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• W2912059729 endingPage "368" @default.
• W2912059729 startingPage "368" @default.
• W2912059729 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." @default.
• W2912059729 created "2019-02-21" @default.
• W2912059729 creator A5041093991 @default.
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• W2912059729 date "2019-01-28" @default.
• W2912059729 modified "2023-10-18" @default.
• W2912059729 title "Optimizing the Predictive Ability of Machine Learning Methods for Landslide Susceptibility Mapping Using SMOTE for Lishui City in Zhejiang Province, China" @default.
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• W2912059729 cites W1964096310 @default.
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• W2912059729 cites W1983631475 @default.
• W2912059729 cites W1989158271 @default.
• W2912059729 cites W1992329860 @default.
• W2912059729 cites W2006818562 @default.
• W2912059729 cites W2006968845 @default.
• W2912059729 cites W2008811360 @default.
• W2912059729 cites W2010103528 @default.
• W2912059729 cites W2021765639 @default.
• W2912059729 cites W2023203753 @default.
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• W2912059729 cites W2287278712 @default.
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• W2912059729 cites W2312767325 @default.
• W2912059729 cites W2336394836 @default.
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• W2912059729 doi "https://doi.org/10.3390/ijerph16030368" @default.
• W2912059729 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6388203" @default.
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• W2912059729 hasPublicationYear "2019" @default.
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