[1]王宇航,岳德鹏*,于强,等. 基于EMD的磴口县地下水埋深动态预测[J].西北林学院学报,2017,32(6):53-59.[doi:10.3969/j.issn.1001-7461.2017.06.08]
 WANG Yu-hang,YUE De-peng*,YU Qiang,et al. Dynamic Prediction of Groundwater Depth in Dengkou County Based on EMD[J].JOURNAL OF NORTHWEST FORESTRY UNIVERSITY,2017,32(6):53-59.[doi:10.3969/j.issn.1001-7461.2017.06.08]
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 基于EMD的磴口县地下水埋深动态预测()
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《西北林学院学报》[ISSN:1001-7461/CN:61-1202/S]

卷:
第32卷
期数:
2017年第6期
页码:
53-59
栏目:
出版日期:
2017-11-30

文章信息/Info

Title:
 Dynamic Prediction of Groundwater Depth in Dengkou County Based on EMD
文章编号:
1001-7461(2017)06-0053-07
作者:
 王宇航岳德鹏*于强张启斌
 (北京林业大学 精准林业北京市重点实验室,北京 100083)
Author(s):
 WANG Yu-hangYUE De-peng*YU QiangZHANG Qi-bin
 (Beijing Key Laboratory of Precision Forestry,Beijing Forestry University,Beijing 100083,China)
关键词:
 EMDBP神经网络地下水埋深预测模型影响因子
Keywords:
 EMD BP neural network groundwater depth prediction model influencing factor
分类号:
S715.3
DOI:
10.3969/j.issn.1001-7461.2017.06.08
文献标志码:
A
摘要:
 以生态脆弱区典型县域磴口县为研究区,利用经验模态分解法(EMD)处理非线性、非稳定性复杂信号的优势,对磴口县1987—2016年共30 a的年均地下水埋深时间序列进行分解,并对分解的结果进行分析;然后将降水量、气温、相对湿度、蒸发量、日照时数5种影响地下水埋深动态变化的因子与地下水埋深进行回归分析,得到其变化规律;把对磴口县地下水埋深影响最为显著的因子作为BP神经网络预测模型输入层的一部分,建立基于EMD的BP预测模型和未经过EMD处理的BP预测模型。结果表明,IMF1、IMF2和IMF3本征模态分量是影响磴口县1987—2016年地下水埋深动态变化的主要原因,方差贡献率之和为95.38%;降水量和蒸发量对磴口县地下水埋深影响最显著,且降水主要集中在秋季和夏季,秋季降水最多;蒸发主要集中在夏季和秋季,夏季蒸发量最大;EMD-BP预测值均方误差(MSE)较小且决定系数(R2)较高,均优于BP预测值,模型预测精度较高,对磴口县地下水埋深预测具有一定的实用性。
Abstract:
 Taking the Dengkou County,a typical county in the ecologically fragile area as the research area,using the empirical mode decomposition method (EMD) to deal with the advantages of non-linear and non-stable complex signals,the average annual groundwater depth in the county during 1987-2016 time series was analyzed.Then,the five factors influencing the dynamic changes of groundwater depth and the depth of groundwater were analyzed by regression analysis of precipitation,temperature,relative humidity,evaporation and sunshine hours.The BP forecasting model based on EMD and the BP forecasting model without EMD were established as part of the BP neural network prediction model input layer,which was the most significant factor affecting the groundwater depth.The results showed that the three principal modal components of IMF1,IMF2 and IMF3 were the main reasons influencing the dynamic changes of groundwater depth,and the sum of variance contribution was 95.38%.The precipitation and evaporation were the most significant factors affecting ground water depth,and the precipitation was mainly concentrated in the autumn and summer,and mostly in the autumn.The evaporation was mainly concentrated in the summer and autumn,the largest in summer.The mean square error (MSE) of the EMD-BP was small.The coefficient of determination (R2) was higher than that of BP forecast,and the prediction accuracy of the model was high,which was of practicality to the prediction of groundwater depth in Dengkou County.

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备注/Memo

备注/Memo:
 收稿日期:2016-12-21修回日期:2017-04-24
基金项目:国家自然科学基金(41371189);国家“十二五”科技支撑计划项目(2012BAD16B00)。
作者简介:王宇航,男,硕士,研究方向:3S技术在生态环境中的应用。E-mail:Jason19921004@foxmail.com
*通信作者:岳德鹏,男,教授,博士生导师,研究方向:景观生态学、土地评价。E-mail:yuedepeng@126.com
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