面向生态系统服务价值的组合预测算法研究

Research on Combination Forecasting Algorithm for Ecosystem Service Value

针对当前生态环境服务价值体系中存在的评估单一性和预测精度不高等问题,提出了三种不同的面向生态系统服务价值的动态组合预测体系,即ARIMA-BP、PCABP-ARIMA及PCABP+ARIMA预测模型,并对西藏自治区的草地、黑龙江省的林地和河南省耕地的生态系统服务价值预测进行实证分析。实验结果表明ARIMA-BP预测模型的平均绝对误差仅为0.87%,PCABP-ARIMA和PCABP+ARIMA预测模型的平均绝对误差分别为1.98%和2.24%,其中ARIMA-BP预测性能最优,相对于传统BP神经网络预测模型误差缩小了62.67%。

In order to solve the problems of single evaluation and low prediction accuracy in the current ecological environment service value system,this paper puts forward three dynamic combination forecasting systems for ecosystem service value,namely ARIMA-BP,PCABP-ARIMA and PCABP ARIMA prediction model,and makes an empirical analysis on the ecosystem service value prediction of grassland in Tibet Autonomous region,forest land in Heilongjiang Province and cultivated land in Henan Province.The experimental results show that the average absolute error of based on ARIMA-BP ecosystem service value prediction model is only 0.87%,and the average absolute error of PCABP-ARIMA and PCABP+ARIMA prediction model is 1.98%and 2.24%respectively.ARIMA-BP is the best,which is 62.67%smaller than the traditional BP neural network prediction model.