Hyperspectral estimation of petroleum hydrocarbon content in soil using ensemble learning method and LASSO feature extraction
Menghong Wu, Nan Lin, Genjun Li, Hanlin Liu, Delin Li
Abstract
Rapid and accurate estimation of soil petroleum hydrocarbon content is crucial for analyzing the degree of soil pollution and evaluating pollution status. Surface soil samples and hyperspectral measurements in the reservoir area in Lenghu Town, Qinghai Province, China, were viewed as research objects, and the correlation between different spectral forms of original data and petroleum hydrocarbon content in soil was analyzed. To improve the estimation accuracy, we proposed a solution that introduces least absolute shrinkage and selection operator (LASSO) combined with extremely randomized trees (ERT) and gradient boosting decision tree (GBDT) ensemble learning for constructing hyperspectral estimation model. The results show: LASSO algorithm can not only solve the spectral multicollinearity problem effectively but also reduce the number and calculation complexity of soil hyperspectral variables to a great extent. Compared with traditional machine learning, ERT and GBDT perform superior. In particular, the estimation accuracy of the LASSO-GBDT model is the highest.