A Transfer Learning Approach Utilizing Combined Artificial Samples for Improved Robustness of Model to Estimate Heavy Metal Contamination in Soil

Yajin Wang,Bin Zou,Chao Tao

doi:10.1109/access.2020.3025779

Abstract

Benefiting from the nanoscale sampling intervals and subtle spectral information in the visible and near-infrared band, hyperspectral technology is considered as an efficient means for monitoring soil heavy metal contamination whereby the good robustness of prediction model is driven by the increase to spectral dimension in model analysis. Considering the positive correlation between samples size and spectral dimension, we focuses on a novel derivation of enlarging samples size in this study to improve model performance by i) preparing artificial samples taking into account of flexibility and control over the laboratory environment compared with collecting wild samples, and ii) using transfer learning method called transfer component analysis (TCA) for reducing spectral feature differences caused by soil heterogeneity to train model in the same data distribution. The proposed approach was tested on three heavy metals, namely copper (Cu), cadmium (Cd) and lead (Pb), collected in the mining area located in the Xiangjiang Basin, Hunan Province, China. The experiments showed that the initial model constructed by a small number of wild samples performed strong prediction sensitivity as the training samples change. In contrast, a modified model with TCA could showed good robustness with excellent predicted ability, the average prediction accuracy of the determinable coefficient (R2) and the ratio of prediction to deviation (RPD) improved to 0.73 and 1.90, 0.74 and 1.92, 0.72 and 1.73, respectively. The results illustrated there was a more reliable modeling method in potential to predict soil heavy metals based on hyperspectral analysis at low cost.

Highlights

In recent years, the frequent incidents related to excessive heavy metal content in grain have indicated the serious situation of soil contamination around the word [1]–[3]
Considering the high sampling cost in the natural environment, studies on soil heavy metal contamination were usually based on small sample data, which made questionable in the matter of model predictive accuracy and robustness
Three kinds of heavy metals, namely cadmium, copper and lead, were explored experimentally to draw a conclusion: the model trained by a small number of samples did not have good prediction accuracy and robustness

Summary

INTRODUCTION

The frequent incidents related to excessive heavy metal content in grain have indicated the serious situation of soil contamination around the word [1]–[3]. This article takes the mining area located in Xiangjiang River Basin, Hunan Province, China as a case analysis, and regards three different heavy metals included cadmium (Cd), copper (Cu), and lead (Pb) as research examples It aims to: (i) analyze the sensitivity of statistical models to test sets trained by small sample data from wild collection; (ii) compare and contrast the data on soil spectral reflectance in the laboratory and field environment, and reduce the characteristic differences through TCA transformation; (iii) use artificially prepared sample data to spike wild samples to train model after spectral transformation with TCA method; (iv) compare and contrast the prediction accuracy and robustness of models constructed with different training samples to provide a more reliable modeling approach in practical environment. One further point to note was that the decrease of the spectral dimension avoided the redundancy of high-dimensional data in the model calibration

MODEL CALIBRATION AND EVALUATION

RESULTS AND DISCUSSION

CONCLUSION