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GM(0,N)模型在湿地面积预测中的应用——以莫莫格湿地为例
引用本文:史文杰,李昱,刘学智,张小丽,张弛.GM(0,N)模型在湿地面积预测中的应用——以莫莫格湿地为例[J].南水北调与水利科技,2017,15(6):101-107.
作者姓名:史文杰  李昱  刘学智  张小丽  张弛
作者单位:1. 大连理工大学建设工程学部水利工程学院,辽宁,大连,116024;2. 广州珠科院工程勘察设计有限公司, 广州, 510611
基金项目:水利部公益性行业科研专项项目(201401014-2);国家自然科学基金(51279021)
摘    要:预测未来年度湿地面积,对研究湿地生境变化趋势、保护湿地有重要作用。建立灰度GM(0,N)模型旨在提供一种简便的方法,预测湿地水面面积大小。首先应用灰色关联度分析模型,量化确定了对湿地面积影响程度较大的相关因素,分别是:莫莫格湿地年降水量、嫩江径流量、洮儿河径流量。利用这三个相关因素建立了GM(0,N)预测模型,对莫莫格湿地面积进行了模拟预测。为了提高精度,对GM(0,N)模型进行了修正。利用残差和后验差检验方法对模型作了可靠度分析,检测结果显示:修正的GM(0,N)模型平均相对误差9.1%,后验差检验等级为1级,多元线性回归模型平均相对误差15.5%,说明灰度预测模型对于莫莫格湿地水面面积预测具有一定优势。

关 键 词:GM(0  N)模型  灰色关联度分析  湿地面积  预测  残差

Application of GM(0, N) model to prediction of wetland area: A case study on Momoge wetland
SHI Wen-jie,LI Yu,LIU Xue-zhi,ZHANG Xiao-li,ZHANG Chi.Application of GM(0, N) model to prediction of wetland area: A case study on Momoge wetland[J].South-to-North Water Transfers and Water Science & Technology,2017,15(6):101-107.
Authors:SHI Wen-jie  LI Yu  LIU Xue-zhi  ZHANG Xiao-li  ZHANG Chi
Affiliation:1. Faculty of Infrastructure Engineering, Dalian University of Technology, Dalian 116024, China; 2. The Pearl River Hydraulic Research Institute Surveying & Designing Co., Ltd., Guangzhou, 510611, China
Abstract:The prediction of wetland area in the coming years is critical for studying the change trends of the wetland habitat and preserving the wetlands. We developed a grey GM(0, N) model as a simple and convenient method to predict the water surface area of wetlands. First, we used the grey relational analysis model to quantitatively determine the main correlative factors that greatly influenced the wetland area. They were: annual rainfall of Momoge wetland, runoff volume of Nen River, and runoff volume of Taoer River. These three factors were used to establish the GM(0, N) model to predict the area of Momoge wetland. To improve the prediction precision, we modified the GM(0, N) model. Then we conducted the residual test and posterior variance test to evaluate the reliability of the model. The average relative error of the modified GM(0, N) model was 9.1%, and the posterior variance test grade was Grade 1, while the average relative error of the multiple linear regressive model was 15.5%. This suggests that the modified GM(0, N) model has an advantage in a practical application.
Keywords:GM(0  N) model  grey relational analysis  wetland area  prediction  residual
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