基于GIS的鄱阳湖区洪灾损失评估系统研究

发布时间：2018-04-11 10:35

本文选题：鄱阳湖区 + 洪灾损失评估　；参考：《江西理工大学》2013年硕士论文

【摘要】：鄱阳湖区是我国洪涝灾害发生较为严重的地区，每年因洪涝灾害所造成的经济损失十分巨大。洪灾损失评估是防洪减灾领域中不可或缺的一部分，如何通过防洪非工程措施，对洪灾损失进行准确评估，这已是摆在我们面前关系到鄱阳湖区经济可持续发展的一项战略任务，越来越受到鄱阳湖区政府部门的高度重视，因此有必要开展基于GIS的鄱阳湖区洪灾损失评估系统研究。首先，论文概述了鄱阳湖区洪灾损失评估系统的研究背景及意义；对鄱阳湖区洪灾损失评估系统进行了需求分析和总体设计，其中需求分析包括数据需求分析、用户需求分析和功能需求分析，总体设计包括开发环境、框架结构和设计模式，设计模式部分搭建了插件式GIS应用框架。其次，对鄱阳湖区洪灾损失评估系统应用模型进行了研究，阐述了应用模型基础GPU和BP神经网络，建立了GPU加速的洪水淹没分析模型和BP神经网络洪灾损失评估模型。然后，对鄱阳湖区洪灾损失评估系统数据库进行了概念模型设计和逻辑模型设计，在此基础上完成了系统空间数据库和属性数据库的建立工作，并通过空间数据库引擎ArcSDE和ADO.NET技术实现了数据与Oracle的连接。最后，以ArcGIS9.3为GIS平台，以Oracle10g为数据库平台，利用.NET平台下的C#编程语言，采用C/S结构，通过ArcGIS Engine组件技术和第三方插件Developer Express开发实现了鄱阳湖区洪灾损失评估系统，实现的主要功能包括基础数据管理、基础工情信息查询与分析、GPU加速确定淹没范围、洪水预报和BP神经网络洪灾损失评估等。鄱阳湖区洪灾损失评估系统建立的基于GPU加速的洪水淹没分析模型能够很好地发挥GPU并行处理与浮点运算的能力，在保证计算精度的前提下快速获取淹没范围，进而向BP神经网络洪灾损失评估模型输入实时洪水淹没范围影响因素，更好地反应洪灾情况，，增强洪灾损失评估的准确性。与此同时，鄱阳湖区洪灾损失评估系统还可以及时准确地向抗洪救灾部门提供准确的灾害情报，为防洪救灾决策提供科学支持服务，能够产生较好的经济效益和社会效益，具有良好推广应用价值。
[Abstract]:Poyang Lake region is a serious area of flood disaster in China, and the economic loss caused by flood disaster is very huge every year.Flood disaster loss assessment is an indispensable part in flood control and disaster reduction field.This is a strategic task related to the sustainable development of economy in Poyang Lake area, which has been paid more and more attention by the government departments in Poyang Lake District. Therefore, it is necessary to carry out the research of flood disaster loss assessment system based on GIS in Poyang Lake region.First of all, the paper summarizes the research background and significance of the Poyang Lake area flood loss assessment system, and carries on the demand analysis and the overall design to the Poyang Lake area flood disaster loss assessment system, in which the demand analysis includes the data demand analysis.The overall design includes development environment, frame structure and design pattern. The part of design pattern builds plug-in GIS application framework.Secondly, the application model of flood damage assessment system in Poyang Lake region is studied. The application model based on GPU and BP neural network is expounded, and the GPU accelerated flood flood flood analysis model and BP neural network flood loss assessment model are established.Then, the conceptual model design and logical model design of flood disaster loss assessment system database in Poyang Lake region are carried out. On this basis, the establishment of system spatial database and attribute database is completed.The connection between data and Oracle is realized by spatial database engine ArcSDE and ADO.NET technology.Finally, using ArcGIS9.3 as GIS platform, Oracle10g as database platform, using C # programming language under. Net platform, adopting C / S structure, using ArcGIS Engine component technology and third party plug-in Developer Express to develop and realize Poyang Lake area flood disaster loss assessment system.The main functions realized include basic data management, information query and analysis of basic information, GPU accelerated determination of inundation range, flood forecast and BP neural network flood loss assessment, and so on.The flood flood flood analysis model based on GPU acceleration established by Poyang Lake area flood damage assessment system can give full play to the ability of GPU parallel processing and floating-point operation, and obtain the inundation range quickly under the premise of guaranteeing the calculation accuracy.Then, the paper inputted the influencing factors to the BP neural network flood loss assessment model, which can better reflect the flood situation and enhance the accuracy of flood loss assessment.At the same time, the Poyang Lake District Flood disaster loss Assessment system can also provide accurate disaster information to flood control and disaster relief departments in time, provide scientific support services for flood control and disaster relief decisions, and can produce better economic and social benefits.It has good application value.
【学位授予单位】：江西理工大学
【学位级别】：硕士
【学位授予年份】：2013
【分类号】：P208;TV87;TP311.52

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