Comparison of Three Supervised Learning Methods for Digital Soil Mapping : Application to a Complex Terrain in the Ecuadorian Andes
المؤلفون المشاركون
المصدر
Applied and Environmental Soil Science
العدد
المجلد 2014، العدد 2014 (31 ديسمبر/كانون الأول 2014)، ص ص. 1-12، 12ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2014-05-20
دولة النشر
مصر
عدد الصفحات
12
التخصصات الرئيسية
الملخص EN
A digital soil mapping approach is applied to a complex, mountainous terrain in the Ecuadorian Andes.
Relief features are derived from a digital elevation model and used as predictors for topsoil texture classes sand, silt, and clay.
The performance of three statistical learning methods is compared: linear regression, random forest, and stochastic gradient boosting of regression trees.
In linear regression, a stepwise backward variable selection procedure is applied and overfitting is controlled by minimizing Mallow’s Cp.
For random forest and boosting, the effect of predictor selection and tuning procedures is assessed.
100-fold repetitions of a 5-fold cross-validation of the selected modelling procedures are employed for validation, uncertainty assessment, and method comparison.
Absolute assessment of model performance is achieved by comparing the prediction error of the selected method and the mean.
Boosting performs best, providing predictions that are reliably better than the mean.
The median reduction of the root mean square error is around 5%.
Elevation is the most important predictor.
All models clearly distinguish ridges and slopes.
The predicted texture patterns are interpreted as result of catena sequences (eluviation of fine particles on slope shoulders) and landslides (mixing up mineral soil horizons on slopes).
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Hitziger, Martin& Ließ, Mareike. 2014. Comparison of Three Supervised Learning Methods for Digital Soil Mapping : Application to a Complex Terrain in the Ecuadorian Andes. Applied and Environmental Soil Science،Vol. 2014, no. 2014, pp.1-12.
https://search.emarefa.net/detail/BIM-499832
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Hitziger, Martin& Ließ, Mareike. Comparison of Three Supervised Learning Methods for Digital Soil Mapping : Application to a Complex Terrain in the Ecuadorian Andes. Applied and Environmental Soil Science No. 2014 (2014), pp.1-12.
https://search.emarefa.net/detail/BIM-499832
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Hitziger, Martin& Ließ, Mareike. Comparison of Three Supervised Learning Methods for Digital Soil Mapping : Application to a Complex Terrain in the Ecuadorian Andes. Applied and Environmental Soil Science. 2014. Vol. 2014, no. 2014, pp.1-12.
https://search.emarefa.net/detail/BIM-499832
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-499832
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر