Inversion of Heavy Metal Content in a Copper Mining Area Based on Extreme Learning Machine Optimized by Particle Swarm Algorithm

Abstract

A model to estimate heavy metal content based on spectral analysis can provide the theory and method to rapidly obtain the heavy metal content in leaves. This study established a multiple stepwise regression model for selecting sensitive spectral bands, then used an extreme learning machine model optimized by particle swarm algorithm (PSOELM) to invert the contents of six metals in leaves in the Duobaoshan copper mine area in Heilongjiang Province, China. The results show that the Cu content of some leaves decreased with the distance from the copper mine therefore, the heavy metal content of leaves is related to mineral information. The PSOELM model is superior to both the back propagation model and extreme learning machine models in inversion accuracy and trend analysis.