Evaluation of Regional Landslide Susceptibility Assessment Based on BP Neural Network
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摘要: 区域滑坡易发性评价是开展区域滑坡地质灾害危险性、风险性评价的基础。结合新疆伊宁县野外地质调查数据，采用数据挖掘技术分析研究区黄土滑坡控制性因素，以此作为挑选致灾因子的判据。通过BP神经网络模型来构建区域滑坡易发性预测模型，采用训练良好的BP神经网络模型，结合整个研究区DEM数据和遥感解译数据，得出研究区滑坡灾害易发性分区图，为当地区域滑坡的预防和治理决策提供一定参考。Abstract: The evaluation of regional landslide susceptibility is the basis of regional landslide hazard assessment and regional landslide risk assessment. Combined with the field geological survey data of Yining County in Xinjiang, the controlling factors of landslide in the study area are analyzed by using data mining technology, which can be used as the criteria for selecting disaster causing factors. The BP neural network model is used to build the prediction model of regional landslide susceptibility. The trained BP neural network model is combined with the DEM data and remote sensing interpretation data of the whole study area to obtain the landslide hazard susceptibility zoning map of the study area, which provides a certain reference for the local regional landslide prevention and governance decision-making.
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林金煌, 张岸, 邓超, 等.闽三角城市群地质灾害敏感性评价[J].地球信息科学学报, 2018, 20(09):1286-1297. LIN Jinhuang, ZHANG An, DENG Chao, et al. Geological Hazard Sensitivity Evaluation of Urban Agglomeration in Fujian Delta[J]. Journal of Geo-Information Science, 2018, 20(09):1286-1297.
陶舒, 胡德勇, 赵文吉, 等.基于信息量与逻辑回归模型的次生滑坡灾害敏感性评价——以汶川县北部为例[J].地理研究, 2010, 29(09):1594-1605. TAO Shu, HU Deyong, ZHAO Wenji, et al. Sensitivity Evaluation of Secondary Landslide Disaster Based on Information and Logistic Regression Model-Taking the North of Wenchuan County as an Example[J].Geographical Research, 2010, 29(09):1594-1605.
兰恒星, 王苓涓, 周成虎.地理信息系统支持下的滑坡灾害分析模型研究[J].工程地质学报, 2002, (04):421-427. LAN Hengxing, WANG Lingjuan, ZHOU Chenghu. Study on Landslide Disaster Analysis Model Supported by GIS[J].Journal of Engineering Geology, 2002(04):421-427.
黄发明, 殷坤龙, 蒋水华, 等.基于聚类分析和支持向量机的滑坡易发性评价[J].岩石力学与工程学报, 2018, 37(01):156-167. HUANG Faming, YIN Kunlong, JIANG Shuihua, et al. Landslide Susceptibility Evaluation Based on Cluster Analysis and Support Vector Machine[J].Journal of Rock Mechanics and Engineering, 2018, 37(01):156-167.
王倩, 薛云, 张维, 等.基于支持向量机的滑坡易发性评价[J].湖南城市学院学报(自然科学版), 2021, 30(01):22-28. WANG Qian, XUE Yun, ZHANG Wei, et al. Landslide Susceptibility Evaluation Based on Support Vector Machine[J]. Journal of Hunan City University (Natural Science Edition), 2021, 30(01):22-28.
胡涛, 樊鑫, 王硕, 等.基于逻辑回归模型和3S技术的思南县滑坡易发性评价[J].地质科技通报, 2020, 39(02):113-121. HU Tao, FAN Xin, WANG Shuo, et al. Evaluation of Landslide Susceptibility in Sinan County Based on Logistic Regression Model and 3S Technology[J]. Geological Science and Technology Bulletin, 2020, 39(02):113-121.
唐睿旋, 晏鄂川, 唐薇.基于粗糙集和BP神经网络的滑坡易发性评价[J].煤田地质与勘探, 2017, 45(06):129-138. TANG Ruixuan, YAN Echuan, TANG Wei.Landslide Susceptibility Evaluation Based on Rough Set and BP Neural Network[J].Coalfield Geology and Exploration, 2017, 45(06):129-138.
杨永刚, 殷坤龙, 赵海燕, 等.基于C5.0决策树-快速聚类模型的万州区库岸段乡镇滑坡易发性区划[J].地质科技情报, 2019, 38(06):189-197. YANG Yonggang, YIN Kunlong, ZHAO Haiyan, et al. Zoning of Township Landslide Susceptibility in Kuan Section of Wanzhou District Based on C5.0 Decision Tree Fast Clustering Model[J].Geological Science and Technology Information, 2019, 38(06):189-197.
王佳运, 石小亚, 武立, 等."8.12"山阳滑坡视向滑动成因机理[J].西北地质, 2018, 51(3):232-239. WANG Jiayun, SHI Xiaoya, WU Li, et al. Formation Mechanism of Apparent Dip Slide in the Shanyang "8.12" Landslide[J].Northwestern Geology, 2018, 51(3):232-239.
曹小红, 孟和, 尚彦军, 等.伊犁谷地黄土滑坡发育分布规律及成因[J].新疆地质, 2020, 38(03):405-411. CAO Xiaohong, MENG He, SHANG Yanjun, et al. Development, Distribution and Causes of Loess Landslides in Yili Valley[J].Xinjiang Geology, 2020, 38(03):405-411.
朱立峰.黑方台滑坡群控制因素与外动力条件分析[J].西北地质, 2019, 52(3):217-222. ZHU Lifeng. Analysis of Control Factors and External Force for the Landslides in Heifangtai Area[J].Northwestern Geology, 2019, 52(3):217-222.
唐亚明, 张茂省, 李林, 等.滑坡易发性危险性风险评价例析[J].水文地质工程地质, 2011, 38(02):125-129. TANG Yaming, ZHANG Maosheng, LI Lin, et al. Case Analysis of Landslide Susceptibility Risk Assessment[J].Hydrogeology and Engineering Geology, 2011, 38 (02):125-129.
安海堂, 刘平.新疆伊犁地区黄土滑坡成因及影响因素分析[J].地质灾害与环境保护, 2010, 21(03):22-25. AN Haitang, LIU Ping. Analysis on Causes and Influencing Factors of Loess Landslide in Yili Area, Xinjiang[J].Geological Hazards and Environmental Protection, 2010, 21 (03):22-25.
赵尚毅, 郑颖人, 时卫民, 等.用有限元强度折减法求边坡稳定安全系数[J].岩土工程学报, 2002(03):343-346. ZHAO Shangyi, ZHENG Yingren, SHI Weimin, et al. Calculation of Slope Stability Safety Factor by Finite Element Strength Reduction Method[J].Journal of Geotechnical Engineering, 2002, (03):343-346.
徐张建, 林在贯, 张茂省.中国黄土与黄土滑坡[J].岩石力学与工程学报, 2007(07):1297-1312. XU Zhangjian, LIN Zaiguan, ZHANG Maosheng. Loess and Loess Landslide in China[J]. Journal of Rock Mechanics and Engineering, 2007, (07):1297-1312.
庄茂国, 魏云杰, 邵海, 等.新疆伊犁皮里青河黄土滑坡类型及其发育特征[J].中国地质灾害与防治学报, 2018, 29(01):54-59. ZHUANG Maoguo, WEI Yunjie, SHAO Hai, et al. Types and Development Characteristics of Piliqing River Loess Landslide in Yili, Xinjiang[J].Chinese Journal of Geological Hazards and Prevention, 2018, 29(01):54-59.
武雪玲, 任福, 牛瑞卿.多源数据支持下的三峡库区滑坡灾害空间智能预测[J].武汉大学学报(信息科学版), 2013, 38(08):963-968. WU Xueling, REN Fu, NIU Ruiqing. Spatial Intelligent Prediction of Landslide Disaster in the Three Gorges Reservoir Area Supported by Multi-source Data[J].Journal of Wuhan University(Information Science Edition), 2013, 38(08):963-968.
厍向阳, 薛惠锋, 雷学武, 等.基于分类规则挖掘的遥感影像分类研究[J].遥感学报, 2006(03):332-338. SHE Xiangyang, XUE Huifeng, LEI Xuewu, et al.Research on Remote Sensing Image Classification Based on Classification Rule Mining[J]. Journal of Remote Sensing, 2006(03):332-338.
邱维蓉, 吴帮玉, 潘学树, 等.几种聚类优化的机器学习方法在灵台县滑坡易发性评价中的应用[J].西北地质, 2020, 53(01):222-233. QIU Weirong, WU Bangyu, PAN Xueshu, et al.Application of Several Clustering Optimization Machine Learning Methods in Landslide Susceptibility Evaluation in Lingtai County[J].Northwestern Geology, 2020, 53(01):222-233.
李利峰, 杨华, 张娟, 等.基于人工神经网络的区域滑坡预测研究[J].气象与环境科学, 2020, 43(03):65-70. LI Lifeng, YANG Hua, ZHANG Juan, et al. Study on Regional Landslide Prediction Based on Artificial Neural Network[J].Meteorological and Environmental Science, 2020, 43 (03):65-70.
Corominas J, Westen C V, Frattini P, et al. Recommendations for the quantitative analysis of landslide risk[J]. Bulletin of Engineering Geology and the Environment, 2014, 73(2):209-63.
Langping L, Hengxing L, Changbaog, et al.A modified frequency ratio method for landslide susceptibility assessment[J].Landslides, 2016, 14(2):1-15.
Caniani D, Pascale S, Sdao F, et al. Neural networks and landslide susceptibility:a case study of the urban area of Potenza[J]. Natural Hazards, 2008, 45(1):55-72.
Yeon Y K, Han J G, Ryu K H. Landslide susceptibility mapping in Injae, Korea, using a decision tree[J]. Engineering Geology, 2010, 116(3):274-83.
Yilmaz I. Comparison of landslide susceptibility mapping methodologies for Koyulhisar, Turkey:conditional probability, logistic regression, artificial neural networks, and support vector machine[J].Environmental Earth Sciences, 2010, 61(4):821-36.
Nourani V, Pradhan B, Ghaffari H, et al. Landslide susceptibility mapping at Zonouz Plain, Iran using genetic programming and comparison with frequency ratio, logistic regression, and artificial neural network models[J]. Natural Hazards, 2014, 71(1):523-47.
Park S, Choi C, Kim B, et al. Landslide susceptibility mapping using frequency ratio, analytic hierarchy process, logistic regression, and artificial neural network methods at the Inje area, Korea[J]. Environmental Earth ences, 2013, 68(5):1443-64.
Xin Y. Evolving artificial neural networks[J]. Proceedings of the IEEE, 1999, 87(9):1423-47.
Zhang M, Jie L. Controlling factors of loess landslides in western China[J]. Environmental Earth Sciences, 2010, 59(8):1671-80.
Duan Z, He Z G, Lin H Z. Stability Analysis of Loess Landslides Induced by Irrigation[J]. Applied Mechanics & Materials, 2015, 716-717, 395-9.
Meng X Z, Liu H L, Hou Z S. Multi-Sensor Data Fusion Technology Based on BP Neural Network Application in the Coal Mine Equipment Fault Diagnosis[J]. AppliedMECHANICS & Materials, 2014, 678, 238-41.
Li C Z. Convergence analysis of online gradient method for BP neural networks[J]. Neural Networks, 2011.
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