Next Article in Journal
Measuring the Sustainability of Water Plans in Inter-Regional Spanish River Basins
Next Article in Special Issue
Estimation of Groundwater Recharge Using Tracers and Numerical Modeling in the North China Plain
Previous Article in Journal
Is Recovery of Large-Bodied Zooplankton after Nutrient Loading Reduction Hampered by Climate Warming? A Long-Term Study of Shallow Hypertrophic Lake Søbygaard, Denmark
Previous Article in Special Issue
Quantitative Detection and Attribution of Runoff Variations in the Aksu River Basin
Article Menu

Export Article

Open AccessArticle
Water 2016, 8(8), 340; doi:10.3390/w8080340

EMD-RBFNN Coupling Prediction Model of Complex Regional Groundwater Depth Series: A Case Study of the Jiansanjiang Administration of Heilongjiang Land Reclamation in China

1,2,3
,
1,2,3,* , 1
,
1
and
1
1
School of Water Conservancy & Civil Engineering, Northeast Agricultural University, Harbin 150030, China
2
Heilongjiang Provincial Collaborative Innovation Center of Grain Production Capacity Improvement, Northeast Agricultural University, Harbin 150030, China
3
Key Laboratory of High Efficient Utilization of Agricultural Water Resource of Ministry of Agriculture, Northeast Agricultural University, Harbin 150030, China
*
Author to whom correspondence should be addressed.
Academic Editor: Y. Jun Xu
Received: 29 June 2016 / Revised: 4 August 2016 / Accepted: 4 August 2016 / Published: 10 August 2016
(This article belongs to the Special Issue Tackling Complex Water Problems in China under Changing Environment)
View Full-Text   |   Download PDF [2178 KB, uploaded 10 August 2016]   |  

Abstract

The accurate and reliable prediction of groundwater depth is the basis of the sustainable utilization of regional groundwater resources. However, the complexity of the prediction has been ignored in previous studies of regional groundwater depth system analysis and prediction, making it difficult to realize the scientific management of groundwater resources. To address this defect, taking complexity diagnosis as the research foundation, this paper proposes a new coupling forecast strategy for evaluating groundwater depth based on empirical mode decomposition (EMD) and a radial basis function neural network (RBFNN). The data used for complexity analysis and modelling are the monthly groundwater depth series monitoring data from 15 long-term monitoring wells from 1997 to 2007, which were collected from the Jiansanjiang Administration of Heilongjiang Agricultural Reclamation in China. The calculation results of the comprehensive complexity index for each groundwater depth series obtained are based on wavelet theory, fractal theory, and the approximate entropy method. The monthly groundwater depth sequence of District 8 of Farm Nongjiang, which has the highest complexity among the five farms in the Jiansanjiang Administration midland, was chosen as the modelling sample series. The groundwater depth series of District 8 of Farm Nongjiang was separated into five intrinsic mode function (IMF) sequences and a remainder sequence by applying the EMD method, which revealed that local groundwater depth has a significant one-year periodic character and an increasing trend. The RBFNN was then used to forecast and stack each EMD separation sequence. The results suggest that the future groundwater depth will remain at approximately 10 m if the past pattern of water use continues, exceeding the ideal depth of groundwater. Thus, local departments should take appropriate countermeasures to conserve groundwater resources effectively. View Full-Text
Keywords: Jiansanjiang administration; groundwater depth; complexity; empirical mode decomposition; radial basis function neural network Jiansanjiang administration; groundwater depth; complexity; empirical mode decomposition; radial basis function neural network
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Fu, Q.; Liu, D.; Li, T.; Cui, S.; Hu, Y. EMD-RBFNN Coupling Prediction Model of Complex Regional Groundwater Depth Series: A Case Study of the Jiansanjiang Administration of Heilongjiang Land Reclamation in China. Water 2016, 8, 340.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Water EISSN 2073-4441 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top