Hyperspectral Prediction Model of Metal Content in Soil Based on the Genetic Ant Colony Algorithm

Sustainability

卷:11

Guangjie Luo¹  Dequan Zhou²  Jinfeng Wang³  Yujie Yang³  Xiaoyong Bai³  Yuanhong Deng³  Shiqi Tian³  Mingming Wang³  Zeyin Hu³  Qian Lu³  Chaojun Li³  Shijie Wang³ 

[1] Guizhou Provincial Key Laboratory of Geographic State Monitoring of Watershed, Guizhou Education University, Guiyang 550018, Guizhou Province, China;

[2] School of Geography and Environmental Sciences, Guizhou Normal University, Guiyang 550001, Guizhou Province, China;

[3] State Key Laboratory of Environmental Geochemistry, Institute of Geochemistry, Chinese Academy of Sciences, Guiyang 550081, Guizhou Province, China;

关键词: metal concentration;    spectral reflectance;    spatial distribution;    machine learning;   

DOI  :  10.3390/su11113197

来源: DOAJ

【 摘 要 】

The accumulation of metals in soil harms human health through different channels. Therefore, it is very important to conduct fast and effective non-destructive prediction of metals in the soil. In this study, we investigate the characteristics of four metal contents, namely, Sb, Pb, Cr, and Co, in the soil of the Houzhai River Watershed in Guizhou Province, China, and establish the content prediction back propagation (BP) neural network and genetic-ant colony algorithm BP (GAACA-BP) neural network models based on hyperspectral data. Results reveal that the four metals in the soil have different degrees of accumulation in the study area, and the correlation between them is significant, indicating that their sources may be similar. The fitting effect and accuracy of the GAACA-BP model are greatly improved compared with those of the BP model. The R values are above 0.7, the MRE is reduced to between 6% and 15%, and the validation accuracy is increased by 12−64%. The prediction ability of the model of the four metals is Cr > Co > Sb > Pb. These results indicate the possibility of using hyperspectral techniques to predict metal content.

【 授权许可】

Unknown