中国水利

参考文献

[1]张建云,刘九夫,金君良.关于智慧水利的认识与思考[J].水利水运工程学报,2019(6):1-7.

[2]WAN H.Deep learning:Neural network,optimizing method and libraries review[C]//2019International Conference on Robots&Intelligent System(ICRIS).IEEE,2019:497-500.

[3]WANG J X,ZHANG L M,HOU R,et al.Analysis and comparison between digital and smart water conservancy[M]//Geo-Informatics in Resource Management and Sustainable Ecosystem.Berlin,Heidelberg:Springer Berlin Heidelberg,2013:424-434.

[4]CALO R.Artificial Intelligence Policy:A Primer and Roadmap[J].Social ence Electronic Publishing,2017.

[5]GAO J,HUANG X P,ZHANG L L.Comparative analysis between international research hotspots and national-level policy keywords on artificial intelligence in China from 2009 to 2018[J].Sustainability,2019,11(23):6574.

[6]邹蕾,张先锋.人工智能及其发展应用[J].信息网络安全,2012(2):11-13.

[7]LIAO S H.Expert system methodologies and applications—a decade review from 1995 to 2004[J].Expert systems with applications,2005,28(1):93-103.

[8]VOULODIMOS A,DOULAMIS N,DOULAMIS A,et al.Deep learning for computer vision:A brief review[J].Computational intelligence and neuroscience,2018,2018:7068349.

[9]JORDAN M I,MITCHELL T M.Machine learning:Trends,perspectives,and prospects[J].Science(New York,N.Y.),2015,349(6245):255-260.

[10]CHEN X J,JIA S B,XIANG Y.A review:Knowledge reasoning over knowledge graph[J].Expert Systems With Applications,2020,141:112948.

[11]LI J W,CHEN X L,HOVY E H,et al.Visualizingand understanding neural models in NLP[Z/OL]//arXiv[cs.CL].(2015).http://arxiv.org/abs/1506.01066.

[12]张绿原,胡露骞,沈启航,等.水利工程数字孪生技术研究与探索[J].中国农村水利水电,2021(11):58-62.

[13]蒋云钟,冶运涛,赵红莉,等.智慧水利解析[J].水利学报,2021,52(11):1355-1368.

[14]张珂,牛杰帆,李曦,等.洪水预报智能模型在中国半干旱半湿润区的应用对比[J].水资源保护,2021,37(1):28-35+60.

[15]刘家玮,刘波,龚梦星,等.基于决策树算法的山洪灾害三维动态预测模型[J].计算机与数字工程,2018,46(11):2183-2190.

[16]甘甜,郑英,蒋云钟,等.基于DBSCAN-RF洪水分类的洪水预报应用研究[J/OL].水利水电技术(中英文):1-15[2024-01-20].http://kns.cnki.net/kcms/detail/10.1746.TV.20231204.1016.002.html.

[17]TANG Y H,SUN Y,HAN Z Y,et al.Flood forecasting based on machine learning pattern recognition and dynamic migration of parameters[J].Journal of Hydrology:Regional Studies,2023,47.

[18]刘昱辰,刘佳,刘录三,等.基于LSTM实时校正的WRF/WRF-Hydro耦合径流预报[J].水利学报,2023,54(11):1334-1346.

[19]CUI Z,GUO S L,ZHOU Y L,et al.Exploration of dual-attention mechanism-based deep learning for multi-step-ahead flood probabilistic forecasting[J].Journal of Hydrology,2023,622(PB).

[20]段生月,王长坤,张柳艳.基于正则化GRU模型的洪水预测[J].计算机系统应用,2019,28(5):196-201.

[21]崔震,郭生练,汪芸,等.三峡入库洪水概率预报的深度学习模型[J].水力发电学报,2023,42(4):1-10.

[22]GHARAKHANLOU N M,PEREZ L.Flood susceptible prediction through the use of geospatial variables and machine learning methods[J].Journal of Hydrology,2023,617:129121.

[23]LIN K L,CHEN H,ZHOU Y L,et al.Exploring a similarity search-based data-driven framework for multi-step-ahead flood forecasting.[J].The Science of the Total Environment,2023,891:164494.

[24]柯春光.人工神经网络模型在新疆水文预报中的应用[J].水利科学与寒区工程,2023,6(4):105-108.

[25]QIAO G C,YANG M X,WANG H.A Detection Approach for Floating Debris Using Ground Images Based on Deep Learning[J].Remote Sensing,2022,14(17):4161.

[26]WANG S,JIANG S.Research on recognition method of floating objects on water surface based on Mask R-CNN[C]//Fifth International Conference on Artificial Intelligence and Computer Science(AICS2023).SPIE,2023,12803:738-749.

[27]LI D X,LIU H,YANG Y Y.Research on Small Target Detection Technology for River Floating Garbage Based on Improved YOLOv7[C]//Proceedings of the 2023 International Conference on Intelligent Sensing and Industrial Automation,2023:1-9.

[28]LI H,YANG S P,ZHANG R,et al.Detection of Floating Objects on Water Surface Using YOLOv5s in an Edge Computing Environment[J].Water,2023,16(1).

[29]ZHANG L L,XIE Z Q,XU M Q,et al.EYOLOv3:An Efficient Real-Time Detection Model for Floating Object on River[J].Applied Sciences,2023,13(4):2303.

[30]LIN F,HOU T,JIN Q N,et al.Improved YOLO Based Detection Algorithm for Floating Debris in Waterway[J].Entropy,2021,23(9):1111.

[31]李国进,姚冬宜,艾矫燕,等.基于改进Faster R-CNN的水面漂浮物识别与定位[J].信阳师范学院学报(自然科学版),2021,34(2):292-299.

[32]田熊长,蒋水华,贾卓,等.基于KH-SVM的鄱阳湖水质评价及变化趋势分析[J].环境工程,2023,41(1):72-78.

[33]石晴宜,董增川,罗赟,等.基于机器学习方法的洪泽湖入湖水质评价及预测研究[J].中国农村水利水电,2021(12):53-59.

[34]林艳竹,韩忠,黄林显,等.基于随机森林法的弥河-潍河流域地下水质量评价研究[J].水文,2023,43(3):60-64+70.

[35]ZHU Y T,GU C S,ZHAO E F,et al.Structural Safety Monitoring of High Arch Dam UsingImproved ABC-BP Model[J].Mathematical Problems In Engineering,2016,2016:1-9.

[36]WANG S W,SUI X P,LIU Y,et al.Prediction and interpretation of the deformation behaviour of high arch dams based on a measured temperature field[J].Journal of Civil Structural Health Monitoring,2023,13(2-3):661-675.

[37]MATA J,TAVARES DE CASTRO A,SáDA COSTA J.Constructing statistical models for arch dam deformation[J].Structural Control and Health Monitoring,2014,21(3):423-437.

[38]ZHAO E F,WU C Q.Centroid deformation-based nonlinear safety monitoring model for arch dam performance evaluation[J].Engineering Structures,2021,243.

[39]SALAZAR F,TOLEDO M A,ONATE E,et al.Interpretation of dam deformation and leakage with boosted regression trees[J].Engineering Structures,2016,119:230-251.

[40]REN Q B,LI M C,KONG T,et al.Multi-sensor real-time monitoring of dam behavior using selfadaptive online sequential learning[J].Automation in Construction,2022,140.

[41]CHENG L,ZHENG D J.Two online dam safety monitoring models based on the process of extracting environmental effect[J].Advances in Engineering Software,2013,57:48-56.

[42]齐银峰,谭荣建.基于改进粒子群优化算法的BP神经网络在大坝变形分析中的应用[J].水利水电技术,2017,48(2):118-124.

[43]王浩然,钮新强,徐利福,等.基于奇异谱分析和改进WOA-BP的大坝变形预测模型[J].水力发电学报,2023,42(11):136-145.

[44]CAO E H,BAO T F,LIU Y T,et al.A data enhancement-based quadratic imputation framework for consecutive missing values considering spatiotemporal characteristics of dam deformation[J].Journal of Civil Structural Health Monitoring,2023:1-17

[45]KHAN H M.Irrigation Water Demand Forecasting,A Data Pre-Processing and Data Mining Approach based on Spatio-Temporal Data[M]//AustralasianData Mining Conference.2011.

[46]PULIDO-CALVO I,GUTIERREZ-ESTRADA C.Improved irrigation water demand forecasting using a soft-computing hybrid model[J].Biosystems Engineering,2008,102(2):202-218.

[47]GUO X H,Sun X H,MA J J.Prediction of daily crop reference evapotranspiration(ET0)values through a least-squares support vector machine model[J].Hydrology Research,2011,42(4):268-274.

[48]GONZALEZ P R,FERNANDEZ G I,CAMACHO P E,et al.New memory-based hybrid model for middle-term water demand forecasting in irrigated areas[J].Agricultural Water Management,2023,284.

[49]苏楠,章少辉,白美健,等.灌区用水调度的知识图谱模型构建——以淠史杭灌区瓦西干渠灌域为例[J].灌溉排水学报,2023,42(11):112-120.

[50]易汉文,宁芊,赵成萍.灌区水量调度中闸门流量-开度模型研究[J].人民黄河,2019,41(7):158-162.

[51]张成才,马涛,董洪涛,等.基于遗传算法的灌区渠系优化配水模型研究[J].人民黄河,2013,35(3):65-67.

[52]ZHANG F Q,HE C,YAQIONG F,et al.Canal delivery and irrigation scheduling optimization based on crop water demand[J].Agricultural Water Management,2022,260.

[53]FAN Y,CHEN H R,GAO Z Y,et al.Water distribution and scheduling model of an irrigation canal system[J].Computers and Electronics in Agriculture,2023,209.

[54]韩宇,邵梦璇,李彤姝,等.河套灌区渠系优化配水模型应用研究[J].排灌机械工程学报,2022,40(10):999-1006.

[55]LIAO X C,MAHMOUD A,HU T S,et al.A novel irrigation canal scheduling model adaptable to the spatial-temporal variability of water conveyance loss[J].Agricultural Water Management,2022,274.

[56]刘照,程帅,李华朋,等.基于双层粒子群算法的下级渠道流量不等时渠系优化配水[J].干旱地区农业研究,2017,35(3):88-93+237.