岩溶地下水灰色系统分析软件开发

王亚捷; 郝永红; 王学萌; 李华敏; 赵娇娟

doi:10.3969/j.issn.1001-4810.2010.04.007

岩溶地下水灰色系统分析软件开发

doi: 10.3969/j.issn.1001-4810.2010.04.007

1.
天津师范大学计算机与信息工程学院
2.
天津师范大学天津市水环境与水资源重点实验室
3.
山西省农业科学院综合考察所
4.
天津师范大学城市与环境科学学院

基金项目: 国家自然科学基金(40972165,40572150)、天津市自然科学基金09JCYBJC27500、环境地球化学国家重点实验室开放基金

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出版历程
- 收稿日期: 2010-06-09
- 发布日期: 2010-12-25

Grey system analysis software development for karst ground water modeling

1.
College of Computer and Information Engineering, Tianjin Normal University
2.
Key Lab of Water Environmentand Water Resources, Tianjin Normal University
3.
Institute of Agriculture Resources Surveyof Shanxi Province
4.
College of Urbanand Environmental Sciences, Tianjin Normal University

摘要

摘要: 结合灰色系统在岩溶地下水方面的研究进展,从实际应用的角度出发,通过逐步求精的方法,使用VisualC++6.0编程,开发出一套能够满足水文地质工作者使用且操作方便的岩溶地下水灰色系统分析软件。该软件分别由灰色关联分析、数据预处理、GM(1,1)模型、等维递补预测、残差周期模型和GM(1,N)预测模型6个模块组成,具有操作简单便捷、计算结果精确、出错信息处理完善、预测结果直观、数据输入输出方式灵活、残差结果曲线输出及可推广性强等优点。利用该软件对山西柳林泉2008和2009年的泉水流量进行预测检验,GM(1,1)模型的残差周期修正预测精度明显提高,2008年的预测误差由0.32m3/s下降到0.17m3/s,2009年的预测误差由0.29m3/s下降低到0.19m3/s。
- 岩溶地下水 /
- 流量预测 /
- 灰色系统 /
- 软件工程
Abstract: After analyzed recent application of grey system theory in karst hydrology, a grey system modeling software is developed by means of step wise refinement method and VisualC++ 6.0 with the thoughts of software engineering. There are six modules in the software including grey relational analysis, data pretreatment, grey relational analysis, GM(1,1) model, dimension-fixed and recursion-compensated prediction model, GM(1,1) decomposition model, and GM(1,N) model as well. It is object-oriented and easy to operate, and able to meet the needs of hydrologists for groundwater simulation. This paper describes the software's design process, analyzes the functions and implementation of each module, presents the software examination process, and states the software's benefits on the basis of a case study in Liulin Springs, Shanxi, China. AGM(1,1) model is set up for Liulin Springs in light of the spring discharge data from 1971 to 2007, and the model is verified by spring discharge data in 2008 and 2009. The results show that the residuals errors decrease from 0.32 m3/s to 0.17m3/s in 2008 and from 0.29m3/s to 0.19m3/s in 2009 by periodic correction to the residuals.
- karst groundwater /
- discharge prediction /
- grey system /
- software engineering

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