黄河堤防管理信息系统研究及应用
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摘要
在黄河流域范围内分布了上万公里的各类堤防,因此,如何实现堤防工程管理信息可视化、智能化,是科学防灾减灾的重要前提。本文以VS.NET和SQL Server技术为基础,结合了WebGIS关键技术,开发了“黄河堤防管理信息系统”。系统实现了B\S环境下数据的显示、查询、统计及组织管理,通过预留接口还可以实现堤防工程实时监测、安全评价、预警预报、专用分析等功能。堤防工程管理信息系统的建立为我国堤防工程建设与管理提供科学、系统、可视化的分析与决策管理工具,为堤防防汛抢险提供技术平台。黄河堤防信息系统的建立实现了黄河流域内各区域堤防工程信息统一管理及共享、对提高办公自动化及电子政务应用、提高防洪与防汛调度水平等有着重大意义。具体研究内容及成果如下:
(1)明确了黄河堤防管理信息系统的重要意义及应用前景。确立了系统目标、系统结构、设计原则、实现途径。利用WebGIS及SQL Server数据库等关键技术进行了堤防管理信息系统开发,保证系统运行的稳定,提供准确的数据,界面友好,操作方便,功能完善,并具有良好的系统维护功能。
(2)建立黄河堤防管理信息系统数据库。根据系统数据库设计原则建立数据库,完成数据库的结构设计、数据分类、实体E-R图的绘制、数据表结构设计及数据库的加载及运行,实现了数据浏览、更新、录入、统计等基本操作功能、图片及多媒体视频显示功能、新闻发布及留言板管理等功能。
(3)黄河堤防信息系统的预警预报模块的研究。利用粗糙集理论对影响堤防岸坡滑移稳定性的影响因素进行约简,并求出权重系数,将基本不起作用的或无关紧要的冗余属性从决策表中删除。再利用约简后的属性集为后续的基于BP神经网络堤防稳定性分级预测提供精简的输入参数,神经网络方法得到了快速而精确的训练。结合现场实际地质调查将神经网络预测结果和经验算法计算结果对比可知,BP神经网络堤防岸坡稳定性预测结果与实际相符,得到了客观真实的结果。基于系统预测出的堤防稳定性评价成果,可为黄河堤防防灾减灾及工程维护提供参考。
There are thousand kilometers various types of leeves within the Yellow River Basin, how to achieve leeve project management information visualization, intelligence is an important prerequisite for scientific disaster prevention and mitigation. In this paper, based on VS.NET and SQL Server technology, combining key technologies of WebGIS, "Yellow River leeve management information system" was developed. The system achieves data display, query, statistics and organizational management under B \ S environment. Real-time monitoring of leeve, safety assessment, early warning and forecasting, specialized analysis functions can also be achieved through the reserved interface. The construction of Yellow River leeve management information system provides a scientific, systematic, visual analysis, decision management tools and technical platform for flood control. The construction of Yellow River leeve management information system achieves unity management, shared informations within regional leeve, improves office automation and e-government applications ,the level of flood control,it plays an important role.
(1) Cleared the significance and application prospects of the leeve management information system in Yellow River. Established system target, system structure, design principles and approach. Using the key development technology of WebGIS and SQL Server database to develope the management information systems of leeves. Ensured the stability of system operation, provide accurate and quick data, friendly interface, easy operation, complete function and good maintenance.
(2) Established the leeve management information system database in Yellow river. According to database design principles established database system, completed structure design of the database, completed data classification, drawed entity E-R, completed structure design of data tables and operation and loading of database, achieved data view, update, entry, statistic and other basic operating functions, images and multimedia video display, press and message board management functions. Descriped the database development knowledge in detail and displayed the database result.
(3) Studied the early warning and forecast in Yellow River leeve management information system. This paper is mainly for classification predictions of the leeve slip stability. Used the rough set theory to reduct impact factors which attribute to leeve slope stability, found the weight coefficients, and removed the redundant or irrelevant attributes from the decision table. Set of properties after reduction provided streamline input parameters for the subsequent leeve stability prediction classification based on neural network, neural network had been rapid and precise training. Combining geological survey of the actual site, comparation of neural network prediction results and experience results showed that, BP neural network leeve slope stability prediction is consistent with the actual situation, which is the objective and true result.Based on the stability grade state of leeves BP neural network predicted, provide reference for the people who charges for disaster prevention and mitigation of the Yellow River leeve and the engineering maintenance.
(1)明确了黄河堤防管理信息系统的重要意义及应用前景。确立了系统目标、系统结构、设计原则、实现途径。利用WebGIS及SQL Server数据库等关键技术进行了堤防管理信息系统开发,保证系统运行的稳定,提供准确的数据,界面友好,操作方便,功能完善,并具有良好的系统维护功能。
(2)建立黄河堤防管理信息系统数据库。根据系统数据库设计原则建立数据库,完成数据库的结构设计、数据分类、实体E-R图的绘制、数据表结构设计及数据库的加载及运行,实现了数据浏览、更新、录入、统计等基本操作功能、图片及多媒体视频显示功能、新闻发布及留言板管理等功能。
(3)黄河堤防信息系统的预警预报模块的研究。利用粗糙集理论对影响堤防岸坡滑移稳定性的影响因素进行约简,并求出权重系数,将基本不起作用的或无关紧要的冗余属性从决策表中删除。再利用约简后的属性集为后续的基于BP神经网络堤防稳定性分级预测提供精简的输入参数,神经网络方法得到了快速而精确的训练。结合现场实际地质调查将神经网络预测结果和经验算法计算结果对比可知,BP神经网络堤防岸坡稳定性预测结果与实际相符,得到了客观真实的结果。基于系统预测出的堤防稳定性评价成果,可为黄河堤防防灾减灾及工程维护提供参考。
There are thousand kilometers various types of leeves within the Yellow River Basin, how to achieve leeve project management information visualization, intelligence is an important prerequisite for scientific disaster prevention and mitigation. In this paper, based on VS.NET and SQL Server technology, combining key technologies of WebGIS, "Yellow River leeve management information system" was developed. The system achieves data display, query, statistics and organizational management under B \ S environment. Real-time monitoring of leeve, safety assessment, early warning and forecasting, specialized analysis functions can also be achieved through the reserved interface. The construction of Yellow River leeve management information system provides a scientific, systematic, visual analysis, decision management tools and technical platform for flood control. The construction of Yellow River leeve management information system achieves unity management, shared informations within regional leeve, improves office automation and e-government applications ,the level of flood control,it plays an important role.
(1) Cleared the significance and application prospects of the leeve management information system in Yellow River. Established system target, system structure, design principles and approach. Using the key development technology of WebGIS and SQL Server database to develope the management information systems of leeves. Ensured the stability of system operation, provide accurate and quick data, friendly interface, easy operation, complete function and good maintenance.
(2) Established the leeve management information system database in Yellow river. According to database design principles established database system, completed structure design of the database, completed data classification, drawed entity E-R, completed structure design of data tables and operation and loading of database, achieved data view, update, entry, statistic and other basic operating functions, images and multimedia video display, press and message board management functions. Descriped the database development knowledge in detail and displayed the database result.
(3) Studied the early warning and forecast in Yellow River leeve management information system. This paper is mainly for classification predictions of the leeve slip stability. Used the rough set theory to reduct impact factors which attribute to leeve slope stability, found the weight coefficients, and removed the redundant or irrelevant attributes from the decision table. Set of properties after reduction provided streamline input parameters for the subsequent leeve stability prediction classification based on neural network, neural network had been rapid and precise training. Combining geological survey of the actual site, comparation of neural network prediction results and experience results showed that, BP neural network leeve slope stability prediction is consistent with the actual situation, which is the objective and true result.Based on the stability grade state of leeves BP neural network predicted, provide reference for the people who charges for disaster prevention and mitigation of the Yellow River leeve and the engineering maintenance.
引文
Chan C C. Rough Set Approach to Attribute Generalization in Data Mining. Information Science, 1998, 107(1-4): 169-176.
Chmielewski MR, Grzymala-Busse JM. Global Discretization of Continuous Attributes as Preprocessing For Machine Llearning. International Journal of Approximate Reasoning, 1996, 15(4): 319-331.
David Geary, Cay Horstmann. Core Java Server Faces [M]. Pears on Edueation, 2005. Hopfield JJ, TANK DW. Computing With Neural Circuits: A model [J]. Science, 1986, 233:625- 633.
Kim D. Data Classification Based on Tolerant Rough Set. Pattern Recognition, 2001, 34(8): 1613- 1624.
Kluwer. Academic Publishers, 1991.1-176.
McCulloch, Warren S. Embodiments of Mind. Cambridge, MA: MIT Press, 1965.
Mssherry D. Knowledge Discovery by Inspection. Decision Support Systems, 1997, 21 (1): 43-37.
Microstructure of Cognition[M]. Cambridge, MA: MIT Press, 1986.
Pawlak Z. Rough Set Approach to Knowledge-based Decision Support. European Journal of Operational Research, 1997, 99(1): 48-57.
Pawlak Z. Rough Sets-Theoretical Aspects of Reasoning About Data [M].
Pawlak Z. Rough sets. International Journal of Computer and Information Science, 1982, 11 (5): 341~356.
Pawlak Z, Grzymala-Busse J, Slowinski R, et al. Rough sets: Communications of the ACM, 1995, 38 (11): 89~95.
Pomerol J C.Artificial Iintelligent and Human Decision Making.European Journal of Operational Research, 1997, 99(1):3~25.
Rumelhartde, Mcclelland J L. Parallel Distributed Processing: Explorations in the Martin T. Hagan,
Howard B. Demuth, Mark H. Beale. Neural Network Design [M].
Warren McCulloch, Walter Pitts. A Logical Calculus of the Ideas Immanent in Nervous Activity. In Bulletin of Mathematical Biophysics, 1943, (5): 115~133.
边馥苓.地理信息系统工程[M].北京:测绘出版社, 1996.
边馥苓. GIS地理信息系统原理和方法[M].北京:测绘出版社, 1996.
曹云.堤防风险管理决策与降低风险措施[M].水科学与工程技术. 2008, 3(02): 64-66.
陈述彭等.地理信息系统导论[M].北京:科学出版社, 1999.
陈于. 3S堤防数据库管理系统的建设与应用[D].中国地质大学, 2004.
崔广才,尹巧珍.一种基于遗传算法的属性约简算法[J].长春理工大学学报. 2003,26(3): 4-6.
代建华,李元香.粗糙集中属性约简的一种启发式遗传算法[J].西安交通大学学报. 2002, 36(12): 1286-1290.
付磊.基于B/S模式的水城水库洪水调度系统的研究与开发[D].昆明理工大学, 2006.
高云.基于.NET的校园WebGIS的研究与实现[D].武汉:华中科技大学, 2006.
郭宏慧.地理信息系统的应用现状和发展趋势分析[J].河北农业科学. 2009, 13(01): 140-142.
郭志达,盛业华,余兆平.地理信息系统基础与应用[M].北京:煤炭工业出版社, 1997.
和明,冯博琴,马兆丰等.一种改进的Rough集属性约简启发式遗传算法[J].西安石油大学学报(自然科学版). 2004,19(3):80-85.
李娜.基于GIS的洪灾风险管理系统[D].中国水利水电科学研究院, 2002.
李有德.堤防工程可视化建设管理系统研究与设计[D].西北农林科技大学, 2008.
刘清. Rough集及Rough推理[M].北京:科学出版社, 2001.1~242.
阙金声.三峡工程涪陵区水库塌岸非线性预测研究[D].吉林大学博士学位论文, 2007.6.
任学惠.遗传算法在粗集属性约简中的应用研究[D].兰州大学硕士学位论文, 2005.5:1~43.
孙晶晶,刘粉林,刘坚强等.基于遗传算法的粗糙集属性最小约简方法[J].计算机科学,2004,31(10A):95-97.
王国胤. Rough集理论与知识获取[M].西安:西安交通大学出版社, 2001. 1~226.
王浩军.基于WEB架构的大坝安全监控管理系统若干关键技术的研究[D].浙江大学, 2005.
王加阳.基于粗糙集理论的动态约简研究[D].中南大学硕士学位论文, 2004.4:1~47.
邬伦,刘瑜,张鼎.地理信息系统———原理、方法和应用[M].北京:科学出版社, 2001.
吴兴征,丁留谦,张金接.堤防安全评估系统的设计与实现[J].人民长江. 2003, 34(06): 55-56.
肖建民.基于Web与GIS技术的黑龙江省防汛指挥信息服务子系统的设计与实现[D].大连理工大学, 2002.
解家毕,张金接,刘舒等.堤防工程信息管理与风险评价系统的研究与开发[J].水利水电技术. 2007, 38(03): 69-72.
徐佩华.基于人工神经网络方法的锦屏一级水电站枢纽区高边坡稳定性分区研究[D].吉林大学博士学位论文,2006.6.
徐光磊.基于Web的水库大坝基本资料管理系统开发研究[D].南京水利科学研究院, 2007.
姚佳.三峡库区重庆段塌岸地质条件调查及塌岸预测[D].成都理工大学硕士学位论文,2006.6:1~141.
张家坤.基于WebGIS的空间信息发布系统的设计与实现[D].湖南:湖南大学,2006.
张瑞军,陈定方,石林等.基于三重模式的长江堤防信息系统的研究与实现[J].武汉理工大
学学报(交通科学与工程版). 2007, 31(01): 27-30.
张文修,吴伟志,梁吉业,等.粗糙集理论与方法[M].北京:科学出版社,2001.
张文修,梁怡,吴伟志.信息系统与知识发现[M].北京:科学出版社,2003.
张文修,姚一豫,梁怡.粗糙集与概念格[M].西安:西安交通大学出版社, 2006.
甄宇峰.基于粗糙集的知识约简算法研究及应用[D].江苏:江苏大学, 2010.
郑航桅.基于B/S结构的珠江堤防工程建设管理信息系统[J].人民珠江. 2001, 6(04): 36-38.
Chmielewski MR, Grzymala-Busse JM. Global Discretization of Continuous Attributes as Preprocessing For Machine Llearning. International Journal of Approximate Reasoning, 1996, 15(4): 319-331.
David Geary, Cay Horstmann. Core Java Server Faces [M]. Pears on Edueation, 2005. Hopfield JJ, TANK DW. Computing With Neural Circuits: A model [J]. Science, 1986, 233:625- 633.
Kim D. Data Classification Based on Tolerant Rough Set. Pattern Recognition, 2001, 34(8): 1613- 1624.
Kluwer. Academic Publishers, 1991.1-176.
McCulloch, Warren S. Embodiments of Mind. Cambridge, MA: MIT Press, 1965.
Mssherry D. Knowledge Discovery by Inspection. Decision Support Systems, 1997, 21 (1): 43-37.
Microstructure of Cognition[M]. Cambridge, MA: MIT Press, 1986.
Pawlak Z. Rough Set Approach to Knowledge-based Decision Support. European Journal of Operational Research, 1997, 99(1): 48-57.
Pawlak Z. Rough Sets-Theoretical Aspects of Reasoning About Data [M].
Pawlak Z. Rough sets. International Journal of Computer and Information Science, 1982, 11 (5): 341~356.
Pawlak Z, Grzymala-Busse J, Slowinski R, et al. Rough sets: Communications of the ACM, 1995, 38 (11): 89~95.
Pomerol J C.Artificial Iintelligent and Human Decision Making.European Journal of Operational Research, 1997, 99(1):3~25.
Rumelhartde, Mcclelland J L. Parallel Distributed Processing: Explorations in the Martin T. Hagan,
Howard B. Demuth, Mark H. Beale. Neural Network Design [M].
Warren McCulloch, Walter Pitts. A Logical Calculus of the Ideas Immanent in Nervous Activity. In Bulletin of Mathematical Biophysics, 1943, (5): 115~133.
边馥苓.地理信息系统工程[M].北京:测绘出版社, 1996.
边馥苓. GIS地理信息系统原理和方法[M].北京:测绘出版社, 1996.
曹云.堤防风险管理决策与降低风险措施[M].水科学与工程技术. 2008, 3(02): 64-66.
陈述彭等.地理信息系统导论[M].北京:科学出版社, 1999.
陈于. 3S堤防数据库管理系统的建设与应用[D].中国地质大学, 2004.
崔广才,尹巧珍.一种基于遗传算法的属性约简算法[J].长春理工大学学报. 2003,26(3): 4-6.
代建华,李元香.粗糙集中属性约简的一种启发式遗传算法[J].西安交通大学学报. 2002, 36(12): 1286-1290.
付磊.基于B/S模式的水城水库洪水调度系统的研究与开发[D].昆明理工大学, 2006.
高云.基于.NET的校园WebGIS的研究与实现[D].武汉:华中科技大学, 2006.
郭宏慧.地理信息系统的应用现状和发展趋势分析[J].河北农业科学. 2009, 13(01): 140-142.
郭志达,盛业华,余兆平.地理信息系统基础与应用[M].北京:煤炭工业出版社, 1997.
和明,冯博琴,马兆丰等.一种改进的Rough集属性约简启发式遗传算法[J].西安石油大学学报(自然科学版). 2004,19(3):80-85.
李娜.基于GIS的洪灾风险管理系统[D].中国水利水电科学研究院, 2002.
李有德.堤防工程可视化建设管理系统研究与设计[D].西北农林科技大学, 2008.
刘清. Rough集及Rough推理[M].北京:科学出版社, 2001.1~242.
阙金声.三峡工程涪陵区水库塌岸非线性预测研究[D].吉林大学博士学位论文, 2007.6.
任学惠.遗传算法在粗集属性约简中的应用研究[D].兰州大学硕士学位论文, 2005.5:1~43.
孙晶晶,刘粉林,刘坚强等.基于遗传算法的粗糙集属性最小约简方法[J].计算机科学,2004,31(10A):95-97.
王国胤. Rough集理论与知识获取[M].西安:西安交通大学出版社, 2001. 1~226.
王浩军.基于WEB架构的大坝安全监控管理系统若干关键技术的研究[D].浙江大学, 2005.
王加阳.基于粗糙集理论的动态约简研究[D].中南大学硕士学位论文, 2004.4:1~47.
邬伦,刘瑜,张鼎.地理信息系统———原理、方法和应用[M].北京:科学出版社, 2001.
吴兴征,丁留谦,张金接.堤防安全评估系统的设计与实现[J].人民长江. 2003, 34(06): 55-56.
肖建民.基于Web与GIS技术的黑龙江省防汛指挥信息服务子系统的设计与实现[D].大连理工大学, 2002.
解家毕,张金接,刘舒等.堤防工程信息管理与风险评价系统的研究与开发[J].水利水电技术. 2007, 38(03): 69-72.
徐佩华.基于人工神经网络方法的锦屏一级水电站枢纽区高边坡稳定性分区研究[D].吉林大学博士学位论文,2006.6.
徐光磊.基于Web的水库大坝基本资料管理系统开发研究[D].南京水利科学研究院, 2007.
姚佳.三峡库区重庆段塌岸地质条件调查及塌岸预测[D].成都理工大学硕士学位论文,2006.6:1~141.
张家坤.基于WebGIS的空间信息发布系统的设计与实现[D].湖南:湖南大学,2006.
张瑞军,陈定方,石林等.基于三重模式的长江堤防信息系统的研究与实现[J].武汉理工大
学学报(交通科学与工程版). 2007, 31(01): 27-30.
张文修,吴伟志,梁吉业,等.粗糙集理论与方法[M].北京:科学出版社,2001.
张文修,梁怡,吴伟志.信息系统与知识发现[M].北京:科学出版社,2003.
张文修,姚一豫,梁怡.粗糙集与概念格[M].西安:西安交通大学出版社, 2006.
甄宇峰.基于粗糙集的知识约简算法研究及应用[D].江苏:江苏大学, 2010.
郑航桅.基于B/S结构的珠江堤防工程建设管理信息系统[J].人民珠江. 2001, 6(04): 36-38.