Factors selection for prediction of carbon flux based on Genetic Algorithm—Neural Network

Selecting the driving factors for carbon cycle is critical step prior predicting carbon dioxide(CO2) flux and it also is the important step to study the machines of carbon cycle.But,how to select the driving factors among the plenty of factors is still a challenge problem.This paper proposes a method of driving factors selection based on correlation analysis, Genetic Algorithm(GA)—Neural Network(NN).The redundant factors are reduced using correlation analysis firstly.And then, GA is used to select the driving factors according the criteria that maximizes the correlation coefficient between the Net Ecosystem Exchange(NEE) observed and the NEE predicted using Radial Basis Function Neural Network(RBFNN) as well as minimizes the number of driving factors.To evaluate the validity of the proposed method,it is used to select the driving factors in predicting CO2 flux of Duke Forest.The experimental results illustrate that the method can mine the main driving factors for predictive CO2 flux effectively without loss of precision.