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

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,...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2019-05, Vol.19 (11), p.2444
Main Authors: Tien Bui, Dieu, Shirzadi, Ataollah, Shahabi, Himan, Chapi, Kamran, Omidavr, Ebrahim, Pham, Binh Thai, Talebpour Asl, Dawood, Khaledian, Hossein, Pradhan, Biswajeet, Panahi, Mahdi, Bin Ahmad, Baharin, Rahmani, Hosein, Gróf, Gyula, Lee, Saro
Format: Article
Language:English
Subjects:
Artificial intelligence
Conditioning
ensemble algorithms
Geographic information science
Geomorphology
gully erosion
Hydrologic models
Hydrology
Kurdistan province
Land use
Landslides & mudslides
Lithology
machine learning
Mapping
Model accuracy
Modelling
Natural resources
Polynomials
Soft computing
Soil erosion
Support vector machines
Watershed management
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Summary: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).
ISSN:1424-8220
1424-8220
DOI:10.3390/s19112444