Prediction of copper contents in the grassland soil based on BPNN

Mining has dramatically damaged the ecological balance of grassland, and the elemental copper is diffusing through dust and surface runoff; This has affected normal production and the enrichment of food chains. Therefore, establishing a fast and easy prediction model for monitoring the content of copper in grassland soil is of great significance. This study was conducted in a mining area in Xilingol grassland and aimed to build a BPNN (BP neural network) to predict the copper content by coupling soil organic matter and pH. The results were: 1) the fitting of the training and test data was first increased and then decreased with the increase in the number of hidden layers, and the fitting reached the maximum at layer 4; 2) the number of nodes in the hidden layers was similar in layers 3, 4, 5, and 7; 3) The BPNN achieved a relatively high fitting and optimal scale when the number of hidden layers was four. Therefore, this method was able to rapidly predict the Cu contents rapidly in the surface soil of grassland.