Integrating ATR-FTIR and data-driven models to predict total soil carbon and nitrogen towards sustainable watershed management
Küçük Resim Yok
Tarih
2013
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Dr Jyoti Garg
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
The use of Attenuated Total Reflectance (ATR) is an alternative method in determining carbon (C), nitrogen (N) and other elemental contents of organic and inorganic soils for which diffuse reflectance infrared Fourier transform (DRIFT) spectroscopy has been mostly utilized. In this study, the combined use of ATR-Fourier transform infrared (FTIR) spectroscopy and partial least square regression (PLSR) or artificial neural network (ANN) models in estimating total soil C and N have been explored which provide direct, rapid, economical and multiple in situ measurements. Total soil C and N data obtained from 153 soil samples across agricultural lands and analyzed using CNH elemental analyzer were used to build PLSR and ANN models as a function of ATR-FTIR spectrum ranges based on a training dataset with leave-one-out cross validation (LCV) and independent validation (IV) dataset that randomly constitute 67% and 33% of the entire dataset respectively. Wavenumber ranges of 650-2365 cm(-1) and 773-1726 cm(-1) in ATR-FTIR data were selected as predictors for PLSR and ANN models of soil C respectively. PLSR model of soil C led to r(2) = 0.86 for training and r(2) = 0.68 for validation, with PLSR model of soil N as a result of wavenumber range of 1300-1400 cm(-1) leading to r(2) = 0.81 for training and r(2) < 0.1 for validation. Multilayer perceptron model appeared to be the best-performing ANN for the emulations of both total soil C and N and outperformed PLSR model of total soil N.
Açıklama
Anahtar Kelimeler
Artificial Neural network, Environmental Monitoring, Partial Least Square Regression, Soil Management
Kaynak
Research Journal Of Chemistry And Environment
WoS Q Değeri
N/A
Scopus Q Değeri
Q4
Cilt
17
Sayı
6
