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Neural Network Based Landscape Pattern Simulation in ChangBai Mountain, Northeast China

机译:基于神经网络的东北长白山景观格局模拟

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Simulation on evolution of landscape pattern is a hot problem because the evolution of terrestrial landscape pattern will be related directly to the changes of climate. In the study, it presents a neural network model for the evolution of landscape pattern by using the landscape pattern transformation rules and parameters. Owing to the typical vertical zoning of vegetation, Mount ChangBai is taken as an example to demonstrate the application of simulation model. Landsat TM data in 1985 and 1999 are combined with the geographic data. The landscape pattern evolution parameters are built and the transformation rules are confirmed by the help of the three layers Back Propagation (BP) Neural Network. There are 16 neural cells for the input layer and 11 cells for the output cell. The evolution of the landscape pattern in 2013 and 2027 are predicted by the model. The precision of the model in 1985 was 84% by taking the year of 1999 as starting point, while the precision of the model in 1999 was 82% by taking the year of 1985 as starting point. The simulation result was very close to actual situation by comparison with Moran I index in 1985 and 1999.
机译:模拟景观格局演变是一个热门问题,因为陆地景观格局的演变将与气候变化直接相关。在研究中,通过使用景观格局转换规则和参数,提出了一种用于景观格局演变的神经网络模型。以典型的垂直植被区划为例,以长白山为例,说明了模拟模型的应用。 1985年和1999年的Landsat TM数据与地理数据结合在一起。建立了景观格局演变参数,并借助三层反向传播(BP)神经网络确定了变换规则。输入层有16个神经元单元,输出单元有11个单元格。该模型预测了2013年和2027年景观格局的演变。以1999年为起点,1985年模型的精度为84%,而以1985年为起点,1999年模型的精度为82%。通过与1985年和1999年的Moran I指数进行比较,模拟结果与实际情况非常接近。

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