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为预测区域生态环境的变化趋势,以北之江流域为例,建立了基于时间序列的GRNN神经网络预测模型,将耕地、化石能源地、草地、建筑用地、林地及水域这6类生物生产性土地面积作为生态足迹影响因素,利用GRNN神经网络对生态足迹影响因素进行预测,通过与灰色预测法和BP神经网络模型进行对比,验证了GRNN神经网络模型具有更高的预测精度,进而利用生态足迹影响因素计算了流域的生态足迹。结果表明,北之江流域的生态足迹在2013~2015年会逐步上升,且生态赤字会不断加剧,因此需对流域进行综合规划和治理。
In order to forecast the change trend of the regional ecological environment, taking the Beijiang River Basin as an example, a forecasting model of GRNN neural network based on time series was established. Six types of biological productivity including cultivated land, fossil fuels, grassland, construction land, forest land and water As an influencing factor of ecological footprint, the paper uses GRNN neural network to predict the influencing factors of ecological footprint. By comparing with gray prediction method and BP neural network model, this paper verifies that GRNN neural network model has higher prediction accuracy, and then uses ecological footprint Influencing factors The ecological footprint of the basin is calculated. The results show that the ecological footprint of the Beijiang River Basin will gradually increase from 2013 to 2015, and the ecological deficit will be aggravated. Therefore, the comprehensive planning and management of the river basin should be carried out.