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

Bibliographic Data

ID15514381
AuthorsYumiao Wang (0000-0002-0980-5353, Wuhan University), Xueling Wu (0000-0002-3808-8381, China University of Geosciences), Zhangjian Chen (0000-0002-3370-5184, Chinese Academy of Surveying and Mapping), Fu Ren (0000-0002-5460-9909, Wuhan University), Luwei Feng (0009-0004-3181-6018, Wuhan University), Qingyun Du (0000-0003-4615-2029, Wuhan University, corresponding author)
Year2019
Volume16
Issue3
Pages368-368
Publication date2019-01-28
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueInternational Journal of Environmental Research and Public Health (JOURNAL)
Journal identifiersISSN: 1661-7827 • E-ISSN: 1660-4601
PublisherMultidisciplinary Digital Publishing Institute (PUBLISHER • CH)
DOI10.3390/ijerph16030368
PMID30696105
OpenAlexW2912059729
LanguageEN
Citations received5
References cited67

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

Artificial neural network · Cartography · Data mining · Geography · Geomorphology · Landslide · Logistic regression · Machine learning · Oversampling · Random forest · Remote sensing · Support vector machine · Terrain · Computer Science · Landslides and related hazards · Soil and Unsaturated Flow · Soil erosion and sediment transport · Artificial Intelligence · Geology

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