吉林大学学报(地球科学版) ›› 2019, Vol. 49 ›› Issue (5): 1477-1485.doi: 10.13278/j.cnki.jjuese.20180208
柳长源1,2, 刘鹏1, 毕晓君2
Liu Changyuan1,2, Liu Peng1, Bi Xiaojun2
摘要: 随着土地开发建设规模不断扩大,土地利用情况也在逐年发生变化,准确预测未来土地利用的发展趋势,可以为本地区的土地利用规划提供依据,提升本地区的土地利用效率。传统方法一般采用CA_Markov、ANN以及CA_ANN模型进行预测,存在训练时间长、预测精度不足和缺乏说服力等问题。本文针对上述问题,结合元胞自动机以及人工神经网络模型,建立一种自适应可变滤镜网络模型,针对特定大小区域内的土地类别数目,创建多类数据集来训练不同参数的多个神经网络,可以成功预测未来土地变化的情况,这样就避免了训练单一网络时数据对网络权值的抵消。相比于传统模型中效果最好的CA_ANN模型,本文建立的自适应可变滤镜网络模型不仅总体精度提高了1%~3%,各种地类转化精度提高了12.82%~33.33%,模型预测时间也缩减了49.47%。
中图分类号:
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