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Artificial Neural Networks and Grey Systems for the Prediction of Slope Stability

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  • P. Lu
  • M. Rosenbaum

Abstract

The interactions between factors that affect slope instability are complex, multi-factorial, and often difficult to describe mathematically, imposing a challenge for prediction using traditional methods. The power of the ANN and Grey Systems approaches lies in employing the behaviour of the system rather than knowledge of explicit relations. Published data has been used to illustrate the application of these techniques to predicting the state of slope stability. This has been developed into a tool for analysing and predicting future ground movement based on geotechnical properties and historical behaviour. Copyright Kluwer Academic Publishers 2003

Suggested Citation

  • 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.
    • Handle: RePEc:spr:nathaz:v:30:y:2003:i:3:p:383-398
    DOI: 10.1023/B:NHAZ.0000007168.00673.27
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    1. Shahram Kaboodvandpour & Jamil Amanollahi & Samira Qhavami & Bakhtiyar Mohammadi, 2015. "Assessing the accuracy of multiple regressions, ANFIS, and ANN models in predicting dust storm occurrences in Sanandaj, 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. 78(2), pages 879-893, September.
    2. Sina Shaffiee Haghshenas & Sami Shaffiee Haghshenas & Zong Woo Geem & Tae-Hyung Kim & Reza Mikaeil & Luigi Pugliese & Antonello Troncone, 2021. "Application of Harmony Search Algorithm to Slope Stability Analysis," Land, MDPI, vol. 10(11), pages 1-12, November.
    3. 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.
    4. 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.
    5. Sami Ullah & Muhib Ullah Khan & Gohar Rehman, 2020. "A Brief Review of the Slope Stability Analysis Methods," Geological Behavior (GBR), Zibeline International Publishing, vol. 4(2), pages 73-77:4, May.
    6. 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.
    7. 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.
    8. 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.
    9. Chonghao Zhu & Jianjing Zhang & Yang Liu & Donghua Ma & Mengfang Li & Bo Xiang, 2020. "Comparison of GA-BP and PSO-BP neural network models with initial BP model for rainfall-induced landslides risk assessment in regional scale: a case study in Sichuan, China," 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. 100(1), pages 173-204, January.
    10. Arsalan Mahmoodzadeh & Mokhtar Mohammadi & Hunar Farid Hama Ali & Hawkar Hashim Ibrahim & Sazan Nariman Abdulhamid & Hamid Reza Nejati, 2022. "Prediction of safety factors for slope stability: comparison of machine learning 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. 111(2), pages 1771-1799, March.
    11. Zaiwu Gong & Caiqin Chen & Xinming Ge, 2014. "Risk prediction of low temperature in Nanjing city based on grey weighted Markov model," 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. 71(2), pages 1159-1180, March.
    12. 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.
    13. Zaobao Liu & Jianfu Shao & Weiya Xu & Hongjie Chen & Yu Zhang, 2014. "An extreme learning machine approach for slope stability evaluation and prediction," 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. 73(2), pages 787-804, September.
    14. Wei-ping Lou & Hai-yan Chen & Xin-fa Qiu & Qi-yi Tang & Feng Zheng, 2012. "Assessment of economic losses from tropical cyclone disasters based on PCA-BP," 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. 60(3), pages 819-829, February.
    15. 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.
    16. Chong Xu & Xiwei Xu & Fuchu Dai & Zhide Wu & Honglin He & Feng Shi & Xiyan Wu & Suning Xu, 2013. "Application of an incomplete landslide inventory, logistic regression model and its validation for landslide susceptibility mapping related to the May 12, 2008 Wenchuan earthquake of China," 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. 68(2), pages 883-900, September.
    17. 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.
    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. 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.

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