基于大数据的水生态承载力分析模型

随着科学技术的发展，水文信息数据量发生了巨大的增长，如何充分利用这些支持决策的大规模数据，是当前科学家面临一个大问题.传统的水生态承载力分析计算复杂多样，涉及的数据种类多样，扩展性不强，注重于理论研究和分析，本文通过研究历史数据，分析影响水生态承载力的因素，将数据划分为3个指标层，提出一种基于大数据的水生态承载力分析模型（ECCBD）.利用Hadoop集群的HDFS分布式文件系统实现水生态数据的备份存储，利用MapReduce实现海量水生态数据的并行计算.通过输出值与水生态承载力对比，判断水资源是否盈余或赤字，本文提出的方法和模型能够从压力、承载力、弹性力3个不同的指标层有效分析水生态环境现况，对提供水生态保护依据上有重要意义.

Water Ecological Carrying Capacity Analysis Model Based on Big Data

With the development of science and technology, the volume of hydrological information data has increased tremendously, how to make full use of these large-scale data to support decision-making is a big problem for scientists at present. Traditional water ecological carrying capacity analysis and calculation are complex and diverse, involving various types of data, with unsatisfied expansion, and focus on theoretical research and analysis. This work studies historical data, analyzes the factors affecting water ecological carrying capacity, divides the data into three layers, and proposes an analysis model of water Ecological Carrying Capacity based on Big Data (ECCBD). HDFS distributed file system of Hadoop cluster is used to implement the backup and storage of water ecological data, and MapReduce is used to implement the parallel computation of massive water ecological data. By comparing the output value with the water ecological carrying capacity, determining whether the water resources are surplus or deficit, the method and model proposed in this study can effectively analyze the current status of the aquatic environment from three different index layers: pressure, bearing capacity, and elasticity, it is of great significance to provide a basis for water ecological protection.