NDLI: Estimation of Arsenic Contamination in Reclaimed Agricultural Soils Using Reflectance Spectroscopy and ANFIS Model

Content Provider	IEEE Xplore Digital Library
Author	Kun Tan Yuanyuan Ye Qian Cao Peijun Du Jihong Dong
Copyright Year	2008
Abstract	Heavy metal contamination from anthropogenic sources is a threat to human health. To assess the feasibility of predicting surface soil arsenic (As) concentration from hyperspectral reflectance measurement, three different regression algorithms are compared in this paper, i.e., multiple linear regression (MLR), partial least squares regression (PLSR), and adaptive neural fuzzy inference system (ANFIS) modeling. Soil samples were taken from three study sites in mining/agricultural areas after reclamation. As concentration was determined by hydride generation atomic fluorescence spectrometry (HG-AFS) analysis, and the reflectance was measured with an analytical spectral devices (ASD) field spectrometer covering the spectral region of 350-2500 nm. First, after preprocessing of the original reflectance spectroscopy, the correlation coefficients between the As concentration and spectral reflectance measurement were derived. Characteristic bands were then chosen for the quantitative retrieval model. Finally, all of the 30 samples were divided into a calibration set and a validation set of 18 and 12 samples, respectively. When compared with the MLR and PLSR algorithms, the ANFIS model was the best retrieval model, with a coefficient of determination (R²) of 0.94 and a root-mean-square error (RMSE) of 0.88. ANFIS model and reflectance spectroscopy therefore have the potential to map the spatial distribution of As abundance, with the aim of improving public health.
Starting Page	2540
Ending Page	2546
Page Count	7
File Size	1163527
File Format	PDF
ISSN	19391404
Volume Number	7
Issue Number	6
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-01-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Predictive models Correlation Contamination Pollution measurement Metals Soil properties retrieval Adaptive neural fuzzy inference system (ANFIS) analytical spectral devices (ASD) field spectrometer hyperspectral
Content Type	Text
Resource Type	Article
Subject	Atmospheric Science Computers in Earth Sciences

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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