Scholar 

  • Mcdonald JA, Pedersen J (1984) Alternative strategies for a permanent outlet for spirit lake near Mount St. Helens, Washington

    Google Scholar 

  • Peng M, Zhang LM (2012a) Analysis of human risks due to dam break floods-part 2: application to Tangjiashan landslide dam failure. Nat Hazards 64(2):1899–1923

    Article  Google Scholar 

  • Peng M, Zhang LM (2012b) Breaching parameters of landslide dams. Landslides 9:13–31

    Article  Google Scholar 

  • Schuster RL (2004) Usoi landslide dam and lake Sarez, Pamir mountains, Tajikistan. Environ Eng Geosci 10(2):151–168

    Article  Google Scholar 

  • Shan YB, Chen SS, Zhong QM (2020) Rapid prediction of landslide dam stability using the logistic regression method. Landslides. https://doi.org/10.1007/s10346-020-01414-6

    Article  Google Scholar 

  • Shen DY, Shi ZM, Peng M, Zhang LM, Jiang MZ (2020) Longevity analysis of landslide dams. Landslides 17(8):1797–1821. https://doi.org/10.1007/s10346-020-01386-7

    Article  Google Scholar 

  • Shi ZM, Ma XL, Peng M, Zhang LM (2014) Statistical analysis and efficient dam burst modelling of landslide dams based on a large-scale database. Chin J Rock Mech Eng 33(9):1780–1790 (in Chinese)

    Google Scholar 

  • Swanson FJ, Graham RL, Grant GE (1985) Some effects of slope movements on river channel. Proceedings of the International Symposium on Erosion, Debris Flow and Disaster Prevention, Tsukuba, Japan, pp 273–27

  • Swanson FJ, Oyagi N, Tominaga M (1986) Landslide dams in Japan. Landslide dams: processes, risk and mitigation. Geotech Special Publication (ASCE) 3:131–145

    Google Scholar 

  • Swets J (1988) Measuring the accuracy of diagnostic systems. Science 240(4857):1285–1293

    Article  Google Scholar 

  • Tacconi Stefanelli C, Catani F, Casagli N (2015) Geomorphological investigations on landslide dams. Geoenviron Disasters 2:21

    Article  Google Scholar 

  • Tacconi Stefanelli C, Segoni S, Casagli N, Catani F (2016) Geomorphic indexing of landslide dams evolution. Eng Geol 208:1–10

    Article  Google Scholar 

  • Tong YX (2008) Quantitative analysis for stability of landslide dam. Master Thesis, National Central University, Taiwan (in Chinese)

  • Ullah B, Kamran M, Rui YC (2022) Predictive Modeling of short-term rockburst for the stability of subsurface structures using machine learning approaches: t-SNE, K-means clustering, and XGBoost. Mathematics 10(3):449. https://doi.org/10.3390/math10030449

    Article  Google Scholar 

  • Zheng HC, Shi ZM, Shen DY, Peng M, Hanley KJ, Ma CY, Zhang LM (2021) Recent advances in stability and failure mechanisms of landslide dams. Front Earth Sci 9:201. https://doi.org/10.3389/feart.2021.659935

    Article  Google Scholar 

  • Zhong QM, Chen SS, Mei SA, Cao W (2018) Numerical simulation of landslide dam breaching due to overtopping. Landslides 16(6):1183–1192

    Article  Google Scholar 

Download references

Funding

This work is supported by the National Science Fund for Distinguished Young Scholars (grant number 52125904), the National Natural Science Foundation of China (grant number 51979224), and the Program 2022TD-01 for Shaanxi Provincial Innovative Research Team (grant number 2022TD-01).

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Authors and Affiliations

  1. State Key Laboratory of Eco-hydraulics in Northwest Arid Region of China, Xi’an University of Technology, Xi’an, 710048, China

    Ning Shi, Yanlong Li, Lifeng Wen & Ye Zhang

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  1. Ning Shi
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  2. Yanlong Li
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  3. Lifeng Wen
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  4. Ye Zhang
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Correspondence to Yanlong Li.

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Shi, N., Li, Y., Wen, L. et al. Rapid prediction of landslide dam stability considering the missing data using XGBoost algorithm. Landslides 19, 2951–2963 (2022). https://doi.org/10.1007/s10346-022-01947-y

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