Universiti Teknologi Malaysia Institutional Repository

A novel ensemble artificial intelligence approach for gully erosion mapping in a semi-arid watershed (Iran)

Dieu, Tien Bui and Shirzadi, Ataollah and Shahabi, Himan and Chapi, Kamran and Omidavr, Ebrahim and Pham, Binh Thai and Asl, Dawood Talebpour and Khaledian, Hossein and Pradhan, Biswajeet and Panahi, Mahdi and Ahmad, Baharin and Rahmani, Hosein and Gróf, Gyula and Lee, Saro (2019) A novel ensemble artificial intelligence approach for gully erosion mapping in a semi-arid watershed (Iran). Sensors (Switzerland), 19 (11). pp. 1-34. ISSN 1424-8220

[img]
Preview
PDF
2MB

Official URL: http://dx.doi.org/10.3390/s19112444

Abstract

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).

Item Type:Article
Uncontrolled Keywords:gully erosion, Kurdistan province, machine learning
Subjects:N Fine Arts > NA Architecture
T Technology > TH Building construction > TH434-437 Quantity surveying
Divisions:Built Environment
ID Code:88715
Deposited By: Yanti Mohd Shah
Deposited On:15 Dec 2020 10:39
Last Modified:15 Dec 2020 10:39

Repository Staff Only: item control page