相似洪水分析与智能推荐模型研究
Research on Similar Flood Analysis and Intelligent Recommendation Modeling
DOI: 10.12677/JWRR.2023.125052, PDF,  被引量    国家科技经费支持
作者: 田逸飞, 王 乐, 严方家, 张 潇*：长江水利委员会水文局，湖北 武汉；长江水利委员会水旱灾害防御创新团队，湖北 武汉；流域水模拟与预报调度智能技术创新中心，湖北 武汉；杨海从：汉江水利水电(集团)有限责任公司，湖北 武汉；纪国良：中国长江三峡集团有限公司，湖北 武汉；马振亮：长江大学资源与环境学院油气地球化学与环境湖北省重点实验室，湖北 武汉
关键词: 洪水预报；洪水分类；主成分分析；聚类分析；嘉陵江流域；Flood Forecasting； Flood Classification； Principal Component Analysis； Clustering Analysis； Jialingjiang Ba-sin
摘要: 本文提出了基于流域历史资料的相似洪水智能推荐模型，根据降雨数值预报过程对历史典型洪水进行相似洪水搜索达到洪水预报的效果。首先，选取暴雨洪水特征指标并采用主成分分析方法对特征指标降维，然后采用K-means聚类算法对历史典型场次暴雨洪水进行分类，构建雨洪聚类推荐模型，最终实现相似洪水智能推荐。在嘉陵江流域的应用显示，本研究构建的智能推荐模型可以匹配到相似的历史洪水过程，为洪水预报业务提供参考。
Abstract: This paper proposes a similar flood intelligent recommendation model based on historical data of the ba-sin, and achieves the effect of flood forecasting by searching for similar floods according to the numerical precipitation forecasting process of historical typical floods. First, the characteristic indicators of rainstorm floods are selected and dimensionality reduction is performed by principal component analysis method. Then, K-means clustering algorithm is used to classify the historical typical cases of rainstorm floods to construct the rainstorm flood clustering recommendation model, and finally the similar flood intelligent recommendation is realized. The application in the Jialing River basin shows that this method can match similar historical flood processes through the constructed intelligent recommendation model, and provide reference for the operational application of flood forecasting.
文章引用：田逸飞, 杨海从, 纪国良, 王乐, 严方家, 马振亮, 张潇. 相似洪水分析与智能推荐模型研究[J]. 水资源研究, 2023, 12(5): 472-485. https://doi.org/10.12677/JWRR.2023.125052

参考文献

[1] 王浩, 王旭, 雷晓辉, 等. 梯级水库群联合调度关键技术发展历程与展望[J]. 水利学报, 2019, 50(1): 25-37. WANG Hao, WANG Xu, LEI Xiaohui, et al. The development and prospect of key techniques in the cascade reservoir operation. Journal of Hydraulic Engineering, 2019, 50(1): 25-37. (in Chinese)
[2] 周建中, 杨佩瑶, 覃晖, 等. 水库(群)随机优化调度研究进展与展望[J]. 华中科技大学学报(自然科学版), 2022, 50(8): 19-26. ZHOU Jianzhong, YANG Peiyao, QIN Hui, et al. Research progress and prospect of multi-reservoir system stochastic optimal operation. Journal of Huazhong University of Science and Technology (Natural Science Edition), 2022, 50(8): 19-26. (in Chi-nese)
[3] 陈守煜, 薛志春, 李敏. 洪水分类的可变集原理与方法[J]. 中国科学: 技术科学, 2013, 43(11): 1202-1207. CHEN Shouyu, XUE Zhichun and LI Min. Variable sets principle and method for flood classification. Scientia Sinica (Techno-logica). 2013, 43(11): 1202-1207. (in Chinese)
[4] 孟碟, 丁志宏, 冯平. 洪水分类的峰量耦合评价方法[J]. 水利水电技术, 2011, 42(11): 14-16, 37. MENG Die, DING Zhihong and FENG Ping. Method of evaluation on coupling of flood peak with flood volume for flood clas-sification. Water Resources and Hydropower Engineering, 2011, 42(11): 14-16, 37. (in Chinese)
[5] 马寅午, 周晓阳, 尚金成, 等. 长江防洪系统洪水分类预报调度研究[J]. 水电能源科学, 1996, 14(2): 84-92. MA Yinwu, ZHOU Xiaoyang, SHANG Jincheng, et al. Study on flood classified prediction and dispatching method in Yangtze River flood control system. International Journal Hydroelectric Energy, 1996, 14(2): 84-92. (in Chinese)
[6] 彭为, 刘丙军, 廖叶颖, 等. 基于强度与形态指标的洪水分类研究[J]. 水文, 2018, 38(6): 7-11, 76. PENG Wei, LIU Bingjun, LIAO Yeying, et al. Flood classification research based on intensity and morphology indexes. Journal of China Hydrology, 2018, 38(6): 7-11, 76. (in Chinese)
[7] 陈建, 李春红, 王峰, 等. 长江上游流域暴雨洪水相似性判别指标研究[J]. 人民长江, 2019, 50(1): 58-63. CHEN Jian, LI Chunhong, WANG Feng, et al. Study on similarity discriminant index of storm flood in upper reaches of Yangtze River. Yangtze River, 2019, 50(1): 58-63. (in Chinese)
[8] 刘卫林, 董增川, 梁忠民, 等. 暴雨洪水相似性分析及其应用研究[J]. 中国农村水利水电, 2007(2): 132-135, 148. LIU Weilin, DONG Zengchuan, LIANG Zhongmin, et al. Analysis and research on similarity of rainstorm and flood. China Ru-ral Water and Hydropower, 2007(2): 132-135, 148. (in Chinese)
[9] 王本德, 张静. 考虑暴雨成因的大伙房水库洪水分类研究[J]. 水文, 2008(1): 15-20. WANG Bende, ZHANG Jing. Flood classification of Dahuofang Reservoir based on storm genesis. Journal of China Hydrology, 2008(1): 15-20. (in Chinese)
[10] YU, J. R., ZOU, L., XIA, J., et al. Investigating the spatial-temporal changes of flood events across the Yangtze River Basin, China: Identification, spatial heterogeneity, and dominant impact factors. Journal of Hydrology, 2023, 621: 129503.[CrossRef] 
[11] 张艳平, 周惠成. 基于暴雨洪水相似性分析的洪水分类研究[J]. 水电能源科学, 2012, 30(9): 50-54. ZHANG Yanping, ZHOU Huicheng. Study on flood classification based on storm flood similarity analysis. Water Resources and Power, 2012, 30(9): 50-54. (in Chinese)
[12] 张远翔. 聚类分析中的最佳聚类数确定方法研究[D]: [硕士学位论文]. 合肥: 安徽大学, 2020 ZHANG Yuanxiang. Research on the method of determining the optimal number of clusters in cluster analysis. Master’s Thesis, Hefei: Anhui University, 2020. (in Chinese)
[13] 孔令燕, 夏乐天. 混合型模糊聚类分析在洪水分类中的应用[J]. 人民黄河, 2011, 33(1): 31-32, 34. KONG Lingyan, XIA Letian. Application of hybrid fuzzy cluster analysis in flood classification. Yellow River, 2011, 33(1): 31-32, 34. (in Chinese)
[14] 任明磊, 王本德. 基于模糊聚类和BP神经网络的流域洪水分类预报研究[J]. 大连理工大学学报, 2009, 49(1): 121-127. REN Minglei, WANG Bende. Research on classified flood forecast based on fuzzy clustering and BP neural networks. Journal of Dalian University of Technology, 2009, 49(1): 121-127. (in Chinese)
[15] 包为民, 万新宇, 荆艳东, 等. 基于主成分分析的河流洪水系统聚类法[J]. 河海大学学报(自然科学版), 2008, 36(1): 1-5. BAO Weimin, WAN Xinyu, JING Yandong, et al. Flood clustering method based on principal component analysis. Journal of Hohai University (Natural Sciences), 2008, 36(1): 1-5. (in Chinese)
[16] 张灵, 陈晓宏, 翁毅, 等. 人工免疫算法在洪水分类中的应用[J]. 中山大学学报(自然科学版), 2008, 47(5): 110-114. ZHANG Ling, CHEN Xiaohong, WENG Yi, et al. Application of artificial immune network in flood classification. Acta Scien-tiarum Naturalium Universitatis Sunyatseni, 2008, 47(5): 110-114. (in Chinese)
[17] 刘恒. 基于BP神经网络的多因子洪水分类研究[J]. 水利规划与设计, 2020(1): 25-29, 52. LIU Heng. Study on multi factor flood classification based on BP neural network. Water Resources Planning and Design, 2020(1): 25-29, 52. (in Chinese)
[18] 杜晟玮. 人工智能在电力系统中的应用与发展[J]. 大众用电, 2021, 36(9): 30-31. DU Shengwei. Application and development of artificial intelligence in power system. Popular Utilization of Electricity, 2021, 36(9): 30-31. (in Chinese)
[19] 杨挺, 赵黎媛, 王成山. 人工智能在电力系统及综合能源系统中的应用综述[J]. 电力系统自动化, 2019, 43(1): 2-14. YANG Ting, ZHAO Liyuan and WANG Chengshan. Review on application of artificial intelligence in power system and inte-grated energy system. Automation of Electric Power Systems, 2019, 43(1): 2-14. (in Chinese)
[20] 崔雅博, 罗清元, 刘丽娜. 基于改进非线性自回归网络的洪水预测算法[J]. 沈阳工业大学学报, 2023, 45(1): 84-89. CUI Yabo, LUO Qingyuan and LIU Lina. Flood forecasting algorithm based on improved nonlinear auto-regressive network. Journal of Shenyang University of Technology, 2023, 45(1): 84-89. (in Chinese)
[21] 黄坚强, 秦亮曦. 采用ALSTM模型的温度和降雨关联预测研究[J]. 广西大学学报(自然科学版), 2021, 46(4): 1024-1035. HUANG Jianqiang, QIN Liangxi. Study on association prediction of temperature and precipitation using ALSTM. Journal of Guangxi University (Natural Science Edition), 2021, 46(4): 1024-1035. (in Chinese)
[22] 孙一丹, 郭中华, 杨昌智, 等. GNSS信号估算大气可降雨系统原理及应用进展[J]. 中国农业资源与区划, 2022, 43(9): 50-59. SUN Yidan, GUO Zhonghua, YANG Changzhi, et al. Principle and application progress of GNSS signal estimation system for atmospheric rainfall. Chinese Journal of Agricultural Resources and Regional Planning, 2022, 43(9): 50-59. (in Chinese)