[1] |
徐在庸 . 山洪及其防治[M]. 北京: 水利出版社, 1981.
|
|
[ Xu Z Y. Mountain torrents and their prevention[M]. Beijing: Water Publishing, 1981. ]
|
[2] |
黄大鹏, 刘闯, 彭顺风 . 洪灾风险评价与区划研究进展[J]. 地理科学进展, 2007,26(4):11-22.
doi: 10.11820/dlkxjz.2007.04.004
|
|
[ Huang D P, Liu C, Peng S F . Progress on assessment and regionalization of flood risk[J]. Progress in Geography, 2007,26(4):11-22. ]
doi: 10.11820/dlkxjz.2007.04.004
|
[3] |
王秋香, 崔彩霞, 姚艳丽 . 新疆不同区域洪灾受灾面积变化趋势及多尺度分析[J]. 地理学报, 2008,63(7):769-779.
doi: 10.11821/xb200807011
|
|
[ Wang Q X, Cui C X, Yao Y L . Variation trends and multi-scale analysis of flood affected area in various regions of Xinjiang[J]. Acta Geographica Sinica, 2008,63(7):769-779.]
doi: 10.11821/xb200807011
|
[4] |
González-Arqueros M L, Mendoza M E, Bocco G , et al. Flood susceptibility in rural settlements in remote zones: The case of a mountainous basin in the Sierra-Costa region of Michoacán, Mexico[J]. Journal of Environmental Management, 2018,223:685-693.
doi: 10.1016/j.jenvman.2018.06.075
pmid: 29975896
|
[5] |
Gigovic L, Pamucar D, Bajic Z , et al. Application of GIS-interval rough AHP methodology for flood hazard mapping in urban areas[J]. Water, 2017,9(6):360-386.
doi: 10.1002/1520-6661(200011/12)9:6<360::AID-MFM1008>3.0.CO;2-Y
pmid: 11243295
|
[6] |
Tingsanchali T, Karim F . Flood-hazard assessment and risk-based zoning of a tropical flood plain: Case study of the Yom River, Thailand[J]. Hydrological Sciences Journal, 2010,55(2):145-161.
doi: 10.1080/02626660903545987
|
[7] |
Jiang W, Deng L, Chen L , et al. Risk assessment and validation of flood disaster based on fuzzy mathematics[J]. Progress in Natural Science: Materials International, 2009,19(10):1419-1425.
doi: 10.1016/j.pnsc.2008.12.010
|
[8] |
Patro S, Chatterjee C, Mohanty S , et al. Flood inundation modeling using MIKE FLOOD and remote sensing data[J]. Journal of the Indian Society of Remote Sensing, 2009,37(1):107-118.
doi: 10.1007/s12524-009-0002-1
|
[9] |
Kalantari Z, Cavalli M, Cantone C , et al. Flood probability quantification for road infrastructure: Data-driven spatial-statistical approach and case study applications[J]. Science of the Total Environment, 2017,581:386-398.
doi: 10.1016/j.scitotenv.2016.12.147
pmid: 28062101
|
[10] |
Tehrany M S, Pradhan B, Mansor S , et al. Flood susceptibility assessment using GIS-based support vector machine model with different kernel types[J]. Catena, 2015,125:91-101.
doi: 10.1016/j.catena.2014.10.017
|
[11] |
Zhao G, Pang B, Xu Z , et al. Mapping flood susceptibility in mountainous areas on a national scale in China[J]. Science of the Total Environment, 2018,615:1133-1142.
doi: 10.1016/j.scitotenv.2017.10.037
pmid: 29751419
|
[12] |
Kia M B, Pirasteh S, Pradhan B , et al. An artificial neural network model for flood simulation using GIS: Johor River Basin, Malaysia[J]. Environmental Earth Sciences, 2012,67(1):251-264.
doi: 10.1007/s12665-011-1504-z
|
[13] |
Tehrany M S, Jones S, Shabani F . Identifying the essential flood conditioning factors for flood prone area mapping using machine learning techniques[J]. Catena, 2019,175:174-192.
doi: 10.1016/j.catena.2018.12.011
|
[14] |
赖成光, 陈晓宏, 赵仕威 , 等. 基于随机森林的洪灾风险评价模型及其应用[J]. 水利学报, 2015,46(1):58-66.
|
|
[ Lai C G, Chen X H, Zhao S W . et al. A flood risk assessment model based on Random Forest and its application[J]. Journal of Hydraulic Engineering, 2015,46(1):58-66. ]
|
[15] |
Lee S, Kim J C, Jung H S . Spatial prediction of flood susceptibility using random-forest and boosted-tree models in Seoul metropolitan city, Korea[J]. Geomatics Natural Hazards & Risk, 2017,8(2):1185-1203.
doi: 10.1007/s10661-019-7715-6
pmid: 31485854
|
[16] |
Breiman L . Random forests[J]. Machine Learning, 2001,45(1):5-32.
doi: 10.1023/A:1010933404324
|
[17] |
Sánchez A S, Iglesias-Rodríguez F J, Fernández P R , et al. Applying the K-nearest neighbor technique to the classification of workers according to their risk of suffering musculoskeletal disorders[J]. International Journal of Industrial Ergonomics, 2016,52:92-99.
doi: 10.1016/j.ergon.2015.09.012
|
[18] |
Deng Z Y, Zhu X S, Cheng D B , et al. Efficient kNN classification algorithm for big data[J]. Neurocomputing, 2016,195:143-148.
doi: 10.1155/2017/9290230
pmid: 28316616
|
[19] |
Jiang S Y, Pang G S, Wu M L , et al. An improved K-nearest-neighbor algorithm for text categorization[J]. Expert Systems with Applications, 2012,39(1):1503-1509.
doi: 10.1016/j.eswa.2011.08.040
|
[20] |
Tobler W . A computer movie simulating urban growth in the Detroit region[J]. Economic Geography, 1970,46(2):234-240.
doi: 10.2307/143141
|
[21] |
Freund Y, Schapire R E . A decision-theoretic generalization of on-line learning and an application to boosting[J]. Journal of Computer and System Sciences, 1997,55(1):119-139.
doi: 10.1006/jcss.1997.1504
|
[22] |
曹莹, 苗启广, 刘家辰 , 等. AdaBoost算法研究进展与展望[J]. 自动化学报, 2013,39(6):745-758.
doi: 10.3724/SP.J.1004.2013.00745
|
|
[ Cao Y, Miao Q G, Liu J C , et al. Advance and prospects of AdaBoost algorithm[J]. Acta Automatica Sinica, 2013,39(6):745-758. ]
doi: 10.3724/SP.J.1004.2013.00745
|
[23] |
Zhao Y, Gong L, Zhou B , et al. Detecting tomatoes in greenhouse scenes by combining AdaBoost classifier and colour analysis[J]. Biosystems Engineering, 2016,148:127-137.
doi: 10.1016/j.biosystemseng.2016.05.001
|
[24] |
Nizamani S, Memon N, Wiil U K . Detection of illegitimate emails using boosting algorithm[J]. Counter Terrorism and Open Source Intelligence, 2011,2:249-264.
|
[25] |
Rios-Cabrera R, Tuytelaars T, Van Gool L . Efficient multi-camera vehicle detection, tracking, and identify-cation in a tunnel surveillance application[J]. Computer Vision and Image Understanding, 2012,116(6):742-753.
doi: 10.1016/j.cviu.2012.02.006
|
[26] |
管珉, 陈兴旺 . 江西省山洪灾害风险区划初步研究[J]. 暴雨灾害, 2007(4):339-343.
|
|
[ Guan M, Chen X W . Research of regional torrent risk zonation in Jiangxi Province[J]. Torrential Rain and Disasters, 2007(4):339-343. ]
|
[27] |
Davidson R A, Lambert K B . Comparing the hurricane disaster risk of U.S. coastal counties[J]. Natural Hazards Review, 2001,2(3):132-142.
doi: 10.1061/(ASCE)1527-6988(2001)2:3(132)
|
[28] |
方秀琴, 王凯, 任立良 , 等. 基于GIS的江西省山洪灾害风险评价与分区[J]. 灾害学, 2017,32(1):111-116.
|
|
[ Fang X Q, Wang K, Ren L L , et al. Risk assessment and zoning of mountain torrent disaster based on GIS in Jiangxi Province[J]. Journal of Catastrophology, 2017,32(1):111-116. ]
|
[29] |
江西省水文局. 江西省暴雨洪水查算手册(2010版)[EB/OL].
|
|
[ Hydrographic Office of Jiangxi Province. Rainstorm and flood calculation manual in Jiangxi Province (2010) [EB/OL]. . ]
|
[30] |
中国科学院资源环境科学数据中心[DB/OL].
|
|
[ Resource and Environment Data Cloud Platform[DB/OL]. ]
|
[31] |
Huang J, Ling C X . Using AUC and accuracy in evaluating learning algorithms[J]. IEEE Transactions on Knowledge & Data Engineering, 2005,17(3):299-310.
doi: 10.1109/TVCG.2018.2864814
pmid: 30130212
|
[32] |
Bradley A P . The use of the area under the ROC curve in the evaluation of machine learning algorithms[J]. Pattern Recognition, 1997,30(7):1145-1159.
doi: 10.1038/s41398-019-0638-8
pmid: 31740657
|
[33] |
Monserud R A, Leemans R . Comparing global vegetation maps with the Kappa statistic[J]. Ecological Modelling, 1992,62(4):275-293.
doi: 10.1016/0304-3800(92)90003-W
|
[34] |
汪云云, 陈松灿 . 基于AUC的分类器评价和设计综述[J]. 模式识别与人工智能, 2011,24(1):64-71.
|
|
[ Wang Y Y, Chen S C . A survey of evaluation and design for AUC based classifier[J]. Pattern Recognition and Artificial Intelligence, 2011,24(1):64-71. ]
|
[35] |
Swets J A . Measuring the accuracy of diagnostic systems[J]. Science, 1988,240(4857):1285-1293.
doi: 10.1126/science.3287615
pmid: 3287615
|
[36] |
Freund Y, Schapire R E . Experiments with a new boosting algorithm[J]. Machine Learning: Proceedings of the Thirteenth International Conference, 1996,13:148-156.
doi: 10.3390/s19204383
pmid: 31658774
|
[37] |
Kursa M B, Rudnicki W R . Feature selection with the Boruta package[J]. Journal of Statistical Software, 2010,36(11):1-13.
|