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首页> 外文期刊>Earth Surface Processes and Landforms: The journal of the British Geomorphological Research Group >A comparison of predictive methods in modelling the distribution of periglacial landforms in Finnish Lapland
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A comparison of predictive methods in modelling the distribution of periglacial landforms in Finnish Lapland

机译：芬兰拉普兰冰河地貌分布建模中预测方法的比较

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This study compares the predictive accuracy of eight state-of-the-art modelling techniques for 12 landforms types in a cold environment. The methods used are Random Forest (RF), Artificial Neural Networks (ANN), Generalized Boosting Methods (GBM), Generalized Linear Models (GLM), Generalized Additive Models (GAM), Multivariate Adaptive Regression Splines (MARS), Classification Tree Analysis (CTA) unit Mixture Discriminant Analysis (MDA). The spatial distributions of 12 periglacial landforms types were recorded in sub-Arctic landscape of northern Finland in 2032 grid squares at a resolution of 25 ha. First, three topographic variables were implemented into the eight modelling techniques (simple model), and then six other variables were added (three soil and three vegetation variables; complex model) to reflect the environmental conditions of each grid square. The predictive accuracy was measured by two methods: the area under the curve (AUC) of a receiver operating characteristic (ROC) plot, and the Kappa index (K), based on spatially independent model evaluation data. The mean AUC values of the simple models varied between 0.709 and 0.796, whereas the AUC values of the complex model ranged from 0.725 to 0.825. For both simple and complex models GAM, GLM, ANN and GBM provided the highest predictive performances based on both AUC and kappa values. The results encourage further applications of the novel modelling methods in geomorphology. Copyright (D 2008 John Wiley & Sons, Ltd.
机译：这项研究比较了八种最先进的建模技术在寒冷环境中对12种地貌类型的预测准确性。所使用的方法是随机森林（RF），人工神经网络（ANN），广义提升方法（GBM），广义线性模型（GLM），广义加性模型（GAM），多元自适应回归样条（MARS），分类树分析（ CTA）单元混合判别分析（MDA）。在2032年网格正方形的芬兰北部亚北极景观中，以25公顷的分辨率记录了12种冰缘地貌类型的空间分布。首先，将三种地形变量应用到八种建模技术（简单模型）中，然后添加了六个其他变量（三个土壤和三个植被变量;复杂模型）以反映每个网格正方形的环境条件。预测准确性是通过两种方法测量的：基于空间独立的模型评估数据，接收器工作特性（ROC）图的曲线下面积（AUC）和Kappa指数（K）。简单模型的平均AUC值在0.709至0.796之间变化，而复杂模型的AUC值在0.725至0.825之间。对于简单模型和复杂模型，GAM，GLM，ANN和GBM都基于AUC和kappa值提供了最高的预测性能。结果鼓励了新型建模方法在地貌学中的进一步应用。版权所有（D 2008 John Wiley＆Sons，Ltd.

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