Next Article in Journal
Mortality and Recovery of Hemlock Woolly Adelgid (Adelges tsugae) in Response to Winter Temperatures and Predictions for the Future
Previous Article in Journal
The Contribution of Traditional Ecological Knowledge and Practices to Forest Management: The Case of Northeast Asia
Article Menu
Issue 12 (December) cover image

Export Article

Open AccessArticle
Forests 2017, 8(12), 498; doi:10.3390/f8120498

Modeling and Predicting Carbon and Water Fluxes Using Data-Driven Techniques in a Forest Ecosystem

1,2
and
1,2,*
1
Key Laboratory of Coalbed Methane Resources and Reservoir Formation Process of Ministry of Education, China University of Mining and Technology, Xuzhou 221116, China
2
School of Resources and Geosciences, China University of Mining and Technology, Xuzhou 221116, China
*
Author to whom correspondence should be addressed.
Received: 30 October 2017 / Revised: 4 December 2017 / Accepted: 8 December 2017 / Published: 12 December 2017
(This article belongs to the Special Issue Water and Gas Exchanges in Forests)
View Full-Text   |   Download PDF [3034 KB, uploaded 12 December 2017]   |  

Abstract

Accurate estimation of carbon and water fluxes of forest ecosystems is of particular importance for addressing the problems originating from global environmental change, and providing helpful information about carbon and water content for analyzing and diagnosing past and future climate change. The main focus of the current work was to investigate the feasibility of four comparatively new methods, including generalized regression neural network, group method of data handling (GMDH), extreme learning machine and adaptive neuro-fuzzy inference system (ANFIS), for elucidating the carbon and water fluxes in a forest ecosystem. A comparison was made between these models and two widely used data-driven models, artificial neural network (ANN) and support vector machine (SVM). All the models were evaluated based on the following statistical indices: coefficient of determination, Nash-Sutcliffe efficiency, root mean square error and mean absolute error. Results indicated that the data-driven models are capable of accounting for most variance in each flux with the limited meteorological variables. The ANN model provided the best estimates for gross primary productivity (GPP) and net ecosystem exchange (NEE), while the ANFIS model achieved the best for ecosystem respiration (R), indicating that no single model was consistently superior to others for the carbon flux prediction. In addition, the GMDH model consistently produced somewhat worse results for all the carbon flux and evapotranspiration (ET) estimations. On the whole, among the carbon and water fluxes, all the models produced similar highly satisfactory accuracy for GPP, R and ET fluxes, and did a reasonable job of reproducing the eddy covariance NEE. Based on these findings, it was concluded that these advanced models are promising alternatives to ANN and SVM for estimating the terrestrial carbon and water fluxes. View Full-Text
Keywords: carbon fluxes; evapotranspiration; forest ecosystem; data-driven techniques; group method of data handling; extreme learning machine; adaptive neuro-fuzzy inference system carbon fluxes; evapotranspiration; forest ecosystem; data-driven techniques; group method of data handling; extreme learning machine; adaptive neuro-fuzzy inference system
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

Dou, X.; Yang, Y. Modeling and Predicting Carbon and Water Fluxes Using Data-Driven Techniques in a Forest Ecosystem. Forests 2017, 8, 498.

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]
Forests EISSN 1999-4907 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top