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Evaluation of the Gray Model GM(1,1) Applied to Soil Particle Distribution
Author(s) -
Wu Cheng-Mau,
Wen Jet-Chau,
Chang Kou-Chiang
Publication year - 2009
Publication title -
soil science society of america journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.836
H-Index - 168
eISSN - 1435-0661
pISSN - 0361-5995
DOI - 10.2136/sssaj2007.0323
Subject(s) - gray (unit) , soil texture , soil science , particle size distribution , mathematics , statistics , environmental science , particle size , geology , soil water , medicine , paleontology , radiology
Particle size distribution (PSD) is a fundamental soil physical property. The conventional approaches for representing PSD use empirical models with two to four parameters. We developed an alternative way to predict PSD that differs from conventional approaches by using the gray model GM(1,1), which does not depend on the model shape as empirical approaches do. The performance of GM(1,1) was compared with Skaggs model by using four statistical criteria. From nine textures of soil samples in our study, the results reveal that the GM(1,1) is superior for making PSD predictions. The results show that for the overall textures, the GM(1,1) model makes better predictions than the Skaggs model except for sand. Therefore, the performance of the GM(1,1) is fairly reliable and efficient and is not affected by soil textures in general.