Application of incremental learning in landslide susceptibility assessment: A case study of Tianshui, Gansu Province

YAN Tianxiao; ZHANG Jiantong; ZHU Yueqin; LIU Haoran; ZHU Haomeng

doi:10.12097/gbc.2023.07.020

2024 Vol. 43, No. 4

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Article navigation > Geological Bulletin of China > 2024 > 43(4): 630-640

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YAN Tianxiao, ZHANG Jiantong, ZHU Yueqin, LIU Haoran, ZHU Haomeng. 2024. Application of incremental learning in landslide susceptibility assessment: A case study of Tianshui, Gansu Province. Geological Bulletin of China, 43(4): 630-640. doi: 10.12097/gbc.2023.07.020

Citation:

YAN Tianxiao, ZHANG Jiantong, ZHU Yueqin, LIU Haoran, ZHU Haomeng. 2024. Application of incremental learning in landslide susceptibility assessment: A case study of Tianshui, Gansu Province. Geological Bulletin of China, 43(4): 630-640. doi: 10.12097/gbc.2023.07.020

Application of incremental learning in landslide susceptibility assessment: A case study of Tianshui, Gansu Province

1.
Institute of Disaster Prevention, Langfang 065201, Heibei, China
2.
National Institute of Natural Hazards, Ministry of Emergency Management of China, Beijing 100085, China
3.
Jiaoxin Beidou Technology Company, Beijing 100011, China
4.
Zhejiang Institute of Geosciences, Hangzhou 310000, Zhejiang, China

More Information

Corresponding authors: ZHANG Jiantong, zhangjiantong@cttic.cn ; ZHU Yueqin, yueqinzhu@163.com

Received Date: 20 July 2023

Revised Date: 15 November 2023

Publish Date: 15 April 2024

Abstract

Abstract

To enhance the generalization ability of machine learning models in the assessment of landslide susceptibility, this paper takes the city of Tianshui as an example and employs an incremental learning model based on LightGBM. By utilizing the Autogluon automated machine learning framework, the model's hyperparameter optimization and mdoel stacking are implemented. Additionally, the SHAP explainable framework is used for feature selection and data anomaly analysis. By using the above methods we construct an incremental learning model suitable for landslide susceptibility assessment. Model validation using landslide disaster data collected from various regions in Tianshui city demonstrates that the incremental learning model for landslide susceptibility can effectively identify and predict landslide-prone areas. It adapts to new datasets by self-adjusting the model and improves model performance.
- suspectibility of landslide /
- machine learning /
- incremental learning /
- feature selection /
- interpretability

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References

[1]	Erickson N, Mueller J, Shirkov A, et al. 2020. Autogluon−tabular: Robust and accurate automl for structured data[J]. arXiv, preprint arXiv: 2003.06505. Google Scholar
[2]	Huang F, Ye Z, Zhou X, et al. 2022. Landslide susceptibility prediction using an incremental learning Bayesian Network model considering the continuously updated landslide inventories[J]. Bulletin of Engineering Geology and the Environment, 81(6): 250. doi: 10.1007/s10064-022-02748-2 CrossRef Google Scholar
[3]	Ke G, Meng Q, Finley T, et al. 2017. Lightgbm: A highly efficient gradient boosting decision tree[J]. Advances in Neural Information Processing Systems, 2017: 3149−3157. Google Scholar
[4]	Lundberg S M, Lee S I. 2017. A unified approach to interpreting model predictions[J]. Advances in Neural Information Processing Systems, 2017: 30. Google Scholar
[5]	Merghadi A, Yunus A P, Dou J, et al. 2020. Machine learning methods for landslide susceptibility studies: A comparative overview of algorithm performance[J]. Earth−Science Reviews, 207: 103225. doi: 10.1016/j.earscirev.2020.103225 CrossRef Google Scholar
[6]	Wu C, Jia R, Qiu T, et al. 2013. Rock burst monitoring and early warning based on incremental learning method with SVM[J]. Research Journal of Information Technology, 5(4): 121−124. doi: 10.19026/rjit.5.5797 CrossRef Google Scholar
[7]	Wu Y, Xu W, Yu Q, et al. 2019. Hierarchical Bayesian network based incremental model for flood prediction[C]//MultiMedia Modeling: 25th International Conference, MMM 2019, Thessaloniki, Greece, January 8–11, 2019, Proceedings, Part I 25. Springer International Publishing: 556−566. Google Scholar
[8]	邓念东, 李宇新, 崔阳阳, 等. 2022. 基于机器学习混合模型的滑坡易发性评价[J]. 科学技术与工程, 22(14): 5539−5547. Google Scholar
[9]	黄发明, 胡松雁, 闫学涯, 等. 2022. 基于机器学习的滑坡易发性预测建模及其主控因子识别[J]. 地质科技通报, 41(2): 79−90. Google Scholar
[10]	康孟羽, 朱月琴, 陈晨, 等. 2022. 基于多元非线性回归和BP神经网络的滑坡滑动距离预测模型研究[J]. 地质通报, 41(12): 2281−2289. Google Scholar
[11]	李世其, 段学燕, 刘燕. 2006. 一种决策树增量学习算法在故障诊断中的应用[J]. 华中科技大学学报: 自然科学版, 34(4): 79−81. Google Scholar
[12]	李挺, 洪镇南, 刘智勇, 等. 2018. 基于增量单类支持向量机的工业控制系统入侵检测[J]. 信息与控制, 47(06): 756−761. Google Scholar
[13]	刘纪平, 梁恩婕, 徐胜华, 等. 2022. 顾及样本优化选择的多核支持向量机滑坡灾害易发性分析评价[J]. 测绘学报, 51(10): 2034−2045. Google Scholar
[14]	齐娜, 胡良柏. 2022. 天水盆地黄土滑坡特征与分布规律分析[J]. 甘肃科技, 38(22): 28−33. Google Scholar
[15]	邵葆蓉, 孙即超, 朱月琴, 等. 2020. 基于多元回归的黄土滑坡滑动距离预测模型探讨——以甘肃天水地区为例[J]. 地质通报, 39(12): 1993−2003. Google Scholar
[16]	王毅, 陈曦, 唐贵希, 等. 2022. 基于自动机器学习的全球尺度滑坡灾害易发性预测[J]. 资源环境与工程, 36(5): 604−613. Google Scholar
[17]	王洪林, 董春林, 董俊, 等. 2022. 基于支持向量机增量学习算法的高压电网短路故障位置自动识别[J]. 电气自动化, 44(4): 34−36. Google Scholar
[18]	武雪玲, 沈少青, 牛瑞卿. 2016. GIS支持下应用PSO-SVM模型预测滑坡易发性[J]. 武汉大学学报(信息科学版), 41(5): 665−671. Google Scholar
[19]	严武文. 2010, 基于粗集——神经网络的区域滑坡灾害易发性预测研究[D]. 中国地质大学硕士学位论文. Google Scholar
[20]	张博, 向旭, 贾俊龙, 等. 2023. 基于LightGBM的天然气管道周围滑坡灾害预测方法[J]. 吉林大学学报(理学版), 61(2): 338−346. Google Scholar
[21]	赵泽园, 罗菲. 2020. 基于LightGBM模型的区域滑坡危险性评价研究[J]. 内蒙古煤炭经济, 5: 48−49. Google Scholar
[22]	庄维嘉, 谭文安, 林瑞钦, 等. 2022. GA-LightGBM模型及其在车辆保险需求预测中应用[J]. 上海第二工业大学学报, 39(4): 339−346. Google Scholar