Comparison of data mining and allometric model in estimation of tree biomass

BMC Bioinformatics

Research Article

Alexandre Behling¹  Ana P. Dalla Corte¹  Aurélio L. Rodrigues¹  Sylvio Péllico Netto¹  Mateus N. I. Sanquetta¹  Carlos R. Sanquetta¹  Jaime Wojciechowski¹ 

[1] Forest Science Department, Federal University of Paraná, 900 Lothário Meissner Avenue, Curitiba, Paraná, Brazil;

关键词: Accuracy;    Carbon;    Forests;    Instance;    Modeling;   

DOI  :  10.1186/s12859-015-0662-5

 received in 2014-08-14, accepted in 2015-07-03,  发布年份 2015

来源: Springer

【 摘 要 】

BackgroundThe traditional method used to estimate tree biomass is allometry. In this method, models are tested and equations fitted by regression usually applying ordinary least squares, though other analogous methods are also used for this purpose. Due to the nature of tree biomass data, the assumptions of regression are not always accomplished, bringing uncertainties to the inferences. This article demonstrates that the Data Mining (DM) technique can be used as an alternative to traditional regression approach to estimate tree biomass in the Atlantic Forest, providing better results than allometry, and demonstrating simplicity, versatility and flexibility to apply to a wide range of conditions.ResultsVarious DM approaches were examined regarding distance, number of neighbors and weighting, by using 180 trees coming from environmental restoration plantations in the Atlantic Forest biome. The best results were attained using the Chebishev distance, 1/d weighting and 5 neighbors. Increasing number of neighbors did not improve estimates. We also analyze the effect of the size of data set and number of variables in the results. The complete data set and the maximum number of predicting variables provided the best fitting. We compare DM to Schumacher-Hall model and the results showed a gain of up to 16.5 % in reduction of the standard error of estimate.ConclusionIt was concluded that Data Mining can provide accurate estimates of tree biomass and can be successfully used for this purpose in environmental restoration plantations in the Atlantic Forest. This technique provides lower standard error of estimate than the Schumacher-Hall model and has the advantage of not requiring some statistical assumptions as do the regression models. Flexibility, versatility and simplicity are attributes of DM that corroborates its great potential for similar applications.

【 授权许可】

CC BY   
© Sanquetta et al. 2015