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Open AccessArticle

A Novel Ensemble Artificial Intelligence Approach for Gully Erosion Mapping in a Semi-Arid Watershed (Iran)

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Geographic Information Science 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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Department of Rangeland and Watershed Management, Faculty of Natural Resources, University of Kurdistan, Sanandaj 66177-15175, Iran
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Department of Geomorphology, Faculty of Natural Resources, University of Kurdistan, Sanandaj 66177-15175, Iran
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Department of Rangeland and Watershed Management, Faculty of Natural Resources and Earth Sciences, University of Kashan, Kashan 87317-53153, Iran
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Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
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Kurdistan Agriculture and Natural Resources Research and Education Center, AREEO, Sanandaj 66169-36311, Iran
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Center for Advanced Modeling and Geospatial System (CAMGIS), Faculty of Engineering and IT, University of Technology Sydney, CB11.06.106, Building 11, 81 Broadway, Ultimo NSW 2007, Australia
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Department of Energy and Mineral Resources Engineering, Choongmu-gwan, Sejong University, 209 Neungdong-ro, Gwangjin-gu, Seoul 05006, Korea
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Department of Geophysics, Young Researchers and Elites Club, North Tehran Branch, Islamic Azad University, Tehran P.O. Box 19585/466, Iran
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Faculty of Built Environment and Surveying, Universiti Teknologi Malaysia (UTM), Johor Bahru 81310, Malaysia
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Department of Computer Science and Engineering, and IT, School of Electrical and Computer Engineering, Shiraz University, Shiraz 84334-71964, Iran
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Department of Energy Engineering, Budapest University of Technology and Economics, Budapest 1111, Hungary
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Geoscience Platform Research Division, Korea Institute of Geoscience and Mineral Resources (KIGAM), 124 Gwahak-ro Yuseong-gu, Daejeon 34132, Korea
15
Department of Geophysical Exploration, Korea University of Science and Technology, 217 Gajeong-ro Yuseong-gu, Daejeon 34113, Korea
*
Authors to whom correspondence should be addressed.
Sensors 2019, 19(11), 2444; https://doi.org/10.3390/s19112444
Received: 19 March 2019 / Revised: 12 May 2019 / Accepted: 18 May 2019 / Published: 29 May 2019
In this study, we introduced a novel hybrid artificial intelligence approach of rotation forest (RF) as a Meta/ensemble classifier based on alternating decision tree (ADTree) as a base classifier called RF-ADTree in order to spatially predict gully erosion at Klocheh watershed of Kurdistan province, Iran. A total of 915 gully erosion locations along with 22 gully conditioning factors were used to construct a database. Some soft computing benchmark models (SCBM) including the ADTree, the Support Vector Machine by two kernel functions such as Polynomial and Radial Base Function (SVM-Polynomial and SVM-RBF), the Logistic Regression (LR), and the Naïve Bayes Multinomial Updatable (NBMU) models were used for comparison of the designed model. Results indicated that 19 conditioning factors were effective among which distance to river, geomorphology, land use, hydrological group, lithology and slope angle were the most remarkable factors for gully modeling process. Additionally, results of modeling concluded the RF-ADTree ensemble model could significantly improve (area under the curve (AUC) = 0.906) the prediction accuracy of the ADTree model (AUC = 0.882). The new proposed model had also the highest performance (AUC = 0.913) in comparison to the SVM-Polynomial model (AUC = 0.879), the SVM-RBF model (AUC = 0.867), the LR model (AUC = 0.75), the ADTree model (AUC = 0.861) and the NBMU model (AUC = 0.811). View Full-Text
Keywords: gully erosion; machine learning; ensemble algorithms; geomorphology; Geographic information science; Kurdistan province gully erosion; machine learning; ensemble algorithms; geomorphology; Geographic information science; Kurdistan province
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MDPI and ACS Style

Tien Bui, D.; Shirzadi, A.; Shahabi, H.; Chapi, K.; Omidavr, E.; Pham, B.T.; Talebpour Asl, D.; Khaledian, H.; Pradhan, B.; Panahi, M.; Bin Ahmad, B.; Rahmani, H.; Gróf, G.; Lee, S. A Novel Ensemble Artificial Intelligence Approach for Gully Erosion Mapping in a Semi-Arid Watershed (Iran). Sensors 2019, 19, 2444. https://doi.org/10.3390/s19112444

AMA Style

Tien Bui D, Shirzadi A, Shahabi H, Chapi K, Omidavr E, Pham BT, Talebpour Asl D, Khaledian H, Pradhan B, Panahi M, Bin Ahmad B, Rahmani H, Gróf G, Lee S. A Novel Ensemble Artificial Intelligence Approach for Gully Erosion Mapping in a Semi-Arid Watershed (Iran). Sensors. 2019; 19(11):2444. https://doi.org/10.3390/s19112444

Chicago/Turabian Style

Tien Bui, Dieu; Shirzadi, Ataollah; Shahabi, Himan; Chapi, Kamran; Omidavr, Ebrahim; Pham, Binh T.; Talebpour Asl, Dawood; Khaledian, Hossein; Pradhan, Biswajeet; Panahi, Mahdi; Bin Ahmad, Baharin; Rahmani, Hosein; Gróf, Gyula; Lee, Saro. 2019. "A Novel Ensemble Artificial Intelligence Approach for Gully Erosion Mapping in a Semi-Arid Watershed (Iran)" Sensors 19, no. 11: 2444. https://doi.org/10.3390/s19112444

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