Shallow Landslide Susceptibility Mapping by Random Forest Base Classifier and its Ensembles in a Semi-Arid Region of Iran

Forests

Marten Geertsema¹  Wei Chen²  John J Clague³  Davood Talebpour Asl⁴  Himan Shahabi⁴  Ataollah Shirzadi⁵  Mohammadtaghi Avand⁶  Shaghayegh Miraki⁷  Baharin Bin Ahmad⁸  Viet-Ha Nhu⁹  Saro Lee^1,10  Binh Thai Pham^1,11  Abolfazl Jaafari^1,12 

[1] British Columbia, Ministry of Forests, Lands, Natural Resource Operations and Rural Development, Prince George, BC V2L 1R5, Canada;College of Geology & Environment, Xi’an University of Science and Technology, Xi’an 710054, China;Department of Earth Sciences, Simon Fraser University 8888 University Drive Burnaby, Burnaby, BC V5A 1S6, Canada;Department of Geomorphology, Faculty of Natural Resources, University of Kurdistan, Sanandaj 66177-15175, Iran;Department of Rangeland and Watershed Management, Faculty of Natural Resources, University of Kurdistan, Sanandaj 66177-15175, Iran;Department of Watershed Management Engineering and Sciences, Faculty of Natural Resources and Marine science, Tarbiat Modares University, Tehran 14115-111, Iran;Department of Watershed Sciences Engineering, Faculty of Natural Resources, University of Agricultural Science and Natural Resources of Sari, Mazandaran 48181-68984, Iran;Faculty of Built Environment and Surveying, Universiti Teknologi Malaysia (UTM), Johor Bahru 81310, Malaysia;Geographic Information Science Research Group, Ton Duc Thang University, Ho Chi Minh City 758307, Vietnam;Geoscience Platform Research Division, Korea Institute of Geoscience and Mineral Resources (KIGAM), 124 Gwahak-ro, Yuseong-gu, Daejeon 34132, Korea;Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam;Research Institute of Forests and Rangelands, Agricultural Research, Education, and Extension Organization (AREEO), Tehran 13185-116, Iran;

关键词: shallow landslide;    machine learning;    goodness-of-fit;    over-fitting;    GIS;    Iran;   

DOI  :  10.3390/f11040421

来源: DOAJ

【 摘 要 】

We generated high-quality shallow landslide susceptibility maps for Bijar County, Kurdistan Province, Iran, using Random Forest (RAF), an ensemble computational intelligence method and three meta classifiers—Bagging (BA, BA-RAF), Random Subspace (RS, RS-RAF), and Rotation Forest (RF, RF-RAF). Modeling and validation were done on 111 shallow landslide locations using 20 conditioning factors tested by the Information Gain Ratio (IGR) technique. We assessed model performance with statistically based indexes, including sensitivity, specificity, accuracy, kappa, root mean square error (RMSE), and area under the receiver operatic characteristic curve (AUC). All four machine learning models that we tested yielded excellent goodness-of-fit and prediction accuracy, but the RF-RAF ensemble model (AUC = 0.936) outperformed the BA-RAF, RS-RAF (AUC = 0.907), and RAF (AUC = 0.812) models. The results also show that the Random Forest model significantly improved the predictive capability of the RAF-based classifier and, therefore, can be considered as a useful and an effective tool in regional shallow landslide susceptibility mapping.

【 授权许可】

Unknown