Name
Title | ||
|---|---|---|
Estimation of Arsenic Contamination in Reclaimed Agricultural Soils Using Reflectance Spectroscopy and ANFIS Model |
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. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/JSTARS.2014.2311471 | Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of |
Keywords | Field | DocType |
soil pollution,anfis model,asd field spectrometer,hg-afs analysis,mlr algorithms,plsr algorithms,rmse,adaptive neural fuzzy inference system,analytical spectral devices,anthropogenic sources,arsenic contamination estimation,heavy metal contamination,human health,hydride generation atomic fluorescence spectrometry,hyperspectral reflectance measurement,multiple linear regression,original reflectance spectroscopy,partial least squares regression,public health,quantitative retrieval model,reclaimed agricultural soils,reflectance spectroscopy model,root-mean-square error,soil samples,spectral region,surface soil arsenic concentration,adaptive neural fuzzy inference system (anfis),analytical spectral devices (asd) field spectrometer,hyperspectral,retrieval,contamination,root mean square error,predictive models,metals,correlation | Remote sensing,Partial least squares regression,Mean squared error,Coefficient of determination,Adaptive neuro fuzzy inference system,Mathematics,Calibration,Soil test,Soil water,Linear regression | Journal |
Volume | Issue | ISSN |
7 | 6 | 1939-1404 |
Citations | PageRank | References |
1 | 0.36 | 2 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kun Tan | 1 | 39 | 12.15 |
| yuanyuan ye | 2 | 1 | 0.70 |
| qian cao | 3 | 4 | 3.58 |
| Du Peijun | 4 | 488 | 45.77 |
| jihong dong | 5 | 2 | 1.06 |