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Comparison of Support Vector Machine, Bayesian Logistic Regression, and Alternating Decision Tree Algorithms for Shallow Landslide Susceptibility Mapping along a Mountainous Road in the West of Iran
| Content Provider | MDPI |
|---|---|
| Author | Nhu, Viet-Ha Zandi, Danesh Shahabi, Himan Chapi, Kamran Shirzadi, Ataollah Al-Ansari, Nadhir Singh, Sushant K. Dou, Jie Nguyen, Hoang |
| Copyright Year | 2020 |
| Description | This paper aims to apply and compare the performance of the three machine learning algorithms–support vector machine (SVM), bayesian logistic regression (BLR), and alternating decision tree (ADTree)–to map landslide susceptibility along the mountainous road of the Salavat Abad saddle, Kurdistan province, Iran. We identified 66 shallow landslide locations, based on field surveys, by recording the locations of the landslides by a global position System (GPS), Google Earth imagery and black-and-white aerial photographs (scale 1: 20,000) and 19 landslide conditioning factors, then tested these factors using the information gain ratio (IGR) technique. We checked the validity of the models using statistical metrics, including sensitivity, specificity, accuracy, kappa, root mean square error (RMSE), and area under the receiver operating characteristic curve (AUC). We found that, although all three machine learning algorithms yielded excellent performance, the SVM algorithm (AUC = 0.984) slightly outperformed the BLR (AUC = 0.980), and ADTree (AUC = 0.977) algorithms. We observed that not only all three algorithms are useful and effective tools for identifying shallow landslide-prone areas but also the BLR algorithm can be used such as the SVM algorithm as a soft computing benchmark algorithm to check the performance of the models in future. |
| Starting Page | 5047 |
| e-ISSN | 20763417 |
| DOI | 10.3390/app10155047 |
| Journal | Applied Sciences |
| Issue Number | 15 |
| Volume Number | 10 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2020-07-22 |
| Access Restriction | Open |
| Subject Keyword | Applied Sciences Remote Sensing Shallow Landslides Machine Learning Goodness-of-fit Support Vector Machine Bayesian Logistic Regression Kurdistan Iran |
| Content Type | Text |
| Resource Type | Article |
