TY  - JOUR
A1  - Band, Shahab S.
A1  - Janizadeh, Saeid
A1  - Saha, Sunil
A1  - Mukherjee, Kaustuv
A1  - Khosrobeigi Bozchaloei, Saeid
A1  - Cerdà, Artemi
A1  - Shokri, Manouchehr
A1  - Mosavi, Amir Hosein
T1  - Evaluating the Efficiency of Different Regression, Decision Tree, and Bayesian Machine Learning Algorithms in Spatial Piping Erosion Susceptibility Using ALOS/PALSAR Data
JF  - Land
N2  - 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.
KW  - Maschinelles Lernen
KW  - Bayes-Verfahren
KW  - Naturkatastrophe
KW  - random forest
KW  - support vector machine
KW  - geoinformatics
KW  - OA-Publikationsfonds2020
Y1  - 2020
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20210122-43424
UR  - https://www.mdpi.com/2073-445X/9/10/346
VL  - 2020
IS  - volume 9, issue 10, article 346
SP  - 1
EP  - 22
PB  - MDPI
CY  - Basel
ER  -