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Article

Comparison of Ensemble Machine Learning Methods for Soil Erosion Pin Measurements

1
Department of Civil Engineering, National Taipei University of Technology, Taipei 10608, Taiwan
2
Disaster Prevention Technology Research Center, Sinotech Engineering Consultants, Taipei 11494, Taiwan
3
Faculty of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Bangkok 10520, Thailand
*
Authors to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2021, 10(1), 42; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10010042
Received: 6 December 2020 / Revised: 9 January 2021 / Accepted: 16 January 2021 / Published: 19 January 2021
(This article belongs to the Special Issue Integrating GIS and Remote Sensing in Soil Mapping and Modeling)
Although machine learning has been extensively used in various fields, it has only recently been applied to soil erosion pin modeling. To improve upon previous methods of quantifying soil erosion based on erosion pin measurements, this study explored the possible application of ensemble machine learning algorithms to the Shihmen Reservoir watershed in northern Taiwan. Three categories of ensemble methods were considered in this study: (a) Bagging, (b) boosting, and (c) stacking. The bagging method in this study refers to bagged multivariate adaptive regression splines (bagged MARS) and random forest (RF), and the boosting method includes Cubist and gradient boosting machine (GBM). Finally, the stacking method is an ensemble method that uses a meta-model to combine the predictions of base models. This study used RF and GBM as the meta-models, decision tree, linear regression, artificial neural network, and support vector machine as the base models. The dataset used in this study was sampled using stratified random sampling to achieve a 70/30 split for the training and test data, and the process was repeated three times. The performance of six ensemble methods in three categories was analyzed based on the average of three attempts. It was found that GBM performed the best among the ensemble models with the lowest root-mean-square error (RMSE = 1.72 mm/year), the highest Nash-Sutcliffe efficiency (NSE = 0.54), and the highest index of agreement (d = 0.81). This result was confirmed by the spatial comparison of the absolute differences (errors) between model predictions and observations using GBM and RF in the study area. In summary, the results show that as a group, the bagging method and the boosting method performed equally well, and the stacking method was third for the erosion pin dataset considered in this study. View Full-Text
Keywords: soil erosion; erosion pin; ensemble machine learning; Shihmen Reservoir watershed; bagging; boosting; stacking soil erosion; erosion pin; ensemble machine learning; Shihmen Reservoir watershed; bagging; boosting; stacking
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MDPI and ACS Style

Nguyen, K.A.; Chen, W.; Lin, B.-S.; Seeboonruang, U. Comparison of Ensemble Machine Learning Methods for Soil Erosion Pin Measurements. ISPRS Int. J. Geo-Inf. 2021, 10, 42. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10010042

AMA Style

Nguyen KA, Chen W, Lin B-S, Seeboonruang U. Comparison of Ensemble Machine Learning Methods for Soil Erosion Pin Measurements. ISPRS International Journal of Geo-Information. 2021; 10(1):42. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10010042

Chicago/Turabian Style

Nguyen, Kieu A., Walter Chen, Bor-Shiun Lin, and Uma Seeboonruang. 2021. "Comparison of Ensemble Machine Learning Methods for Soil Erosion Pin Measurements" ISPRS International Journal of Geo-Information 10, no. 1: 42. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10010042

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