项目名称: 基于DEM的洞庭湖流域河网特征分析与数据挖掘问题研究
项目编号: No.51479215
项目类型: 面上项目
立项/批准年度: 2015
项目学科: 水利工程
项目作者: 戴斌祥
作者单位: 中南大学
项目金额: 82万元
中文摘要: DEM是地表形态高程属性的数字化表达,利用流域DEM数据构建数字水文模型并提取流域水文特征,是分布式水文过程模拟的重要基础。本项目的主要工作是:1、寻找一种能够实现河网自动分级且反映汇流关系的分级方法和能够反映河网级别的自动编码方法,建立水系 自动分级数据模型。2、针对DEM栅格数据的特点,提出基于DEM的科学可行的数据挖掘的提取算法,并根据挖掘算法确定和开发相应的数据挖掘系统平台工具。3、开展对河网形态特征的量化研究,采用多个参数综合刻画河网形态,建立新的能够真实反映河流分形特征的河网分形维数的计算方法。4、利用洞庭湖流域的DEM数据,实现洞庭湖流域DEM数据的处理和河网的提取,得到洞庭湖流域对应的河网参数,建立洞庭湖数字流域。通过本项目的研究,能够极大地推动河网形态理论研究的发展,对河床演变、数字流域和流域地貌的研究有着积极的贡献,并可以为洞庭湖流域水文预报和水资源管理提供重要的参考。
中文关键词: 洞庭湖流域;河网特征;分形理论;DEM;数据挖掘
英文摘要: Digital elevation model (DEM), is a digital model or 3D representation of a terrain's surface. Using the DEM data collected in a terrain's surface to construct a mathematical model and to analyze the extraction of hydrological characteristics of a river basin are very important foundation for simulation of distributed hydrological process in a catchment.The main goals include: 1. Create a model to seek for elevative and automatic methods in order to realize the pattern formation and to extract the feature of drainage network and watersheds; 2. Provide a suitable selective algorithm to extract hydrological characteristics efficiently and design a software package for automated drainage network extraction, based on DEM grid property; 3. Analyze the distribution of watershed characteristics qualitatively, establish the computational methods to accurately reflect the pattern of the hydrological process by extracting multiple terrain parameters; 4. Complete the data process based on DEM applied in Dongting watershed, provide the digital terrain in Dongting river and obtain the corresponding terrain parameters.The research in this proposed project will promote the development in the area of Dongting river, extract the terrain parameters efficiently and model water flow or mass movement geographically. Our contribution will give valuable information to predict and manage the terrain analysis in geomorphology and physical geography around Dongting river.
英文关键词: Dongting Lake;River network characteristics;Fractal theory;DEM;Date mining