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

  • 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, duePiping 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.show moreshow less

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  • Gefördert durch das Programm Open Access Publizieren der DFG und den Publikationsfonds der Bauhaus-Universität Weimar.

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Metadaten
Document Type:Article
Author: Shahab S. BandORCiD, Saeid Janizadeh, Sunil SahaORCiD, Kaustuv Mukherjee, Saeid Khosrobeigi Bozchaloei, Artemi CerdàORCiD, Manouchehr ShokriORCiD, Amirhosein MosaviORCiD
DOI (Cite-Link):https://doi.org/10.3390/land9100346Cite-Link
URN (Cite-Link):https://nbn-resolving.org/urn:nbn:de:gbv:wim2-20210122-43424Cite-Link
URL:https://www.mdpi.com/2073-445X/9/10/346
Parent Title (English):Land
Publisher:MDPI
Place of publication:Basel
Language:English
Date of Publication (online):2021/01/21
Date of first Publication:2020/09/23
Release Date:2021/01/22
Publishing Institution:Bauhaus-Universität Weimar
Institutes and partner institutions:Fakultät Bauingenieurwesen / Institut für Strukturmechanik
Volume:2020
Issue:volume 9, issue 10, article 346
Pagenumber:22
First Page:1
Last Page:22
Tag:geoinformatics; random forest; support vector machine
GND Keyword:Maschinelles Lernen; Bayes-Verfahren; Naturkatastrophe
Dewey Decimal Classification:000 Informatik, Informationswissenschaft, allgemeine Werke / 000 Informatik, Wissen, Systeme
BKL-Classification:54 Informatik / 54.72 Künstliche Intelligenz
Open Access Publikationsfonds:Open-Access-Publikationsfonds 2020
Licence (German):License Logo Creative Commons 4.0 - Namensnennung (CC BY 4.0)