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Open AccessArticle

Evaluating the Efficiency of Different Regression, Decision Tree, and Bayesian Machine Learning Algorithms in Spatial Piping Erosion Susceptibility Using ALOS/PALSAR Data

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Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
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Future Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin 64002, Taiwan
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Department of Watershed Management Engineering and Sciences, Faculty in Natural Resources and Marine Science, Tarbiat Modares University, Tehran 14115-111, Iran
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Department of Geography, University of Gour Banga, Malda, West Bengal 732103, India
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Department of Geography, Chandidas Mahavidyalaya, Birbhum, West Bengal 731215, India
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Department of Watershed Management Engineering and Sciences, Faculty in Agriculture and Natural Resources, Tehran University, Tehran 14174-14418, Iran
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Soil Erosion and Degradation Research Group, Department of Geography, Valencia University, Blasco Ibàñez 28, 46010 Valencia, Spain
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Faculty of Civil Engineering, Bauhaus-Universität Weimar, 99423 Weimar, Germany
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Environmental Quality, Atmospheric Science and Climate Change Research Group, Ton Duc Thang University, Ho Chi Minh City, Vietnam
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Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City, Vietnam
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Author to whom correspondence should be addressed.
Land 2020, 9(10), 346; https://doi.org/10.3390/land9100346
Received: 13 August 2020 / Revised: 21 September 2020 / Accepted: 21 September 2020 / Published: 23 September 2020
(This article belongs to the Special Issue Soil Erosion Processes and Rates in Arid and Semiarid Ecosystems)
Piping erosion is one form of water erosion that leads to significant changes in the landscape and environmental degradation. In the present study, we evaluated piping erosion modeling in the Zarandieh watershed of Markazi province in Iran based on random forest (RF), support vector machine (SVM), and Bayesian generalized linear models (Bayesian GLM) machine learning algorithms. For this goal, due to the importance of various geo-environmental and soil properties in the evolution and creation of piping erosion, 18 variables were considered for modeling the piping erosion susceptibility in the Zarandieh watershed. A total of 152 points of piping erosion were recognized in the study area that were divided into training (70%) and validation (30%) for modeling. The area under curve (AUC) was used to assess the effeciency of the RF, SVM, and Bayesian GLM. Piping erosion susceptibility results indicated that all three RF, SVM, and Bayesian GLM models had high efficiency in the testing step, such as the AUC shown with values of 0.9 for RF, 0.88 for SVM, and 0.87 for Bayesian GLM. Altitude, pH, and bulk density were the variables that had the greatest influence on the piping erosion susceptibility in the Zarandieh watershed. This result indicates that geo-environmental and soil chemical variables are accountable for the expansion of piping erosion in the Zarandieh watershed. View Full-Text
Keywords: random forest; support vector machine; Bayesian generalized linear model (Bayesian GLM); machine learning; susceptibility; spatial modeling; piping; erosion; deep learning; natural hazard; geohazard; data science; big data; geoinformatics; hazard mapping random forest; support vector machine; Bayesian generalized linear model (Bayesian GLM); machine learning; susceptibility; spatial modeling; piping; erosion; deep learning; natural hazard; geohazard; data science; big data; geoinformatics; hazard mapping
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MDPI and ACS Style

Band, S.S.; Janizadeh, S.; Saha, S.; Mukherjee, K.; Bozchaloei, S.K.; Cerdà, A.; Shokri, M.; Mosavi, A. Evaluating the Efficiency of Different Regression, Decision Tree, and Bayesian Machine Learning Algorithms in Spatial Piping Erosion Susceptibility Using ALOS/PALSAR Data. Land 2020, 9, 346. https://doi.org/10.3390/land9100346

AMA Style

Band SS, Janizadeh S, Saha S, Mukherjee K, Bozchaloei SK, Cerdà A, Shokri M, Mosavi A. Evaluating the Efficiency of Different Regression, Decision Tree, and Bayesian Machine Learning Algorithms in Spatial Piping Erosion Susceptibility Using ALOS/PALSAR Data. Land. 2020; 9(10):346. https://doi.org/10.3390/land9100346

Chicago/Turabian Style

Band, Shahab S.; Janizadeh, Saeid; Saha, Sunil; Mukherjee, Kaustuv; Bozchaloei, Saeid K.; Cerdà, Artemi; Shokri, Manouchehr; Mosavi, Amirhosein. 2020. "Evaluating the Efficiency of Different Regression, Decision Tree, and Bayesian Machine Learning Algorithms in Spatial Piping Erosion Susceptibility Using ALOS/PALSAR Data" Land 9, no. 10: 346. https://doi.org/10.3390/land9100346

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