TY - GEN
T1 - Predicting soil heavy metal based on Random Forest model
AU - Ma, Weibo
AU - Tan, Kun
AU - Du, Peijun
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/11/1
Y1 - 2016/11/1
N2 - The potential hazard of heavy metals in reclaimed mine soil has been attracted more and more attention. Hyperspectral inversion can be applied to predict the heavy metal content of the soil effectively. Three machine learning methods, Support Vector Machine (SVM), Random Forest (RF) and Extreme Learning Machine (ELM), are introduced in this paper, and then are compared with the Partial Least Squares (PLS) method. With the correlation analysis of heavy metal content and pretreatment spectral band, the models are constructed to predict the content of heavy metal in soil. The results show that the prediction results of machine learning methods are better than PLS, and ELM and RF are better than SVM. Analyzing the stability of the model, it can be found that the concentration of heavy metal samples will affect the prediction of ELM. Meanwhile, the stability of RF is the best than the other three models. RF algorithm has also the highest accuracy in the inversion of soil heavy metal research.
AB - The potential hazard of heavy metals in reclaimed mine soil has been attracted more and more attention. Hyperspectral inversion can be applied to predict the heavy metal content of the soil effectively. Three machine learning methods, Support Vector Machine (SVM), Random Forest (RF) and Extreme Learning Machine (ELM), are introduced in this paper, and then are compared with the Partial Least Squares (PLS) method. With the correlation analysis of heavy metal content and pretreatment spectral band, the models are constructed to predict the content of heavy metal in soil. The results show that the prediction results of machine learning methods are better than PLS, and ELM and RF are better than SVM. Analyzing the stability of the model, it can be found that the concentration of heavy metal samples will affect the prediction of ELM. Meanwhile, the stability of RF is the best than the other three models. RF algorithm has also the highest accuracy in the inversion of soil heavy metal research.
KW - Extreme Learning Machine
KW - Random Forest
KW - hyperspectral inversion
KW - soil heavy metal
UR - https://www.scopus.com/pages/publications/85007413378
U2 - 10.1109/IGARSS.2016.7730129
DO - 10.1109/IGARSS.2016.7730129
M3 - 会议稿件
AN - SCOPUS:85007413378
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 4331
EP - 4334
BT - 2016 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 36th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016
Y2 - 10 July 2016 through 15 July 2016
ER -