Landslide Susceptibility Assessment by Applying the Coupling Method of Radial Basis Neural Network and Adaboost: A Case Study from the Three Gorges Reservoir Area
-
摘要: 准确的滑坡易发性评价结果是滑坡风险评价的重要基础.为提升滑坡易发性评价精度，以三峡库区龙驹坝为例，选取坡度等10个因子构建滑坡易发性评价指标体系，应用频率比方法定量分析各指标与滑坡发育的关系.在此基础上，随机选取70%/30%的滑坡数据作为训练/测试样本，应用径向基神经网络和Adaboost集成学习耦合模型(RBNN-Adaboost)，径向基神经网络和逻辑回归模型分别开展易发性评价.结果显示：水系距离、坡度等是滑坡发育的主控因素；RBNN-Adaboost耦合模型的预测精度最高(0.820)，优于RBNN模型和LR模型的0.781和0.748.Adaboost集成算法能进一步提升模型的预测性能，所提出的耦合模型结合了两者的优点，具有更强的预测能力，是一种可靠的滑坡易发性评价模型.Abstract: Accurate landslide susceptibility map is an important basis for landslide risk assessment. In order to improve the accuracy of landslide susceptibility assessment,Longjuba area in the Three Gorges reservoir area was taken as a case study,10 factors (i.e. slope) were selected and parepared,and the frequency ratio method was used to analyze the relationship between each factor and landslide development quantitatively. 70% landslides were randomly selected as training samples while the 30% were adopted for testing,the radial basis neural network and adaboost ensemble learning coupled model (RBNN-Adaboost),radial basis neural network (RBNN) and logistic regression (LR) model were used to make the assessment of landslide susceptibility,respectively. Results show that factors of distance to river,slope and so on are the main controlling factors of landslide development; RBNN-Adaboost shows the best prediction accuracy (0.820) than logistic regression model (0.748) and RBNN (0.781). The adaboost ensemble learning can further improve the prediction performance of the model. By combining the advantages of RBNN and adaboost,the proposed method achieves the highest prediction accuracy,which is a reliable assessment model of landslide susceptibility.
-
图 3 (a) 三峡库区地图和(b)研究区高程及滑坡分布图
Fig. 3. The map of Three Gorges reservoir area (a) and the landslide distribution map with elevation (b)
图 5 滑坡指标分布图
a.高程；b.坡度；c.坡向；d.径流强度指数；e.地形湿度指数；f.地层岩性；g.斜坡结构；h.构造距离；i.水系距离；j.道路距离；A~F意义见附表 2
Fig. 5. Landslide causal factors of the study area
-
[1] Bai, S.B., Wang, J., Lü, G.N., et al., 2010.GIS-Based Logistic Regression for Landslide Susceptibility Mapping of the Zhongxian Segment in the Three Gorges Area, China.Geomorphology, 115(1-2):23-31. https://doi.org/10.1016/j.geomorph.2009.09.025 [2] Bui, D.T., Ho, T.C., Pradhan, B., et al., 2016.GIS-Based Modeling of Rainfall-Induced Landslides Using Data Mining-Based Functional Trees Classifier with AdaBoost, Bagging, and MultiBoost Ensemble Frameworks.Environmental Earth Sciences, 75(14):1101. https://doi.org/10.1007/s12665-016-5919-4 [3] Cao, Y., Yin, K.L., Zhou, C., et al., 2020.Establishment of Landslide Groundwater Level Prediction Model Based on GA-SVM and Influencing Factor Analysis.Sensors, 20(3):845. https://doi.org/10.3390/s20030845 [4] Corominas, J., Westen, C.V., Frattini, P., et al., 2014.Recommendations for the Quantitative Analysis of Landslide Risk.Bulletin of Engineering Geology and the Environment, 73(2):209-263. https://doi.org/10.1007/s10064-013-0538-8 [5] Fell, R., Corominas, J., Bonnard, C., et al., 2008.Guidelines for Landslide Susceptibility, Hazard and Risk Zoning for Land Use Planning.Engineering Geology, 102(3-4):85-98. https://doi.org/10.1016/j.enggeo.2008.03.022 [6] Feng, H.J., Zhou, A.G., Yu, J.J., et al., 2016.A Comparative Study on Plum-Rain-Triggered Landslide Susceptibility Assessment Models in West Zhejiang Province.Earth Science, 41(3):403-415(in Chinese with English abstract). https://doi.org/10.3799/dqkx.2016.032 [7] Freund, Y., Schapire, R.E., 1997.A Decision-Theoretic Generalization of Online Learning and an Application to Boosting.Journal of Computer and System Sciences, 55(1):119-139. https://doi.org/10.1006/jcss.1997.1504 [8] Guo, Z.Z., Yin, K.L., Fu, S., et al., 2019.Evaluation of Landslide Susceptibility Based on GIS and WOE-BP Model.Earth Science, 44(12):4299-4312(in Chinese with English abstract). https://doi.org/10.3799/dqkx.2018.555 [9] Hong, H., Chen, W., Xu, C., et al., 2016.Rainfall-Induced Landslide Susceptibility Assessment at the Chongren Area (China) Using Frequency Ratio, Certainty Factor, and Index of Entropy.Geocarto International, 32(2):139-154. https://doi.org/10.1080/10106049.2015.1130086 [10] Li, S.L., Xu, Q., Tang, M.G., et al., 2020.Study on Spatial Distribution and Key Influencing Factors of Landslides in Three Gorges Reservoir Area.Earth Science, 45(1):341-354(in Chinese with English abstract). https://doi.org/10.3799/dqkx.2017.576 [11] Krawczyk, B., Minku, L.L., Gama, J., et al., 2017.Ensemble Learning for Data Stream Analysis:A Survey.Information Fusion, 37:132-156. https://doi.org/10.1016/j.inffus.2017.02.004 [12] Ma, S.Y., Qiu, H.J., Hu, S., et al., 2019.Quantitative Assessment of Landslide Susceptibility on the Loess Plateau in China.Physical Geography. https://doi.org/10.1080/02723646.2019.1674559 [13] Moore, I.D., Grayson, R.B., Ladson, A.R., 1991.Digital Terrain Modelling:A Review of Hydrological, Geomorphological, and Biological Applications.Hydrological Processes, 5(1):3-30. https://doi.org/10.1002/hyp.3360050103 [14] Paisitkriangkrai, S., Shen, C.H., van den Hengel, A., 2016.Pedestrian Detection with Spatially Pooled Features and Structured Ensemble Learning.IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(6):1243-1257. https://doi.org/10.1109/tpami.2015.2474388 [15] Pham, B.T., Bui, D.T., Prakash, I., et al., 2017.Hybrid Integration of Multilayer Perceptron Neural Networks and Machine Learning Ensembles for Landslide Susceptibility Assessment at Himalayan Area (India) Using GIS.Catena, 149:52-63. https://doi.org/10.1016/j.catena.2016.09.007 [16] Qiu, H.J., Cao, M.M., Liu, W., et al., 2014.The Susceptibility Assessment of Landslide and Its Calibration of the Models Based on Three Different Models.Scientia Geographica Sinica, 34(1):110-115(in Chinese with English abstract). http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=dlkx201401016 [17] Shi, J.S., Zhang, Y.S., Dong, C., et al., 2005.Based Landslide Hazard Zonation of the New Badong County Site.Acta Geoscientica Sinica, 26(3):275-282(in Chinese with English abstract). http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=dqxb200503014 [18] Wang, J., Guo, J., Wang, W.D., et al., 2012.Application and Comparison of Weighted Linear Combination Model and Logistic Regression Model in Landslide Susceptibility Mapping.Journal of Central South University (Science and Technology), 43(5):1932-1939(in Chinese with English abstract). http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=zngydxxb201205050 [19] Wang, J.J., Yin, K.L., Xiao, L.L., 2014.Landslide Susceptibility Assessment Based on GIS and Weighted Information Value:A Case Study of Wanzhou District, Three Gorges Reservoir.Chinese Journal of Rock Mechanics and Engineering, 33(4):797-808(in Chinese with English abstract). [20] Xu, Q., Li, W.L., Dong, X.J., et al., 2017.The Xinmocun Landslide on June 24, 2017 in Maoxian, Sichuan:Characteristics and Failure Mechanism.Chinese Journal of Rock Mechanics and Engineering, 36(11):2612-2628(in Chinese with English abstract). [21] Yin, K.L., Zhu, L.F., 2001.Landslide Hazard Zonation and Application of GIS.Earth Science Frontiers, 8(2):279-284(in Chinese with English abstract). doi: 10.1080-014311698215865/ [22] Yu, L.B., Cao, Y., Zhou, C., et al., 2019.Landslide Susceptibility Mapping Combining Information Gain Ratio and Support Vector Machines:A Case Study from Wushan Segment in the Three Gorges Reservoir Area, China.Applied Sciences, 9(22):4756. https://doi.org/10.3390/app9224756 [23] Zhang, J., Yin, K.L., Wang, J.J., et al., 2016.Evaluation of Landslide Susceptibility for Wanzhou District of Three Gorges Reservoir Chinese Journal of Rock Mechanics and Engineering, 35(2):284-296(in Chinese with English abstract). http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=yslxygcxb201602009 [24] Zhang, R.L., Meng, H., Lian, J.F., et al., 2010.Landslide Susceptibility Assessment by Probability Ratio Model Based on GIS.Earth Science Frontiers, 17(6):291-297(in Chinese with English abstract). http://d.old.wanfangdata.com.cn/Periodical/dxqy201006039 [25] Zhou, C., 2018.Landslide Identification and Prediction with the Application of Time Series InSAR (Dissertation).China University of Geosciences, Wuhan(in Chinese with English abstract). [26] Zhou, C., Yin, K.L., Cao, Y., et al., 2015.Displacement Prediction of Step-Like Landslide Based on the Response of Inducing Factors and Support Vector Machine.Chinese Journal of Rock Mechanics and Engineering, 34(Suppl.2):4132-4139(in Chinese with English abstract). [27] Zhou, C., Yin, K.L., Cao, Y., et al., 2016.Application of Time Series Analysis and PSO-SVM Model in Predicting the Bazimen Landslide in the Three Gorges Reservoir, China.Engineering Geology, 204:108-120. https://doi.org/10.1016/j.enggeo.2016.02.009 [28] Zhou, C., Yin, K.L., Cao, Y., et al., 2018.Landslide Susceptibility Modeling Applying Machine Learning Methods:A Case Study from Longju in the Three Gorges Reservoir Area, China.Computers and Geosciences, 112:23-37. https://doi.org/10.1016/j.cageo.2017.11.019 [29] Zięba, M., Tomczak, S.K., Tomczak, J.M., 2016.Ensemble Boosted Trees with Synthetic Features Generation in Application to Bankruptcy Prediction.Expert Systems with Applications, 58:93-101. https://doi.org/10.1016/j.eswa.2016.04.001 [30] 冯杭建, 周爱国, 俞剑君, 等, 2016.浙西梅雨滑坡易发性评价模型对比.地球科学, 41(3):403-415. doi: 10.3799/dqkx.2016.032 [31] 郭子正, 殷坤龙, 付圣, 等, 2019.基于GIS与WOE-BP模型的滑坡易发性评价.地球科学, 44(12):4299-4312. doi: 10.3799/dqkx.2018.555 [32] 李松林, 许强, 汤明高, 等, 2020.三峡库区滑坡空间发育规律及其关键影响因子.地球科学, 45(1):341-354. doi: 10.3799/dqkx.2017.576 [33] 邱海军, 曹明明, 刘闻, 等, 2014.基于三种不同模型的区域滑坡灾害敏感性评价及结果检验研究.地理科学, 34(1):110-115. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=dlkx201401016 [34] 石菊松, 张永双, 董诚, 等, 2005.基于GIS技术的巴东新城区滑坡灾害危险性区划.地球学报, 26(3):275-282. http://d.old.wanfangdata.com.cn/Periodical/dqxb200503014 [35] 王佳佳, 殷坤龙, 肖莉丽, 2014.基于GIS和信息量的滑坡灾害易发性评价:以三峡库区万州区为例.岩石力学与工程学报, 33(4):797-808. http://d.old.wanfangdata.com.cn/Periodical/yslxygcxb201404018 [36] 王进, 郭靖, 王卫东, 等, 2012.权重线性组合与逻辑回归模型在滑坡易发性区划中的应用与比较.中南大学学报(自然科学版), 43(5):1932-1939. http://d.old.wanfangdata.com.cn/Periodical/zngydxxb201205050 [37] 许强, 李为乐, 董秀军, 等, 2017.四川茂县叠溪镇新磨村滑坡特征与成因机制初步研究.岩石力学与工程学报, 36(11):2612-2628. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=yslxygcxb201711002 [38] 殷坤龙, 朱良峰, 2001.滑坡灾害空间区划及GIS应用研究.地学前缘, 8(2):279-284. http://d.old.wanfangdata.com.cn/Periodical/dxqy200102010 [39] 张俊, 殷坤龙, 王佳佳, 等, 2016.三峡库区万州区滑坡灾害易发性评价研究.岩石力学与工程学报, 35(2):284-296. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=yslxygcxb201602009 [40] 张若琳, 孟晖, 连建发, 等, 2010.基于GIS的概率比率模型的滑坡易发性评价.地学前缘, 17(6):291-297. http://d.old.wanfangdata.com.cn/Periodical/dxqy201006039 [41] 周超, 2018.集成时间序列InSAR技术的滑坡早期识别与预测研究(博士学位论文).武汉: 中国地质大学. [42] 周超, 殷坤龙, 曹颖, 等, 2015.基于诱发因素响应与支持向量机的阶跃式滑坡位移预测.岩石力学与工程学报, 34(增刊2):4132-4139. -
dqkx-45-6-1865-Table1-5.pdf
-
下载:
点击查看大图
图(8)
计量
- 文章访问数: 1426
- HTML全文浏览量: 200
- PDF下载量: 111
- 被引次数: 0
