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

Shahab S. Band, Saeid Janizadeh, Sunil Saha, Kaustuv Mukherjee, Saeid Khosrobeigi Bozchaloei, Artemi Cerdà, Manouchehr Shokri and Amirhosein Mosavi
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Shahab S. Band: Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
Saeid Janizadeh: Department of Watershed Management Engineering and Sciences, Faculty in Natural Resources and Marine Science, Tarbiat Modares University, Tehran 14115-111, Iran
Sunil Saha: Department of Geography, University of Gour Banga, Malda, West Bengal 732103, India
Kaustuv Mukherjee: Department of Geography, Chandidas Mahavidyalaya, Birbhum, West Bengal 731215, India
Saeid Khosrobeigi Bozchaloei: Department of Watershed Management Engineering and Sciences, Faculty in Agriculture and Natural Resources, Tehran University, Tehran 14174-14418, Iran
Artemi Cerdà: Soil Erosion and Degradation Research Group, Department of Geography, Valencia University, Blasco Ibàñez 28, 46010 Valencia, Spain
Manouchehr Shokri: Faculty of Civil Engineering, Bauhaus-Universität Weimar, 99423 Weimar, Germany
Amirhosein Mosavi: Environmental Quality, Atmospheric Science and Climate Change Research Group, Ton Duc Thang University, Ho Chi Minh City, Vietnam

Land, 2020, vol. 9, issue 10, 1-23

Abstract: 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.

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 (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2020
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