کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
11012938 | 1797857 | 2018 | 19 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
An Insight into Machine Learning Algorithms to Map the Occurrence of the Soil Mattic Horizon in the Northeastern Qinghai-Tibetan Plateau
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کلمات کلیدی
موضوعات مرتبط
علوم زیستی و بیوفناوری
علوم کشاورزی و بیولوژیک
دانش خاک شناسی
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
Soil diagnostic horizons, which each have a set of quantified properties, play a key role in soil classification. However, they are difficult to predict, and few attempts have been made to map their spatial occurrence. We evaluated and compared four machine learning algorithms, namely, the classification and regression tree (CART), random forest (RF), boosted regression trees (BRT), and support vector machine (SVM), to map the occurrence of the soil mattic horizon in the northeastern Qinghai-Tibetan Plateau using readily available ancillary data. The mechanisms of resampling and ensemble techniques significantly improved prediction accuracies (measured based on area under the receiver operator characteristic curve score (AUC)) and produced more stable results for the BRT (AUC of 0.921 ± 0.012, mean ± standard deviation) and RF (0.908 ± 0.013) algorithms compared to the CART algorithm (0.784 ± 0.012), which is the most commonly used machine learning method. Although the SVM algorithm yielded a comparable AUC value (0.906 ± 0.006) to the RF and BRT algorithms, it is sensitive to parameter settings, which are extremely time-consuming. Therefore, we consider it inadequate for occurrence-distribution modeling. Considering the obvious advantages of high prediction accuracy, robustness to parameter settings, the ability to estimate uncertainty in prediction, and easy interpretation of predictor variables, BRT seems to be the most desirable method. These results provide an insight into the use of machine learning algorithms to map the mattic horizon and potentially other soil diagnostic horizons.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Pedosphere - Volume 28, Issue 5, October 2018, Pages 739-750
Journal: Pedosphere - Volume 28, Issue 5, October 2018, Pages 739-750
نویسندگان
Junjun ZHI, Ganlin ZHANG, Renmin YANG, Fei YANG, Chengwei JIN, Feng LIU, Xiaodong SONG, Yuguo ZHAO, Decheng LI,