کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
569729 876686 2011 13 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Application of machine learning methods to spatial interpolation of environmental variables
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر نرم افزار
پیش نمایش صفحه اول مقاله
Application of machine learning methods to spatial interpolation of environmental variables
چکیده انگلیسی

Machine learning methods, like random forest (RF), have shown their superior performance in various disciplines, but have not been previously applied to the spatial interpolation of environmental variables. In this study, we compared the performance of 23 methods, including RF, support vector machine (SVM), ordinary kriging (OK), inverse distance squared (IDS), and their combinations (i.e., RFOK, RFIDS, SVMOK and SVMIDS), using mud content samples in the southwest Australian margin. We also tested the sensitivity of the combined methods to input variables and the accuracy of averaging predictions of the most accurate methods. The accuracy of the methods was assessed using a 10-fold cross-validation. The spatial patterns of the predictions of the most accurate methods were also visually examined for their validity. This study confirmed the effectiveness of RF, in particular its combination with OK or IDS, and also confirmed the sensitivity of RF and its combined methods to the input variables. Averaging the predictions of the most accurate methods showed no significant improvement in the predictive accuracy. Visual examination proved to be an essential step in assessing the spatial predictions. This study has opened an alternative source of methods for spatial interpolation of environmental properties.


► Random forest and its combined methods are the most accurate methods.
► Random forest is sensitive to the input variables.
► Averaging the most accurate methods may not improve the predictive accuracy.
► Visual examination is essential in assessing the spatial predictions.
► An alternative source of methods for spatial interpolation is developed.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Environmental Modelling & Software - Volume 26, Issue 12, December 2011, Pages 1647–1659
نویسندگان
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