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山地城市滑坡灾害空间分布特征及影响因素分析
作者:
作者单位:

1.重庆市勘测院;2.西南石油大学;3.中国科学院海西研究院泉州装备制造研究所;4.福州大学;5.重庆大学

基金项目:

国家自然科学基金项目（面上项目，重点项目，重大项目）


Spatial Distribution Characteristics and Conditional Factors of Landslide Disasters in Mountain cities
Author:
Affiliation:

1.Chongqing Survey Institude;2.Quanzhou Equipment Manufacturing Institute, Haixi Institute of Chinese Academy of Sciences;3.Fuzhou University;4.Chongqing University

Fund Project:

The National Natural Science Foundation of China (General Program, Key Program, Major Research Plan)

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    摘要:

    针对山地城市滑坡灾害影响区域的不确定性，选择重庆市中心城区典型滑坡作为研究对象，利用最邻近指数、空间热点探测与核密度估计方法分析了历史滑坡灾害点的空间分布特征；并选择高程、坡度、坡向、地貌类型、土壤类型、土壤侵蚀、降雨、水系、地表覆盖、归一化植被指数（NDVI）、人口密度和道路等12个影响因素建立滑坡因子数据库，利用神经网络模型分析滑坡灾害空间分布特征的驱动因素，并定量计算各影响因子的贡献权重。利用受试者工作特征曲线（ROC）对模型进行准确性评估。最邻近指数结果表明研究区历史滑坡灾害点呈聚集型分布特征，空间热点探测与核密度估计均显示渝中区、沙坪坝区和巴南区北部是滑坡聚集程度最大的地区；在所有的影响因子中，人口密度、地貌类型和降雨对研究区滑坡灾害的空间分布影响最大，而坡向和道路影响最低。ROC曲线下面积AUC值达到0.917，表明该神经网络模型能准确反映出该地区滑坡影响因子的影响程度。

    Abstract:

    In view of the uncertainty of landslide disasters affected areas in mountain cities, the typical landslides in the central urban area of Chongqing are selected as the research objects. and the spatial distribution characteristics of historical landslide disaster points are analyzed using the nearest neighbor index, spatial hotspot detection and kernel density estimation methods; A landslide factor database was established with 12 influencing factors includes elevation, slope, aspect, landform type, geological lithology, soil type, soil erosion, rainfall, water system, land use, Normalized Difference Vegetation Index (NDVI), and population density. A neural network model was used to quantitatively analyze the contribution weight of each influencing factor, and the model was accurate using Receiver Operating Characteristic (ROC) curve assessment. The research results show that the historical landslide disaster points in the study area are clustered, and Yuzhong District, Shapingba District, and northern Banan District are the areas where the landslides are most concentrated. Among all the factors, population density, land use and rainfall occupy the highest weight, while the aspect and road are the lowest. The area value under the ROC curve (AUC) was 0.917, indicating that the model can accurately reflect the impact of landslide impact factors in the area.

    参考文献
    [1] Hungr, O., Leroueil, S., Picarelli, L., 2014. The Varnes classification of landslide types, an update. Landslides. https://doi.org/10.1007/s10346-013-0436-y
    [2] Lin, Q., Wang, Y., 2018. Spatial and temporal analysis of a fatal landslide inventory in China from 1950 to 2016. Landslides 15, 2357–2372. https://doi.org/10.1007/s10346-018-1037-6
    [3] Ba, Q., Chen, Y., Deng, S., Yang, J., Li, H., 2018.A comparison of slope units and grid cells as mapping units for landslide susceptibility assessment. Earth Sci. Informatics 11, 373–388.
    [4] https://doi.org/10.1007/s12145-018-0335-9
    [5] [4] Bucci, F., Santangelo, M., Cardinali, M., Fiorucci, F., Guzzetti, F., 2016. Landslide distribution and size in response to Quaternary fault activity: The Peloritani Range, NE Sicily, Italy. Earth Surf. Process. Landforms. https://doi.org/10.1002/esp.3898
    [6] [5] Keefer, D.K., 2000. Statiscal analysis of an earthquake-induced landslide distribution - The 1989 Loma Prieta, California event. Eng. Geol. 58, 231–249. https://doi.org/10.1016/S0013-7952(00)00037-5
    [7] [6] Pardeshi, S.D., Autade, S.E., Pardeshi, S.S., 2013. Landslide hazard assessment: Recent trends and techniques. Springerplus 2, 1–23. https://doi.org/10.1186/2193-1801-2-523
    [8] [7] Rosi, A., Tofani, V., Tanteri, L., Tacconi Stefanelli, C., Agostini, A., Catani, F., Casagli, N., 2018. The new landslide inventory of Tuscany (Italy) updated with PS-InSAR: geomorphological features and landslide distribution. Landslides 15, 5–19. https://doi.org/10.1007/s10346-017-0861-4
    [9] [8] Zhang, F., Huang, X., 2018. Trend and spatiotemporal distribution of fatal landslides triggered by non-seismic effects in China. Landslides 15, 1663–1674. https://doi.org/10.1007/s10346-018-1007-z
    [10] [9] Sepúlveda, S.A., Petley, D.N., 2015. Regional trends and controlling factors of fatal landslides in Latin America and the Caribbean. Nat. Hazards Earth Syst. Sci. 15, 1821–1833. https://doi.org/10.5194/nhess-15-1821-2015
    [11] [10] Qiu, H., Cui, Y., Yang, D., Pei, Y., Hu, S., Ma, S., Hao, J., Liu, Z., 2019. Spatiotemporal Distribution of Nonseismic Landslides during the Last 22 Years in Shaanxi Province, China. ISPRS Int. J. Geo-Information 8, 505. https://doi.org/10.3390/ijgi8110505
    [12] [11] Reichenbach, P., Rossi, M., Malamud, B.D., Mihir, M., Guzzetti, F., 2018. A review of statistically-based landslide susceptibility models. Earth-Science Rev. 180, 60–91. https://doi.org/10.1016/j.earscirev.2018.03.001
    [13] [12] Pawluszek, K., Borkowski, A., 2017. Impact of DEM-derived factors and analytical hierarchy process on landslide susceptibility mapping in the region of Ro?nów Lake, Poland. Nat. Hazards 86, 919–952. https://doi.org/10.1007/s11069-016-2725-y
    [14] [13] Kavzoglu, T., Kutlug Sahin, E., Colkesen, I., 2015. Selecting optimal conditioning factors in shallow translational landslide susceptibility mapping using genetic algorithm. Eng. Geol. 192, 101–112. https://doi.org/10.1016/j.enggeo.2015.04.004
    [15] [14] Dou, J., Bui, D.T., Yunus, A.P., Jia, K., Song, X., Revhaug, I., Xia, H., Zhu, Z., 2015. Optimization of causative factors for landslide susceptibility evaluation using remote sensing and GIS data in parts of Niigata, Japan. PLoS One 10. https://doi.org/10.1371/journal.pone.0133262
    [16] [15] Awawdeh, M.M., Elmughrabi, M.A., Atallah, M.Y., 2018. Landslide susceptibility mapping using GIS and weighted overlay method?: a case study from North Jordan. Environ. Earth Sci. 0, 0. https://doi.org/10.1007/s12665-018-7910-8
    [17] [16] Fabbri, A.G., Chung, C.J.F., Cendrero, A., Remondo, J., 2003. Is prediction of future landslides possible with a GIS? Nat. Hazards 30, 487–499. https://doi.org/10.1023/B:NHAZ.0000007282.62071.75
    [18] [17] Chi, Y., Zheng, W., Shi, H., Sun, J., Fu, Z., 2018. Spatial heterogeneity of estuarine wetland ecosystem health influenced by complex natural and anthropogenic factors. Sci. Total Environ. 634, 1445–1462. https://doi.org/10.1016/j.scitotenv.2018.04.085
    [19] [18] Raia, S., Alvioli, M., Rossi, M., Baum, R.L., Godt, J.W., Guzzetti, F., 2013. Improving predictive power of physically based rainfall-induced shallow landslide models: a probabilistic approach. Geosci. Model Dev. Discuss. 6, 1367–1426. https://doi.org/10.5194/gmdd-6-1367-2013
    [20] [19] Tsangaratos, P., Ilia, I., 2016. Comparison of a logistic regression and Na?ve Bayes classifier in landslide susceptibility assessments: The influence of models complexity and training dataset size. Catena 145, 164–179. https://doi.org/10.1016/j.catena.2016.06.004
    [21] [20] Van Asch, T.W.J., Buma, J., Van Beek, L.P.H., 1999. A view on some hydrological triggering systems in landslides. Geomorphology 30, 25–32. https://doi.org/10.1016/S0169-555X(99)00042-2
    [22] [21] Xu, Y., Xu, X., Tang, Q., 2016. Human activity intensity of land surface: Concept, methods and application in China. J. Geogr. Sci. 26, 1349–1361. https://doi.org/10.1007/s11442-016-1331-y
    [23] [22] Piciullo, L., Calvello, M., Cepeda, J.M., 2018. Territorial early warning systems for rainfall-induced landslides. Earth-Science Rev. 179, 228–247. https://doi.org/10.1016/j.earscirev.2018.02.013
    [24] [23] Tien Bui, D., Ho, T.C., Pradhan, B., Pham, B.T., Nhu, V.H., Revhaug, I., 2016. GIS-based modeling of rainfall-induced landslides using data mining-based functional trees classifier with AdaBoost, Bagging, and MultiBoost ensemble frameworks. Environ. Earth Sci. 75, 1–22. https://doi.org/10.1007/s12665-016-5919-4
    [25] [24] Shafabakhsh, G.A., Famili, A., Bahadori, M.S., 2017. GIS-based spatial analysis of urban traffic accidents: Case study in Mashhad, Iran. J. Traffic Transp. Eng. (English Ed. 4, 290–299. https://doi.org/10.1016/j.jtte.2017.05.005
    [26] [25] Davis, R.A., Lii, K.-S., Politis, D.N., 2011. Remarks on Some Nonparametric Estimates of a Density Function. Ann. Math. Stat. 95–100. https://doi.org/10.1007/978-1-4419-8339-8_13
    [27] [26] Harirforoush, H., Bellalite, L., 2019. A new integrated GIS-based analysis to detect hotspots: A case study of the city of Sherbrooke. Accid. Anal. Prev. 130, 62–74. https://doi.org/10.1016/j.aap.2016.08.015
    [28] [27] Hashimoto, S., Yoshiki, S., Saeki, R., Mimura, Y., Ando, R., Nanba, S., 2016. Development and application of traffic accident density estimation models using kernel density estimation. J. Traffic Transp. Eng. (English Ed. 3, 262–270. https://doi.org/10.1016/j.jtte.2016.01.005
    [29] [28] 何华贵, 朱庆, 曾浩炜, 丁雨淋, 谢潇, 刘飞, 张利国, 李海峰, 胡翰, 张骏骁, 陈力, 陈琳, 张鹏程, 2019. 重大滑坡隐患分析方法综述. 测绘学报 48, 1551–1561. https://doi.org/10.11947/j.AGCS.2019.20190452
    [30] [29] Abbaszadeh Shahri, A., Spross, J., Johansson, F., Larsson, S., 2019. Landslide susceptibility hazard map in southwest Sweden using artificial neural network. Catena 183, 104225.
    [31] https://doi.org/10.1016/j.catena.2019.104225
    [32] [30] de Oliveira, G.G., Ruiz, L.F.C., Guasselli, L.A., Haetinger, C., 2019. Random forest and artificial neural networks in landslide susceptibility modeling: a case study of the F?o River Basin, Southern Brazil. Nat. Hazards 99, 1049–1073. https://doi.org/10.1007/s11069-019-03795-x
    [33] [31] Harmouzi, H., Nefeslioglu, H.A., Rouai, M., Sezer, E.A., Dekayir, A., Gokceoglu, C., 2019. Landslide susceptibility mapping of the Mediterranean coastal zone of Morocco between Oued Laou and El Jebha using artificial neural networks (ANN). Arab. J. Geosci. 12. https://doi.org/10.1007/s12517-019-4892-0
    [34] [32] Poudyal, C.P., Chang, C., Oh, H.J., Lee, S., 2010. Landslide susceptibility maps comparing frequency ratio and artificial neural networks: A case study from the Nepal Himalaya. Environ. Earth Sci. 61, 1049–1064. https://doi.org/10.1007/s12665-009-0426-5
    [35] [33] Yilmaz, I., 2009. A case study from Koyulhisar (Sivas-Turkey) for landslide susceptibility mapping by artificial neural networks. Bull. Eng. Geol. Environ. 68, 297–306. https://doi.org/10.1007/s10064-009-0185-2
    [36] [34] Pourghasemi, H.R., Rahmati, O., 2018. Prediction of the landslide susceptibility: Which algorithm, which precision? Catena 162, 177–192. https://doi.org/10.1016/j.catena.2017.11.022
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  • 收稿日期:2020-03-21
  • 最后修改日期:2020-05-10
  • 录用日期:2020-05-12
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