A comparative assessment of support vector regression, artificial neural networks, and random forests for predicting and mapping soil organic carbon stocks across an Afromontane landscape.

作者: Kennedy Were , Dieu Tien Bui , Øystein B. Dick , Bal Ram Singh

DOI: 10.1016/J.ECOLIND.2014.12.028

关键词: Random forestEcologyMean squared errorSpatial ecologyMathematicsSoil organic matterSoil carbonStatisticsDigital soil mappingSampling (statistics)Artificial neural network

摘要: … , evaluate, and compare the performance of random forests (RF), support vector machines … variability of SOC stocks in the Eastern Mau Forest Reserve, Kenya. The distinction between …

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