IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i14p3071-d1192157.html
   My bibliography  Save this article

Slope Stability Prediction Using k -NN-Based Optimum-Path Forest Approach

Author

Listed:
  • Leilei Liu

    (School of Resources and Safety Engineering, Central South University, Changsha 410083, China)

  • Guoyan Zhao

    (School of Resources and Safety Engineering, Central South University, Changsha 410083, China)

  • Weizhang Liang

    (School of Resources and Safety Engineering, Central South University, Changsha 410083, China)

Abstract

Slope instability can lead to catastrophic consequences. However, predicting slope stability effectively is still challenging because of the complex mechanisms and multiple influencing factors. In recent years, machine learning (ML) has received great attention in slope stability prediction due to its strong nonlinear prediction ability. In this study, an optimum-path forest algorithm based on k-nearest neighbor (OPF k -NN ) was used to predict the stability of slopes. First, 404 historical slopes with failure risk were collected. Subsequently, the dataset was used to train and test the algorithm based on randomly divided training and test sets, respectively. The hyperparameter values were tuned by combining ten-fold cross-validation and grid search methods. Finally, the performance of the proposed approach was evaluated based on accuracy, F 1 -score, area under the curve (AUC), and computational burden. In addition, the prediction results were compared with the other six ML algorithms. The results showed that the OPF k -NN algorithm had a better performance, and the values of accuracy, F 1 -score, AUC, and computational burden were 0.901, 0.902, 0.901, and 0.957 s, respectively. Moreover, the failed slope cases can be accurately identified, which is highly critical in slope stability prediction. The slope angle had the most important influence on prediction results. Furthermore, the engineering application results showed that the overall predictive performance of the OPF k -NN model was consistent with the factor of safety value of engineering slopes. This study can provide valuable guidance for slope stability analysis and risk management.

Suggested Citation

  • Leilei Liu & Guoyan Zhao & Weizhang Liang, 2023. "Slope Stability Prediction Using k -NN-Based Optimum-Path Forest Approach," Mathematics, MDPI, vol. 11(14), pages 1-31, July.
  • Handle: RePEc:gam:jmathe:v:11:y:2023:i:14:p:3071-:d:1192157
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/14/3071/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/11/14/3071/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Maria Marsella & Peppe D’Aranno & Silvia Scifoni & Alberico Sonnessa & Marco Corsetti, 2015. "Terrestrial laser scanning survey in support of unstable slopes analysis: the case of Vulcano Island (Italy)," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 78(1), pages 443-459, August.
    2. Weizhang Liang & Suizhi Luo & Guoyan Zhao & Hao Wu, 2020. "Predicting Hard Rock Pillar Stability Using GBDT, XGBoost, and LightGBM Algorithms," Mathematics, MDPI, vol. 8(5), pages 1-17, May.
    3. Weizhang Liang & Asli Sari & Guoyan Zhao & Stephen D. McKinnon & Hao Wu, 2020. "Short-term rockburst risk prediction using ensemble learning methods," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 104(2), pages 1923-1946, November.
    4. P. Lu & M. Rosenbaum, 2003. "Artificial Neural Networks and Grey Systems for the Prediction of Slope Stability," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 30(3), pages 383-398, November.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Guoyan Zhao & Meng Wang & Weizhang Liang, 2022. "A Comparative Study of SSA-BPNN, SSA-ENN, and SSA-SVR Models for Predicting the Thickness of an Excavation Damaged Zone around the Roadway in Rock," Mathematics, MDPI, vol. 10(8), pages 1-26, April.
    2. Arunava Ray & Vikash Kumar & Amit Kumar & Rajesh Rai & Manoj Khandelwal & T. N. Singh, 2020. "Stability prediction of Himalayan residual soil slope using artificial neural network," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 103(3), pages 3523-3540, September.
    3. Ying Chen & Qi Da & Weizhang Liang & Peng Xiao & Bing Dai & Guoyan Zhao, 2022. "Bagged Ensemble of Gaussian Process Classifiers for Assessing Rockburst Damage Potential with an Imbalanced Dataset," Mathematics, MDPI, vol. 10(18), pages 1-22, September.
    4. Yu, Ruyang & Zhang, Kai & Ramasubramanian, Brindha & Jiang, Shu & Ramakrishna, Seeram & Tang, Yuhang, 2024. "Ensemble learning for predicting average thermal extraction load of a hydrothermal geothermal field: A case study in Guanzhong Basin, China," Energy, Elsevier, vol. 296(C).
    5. Xiuzhen Li & Jiming Kong & Zhenyu Wang, 2012. "Landslide displacement prediction based on combining method with optimal weight," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 61(2), pages 635-646, March.
    6. Xizi Wang & Yakun Ma & Guangwei Hu, 2024. "Mobile Platforms as the Alleged Culprit for Work–Life Imbalance: A Data-Driven Method Using Co-Occurrence Network and Explainable AI Framework," Sustainability, MDPI, vol. 16(18), pages 1-22, September.
    7. Min-Yuan Cheng & Nhat-Duc Hoang, 2015. "Typhoon-induced slope collapse assessment using a novel bee colony optimized support vector classifier," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 78(3), pages 1961-1978, September.
    8. Liulei Bao & Guangcheng Zhang & Xinli Hu & Shuangshuang Wu & Xiangdong Liu, 2021. "Stage Division of Landslide Deformation and Prediction of Critical Sliding Based on Inverse Logistic Function," Energies, MDPI, vol. 14(4), pages 1-24, February.
    9. He Jia & Sherong Zhang & Chao Wang & Xiaohua Wang & Zhonggang Ma & Yaosheng Tan, 2023. "MSC-1DCNN-based homogeneous slope stability state prediction method integrated with empirical data," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 118(1), pages 729-753, August.
    10. Jamil Amanollahi & Shahram Kaboodvandpour & Hiva Majidi, 2017. "Evaluating the accuracy of ANN and LR models to estimate the water quality in Zarivar International Wetland, Iran," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 85(3), pages 1511-1527, February.
    11. Babek Erdebilli & Burcu Devrim-İçtenbaş, 2022. "Ensemble Voting Regression Based on Machine Learning for Predicting Medical Waste: A Case from Turkey," Mathematics, MDPI, vol. 10(14), pages 1-16, July.
    12. Diyuan Li & Zida Liu & Danial Jahed Armaghani & Peng Xiao & Jian Zhou, 2022. "Novel Ensemble Tree Solution for Rockburst Prediction Using Deep Forest," Mathematics, MDPI, vol. 10(5), pages 1-23, March.
    13. Keyou Shi & Yong Liu & Weizhang Liang, 2022. "An Extended ORESTE Approach for Evaluating Rockburst Risk under Uncertain Environments," Mathematics, MDPI, vol. 10(10), pages 1-20, May.
    14. Ji, Shujuan & Wang, Xin & Lyu, Tao & Liu, Xiaojie & Wang, Yuanqing & Heinen, Eva & Sun, Zhenwei, 2022. "Understanding cycling distance according to the prediction of the XGBoost and the interpretation of SHAP: A non-linear and interaction effect analysis," Journal of Transport Geography, Elsevier, vol. 103(C).
    15. Shakti Suman & S. Z. Khan & S. K. Das & S. K. Chand, 2016. "Slope stability analysis using artificial intelligence techniques," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 84(2), pages 727-748, November.
    16. Shouye Cheng & Xin Yin & Feng Gao & Yucong Pan, 2024. "Microseismic Data-Driven Short-Term Rockburst Evaluation in Underground Engineering with Strategic Data Augmentation and Extremely Randomized Forest," Mathematics, MDPI, vol. 12(22), pages 1-19, November.
    17. Daxing Lei & Yaoping Zhang & Zhigang Lu & Hang Lin & Zheyuan Jiang, 2024. "Predicting Factor of Safety of Slope Using an Improved Support Vector Machine Regression Model," Mathematics, MDPI, vol. 12(20), pages 1-17, October.
    18. Yukun Yang & Wei Zhou & Izhar Mithal Jiskani & Xiang Lu & Zhiming Wang & Boyu Luan, 2023. "Slope Stability Prediction Method Based on Intelligent Optimization and Machine Learning Algorithms," Sustainability, MDPI, vol. 15(2), pages 1-18, January.
    19. Jie Li & Helin Fu & Kaixun Hu & Wei Chen, 2023. "Data Preprocessing and Machine Learning Modeling for Rockburst Assessment," Sustainability, MDPI, vol. 15(18), pages 1-32, September.
    20. Paraskevas Tsangaratos & Andreas Benardos, 2014. "Estimating landslide susceptibility through a artificial neural network classifier," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 74(3), pages 1489-1516, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:11:y:2023:i:14:p:3071-:d:1192157. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.