Optimizing the Predictive Ability of Machine Learning Methods for Landslide Susceptibility Mapping Using SMOTE for Lishui City in Zhejiang Province, China

Yumiao Wang, Xueling Wu, Zhangjian Chen, Fu Ren, Luwei Feng and Qingyun Du
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Yumiao Wang: School of Resources and Environmental Science, Wuhan University, Wuhan 430079, China
Xueling Wu: Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China
Zhangjian Chen: Zhejiang Academy of Surveying and Mapping, Hangzhou 310012, China
Fu Ren: School of Resources and Environmental Science, Wuhan University, Wuhan 430079, China
Luwei Feng: School of Resources and Environmental Science, Wuhan University, Wuhan 430079, China
Qingyun Du: School of Resources and Environmental Science, Wuhan University, Wuhan 430079, China

IJERPH, 2019, vol. 16, issue 3, 1-27

Abstract: The main goal of this study was to use the synthetic minority oversampling technique (SMOTE) to expand the quantity of landslide samples for machine learning methods (i.e., support vector machine (SVM), logistic regression (LR), artificial neural network (ANN), and random forest (RF)) to produce high-quality landslide susceptibility maps for Lishui City in Zhejiang Province, China. Landslide-related factors were extracted from topographic maps, geological maps, and satellite images. Twelve factors were selected as independent variables using correlation coefficient analysis and the neighborhood rough set (NRS) method. In total, 288 soil landslides were mapped using field surveys, historical records, and satellite images. The landslides were randomly divided into two datasets: 70% of all landslides were selected as the original training dataset and 30% were used for validation. Then, SMOTE was employed to generate datasets with sizes ranging from two to thirty times that of the training dataset to establish and compare the four machine learning methods for landslide susceptibility mapping. In addition, we used slope units to subdivide the terrain to determine the landslide susceptibility. Finally, the landslide susceptibility maps were validated using statistical indexes and the area under the curve (AUC). The results indicated that the performances of the four machine learning methods showed different levels of improvement as the sample sizes increased. The RF model exhibited a more substantial improvement (AUC improved by 24.12%) than did the ANN (18.94%), SVM (17.77%), and LR (3.00%) models. Furthermore, the ANN model achieved the highest predictive ability (AUC = 0.98), followed by the RF (AUC = 0.96), SVM (AUC = 0.94), and LR (AUC = 0.79) models. This approach significantly improves the performance of machine learning techniques for landslide susceptibility mapping, thereby providing a better tool for reducing the impacts of landslide disasters.

Keywords: landslide susceptibility; Lishui City; machine learning; SMOTE; slope units; neighborhood rough set theory (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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