Tên bài báo:
A comparison of Support Vector Machines and Bayesian algorithms for landslide susceptibility modelling- Tác giả:
- Phạm Thái Bình
- Tham gia cùng:
- Ngô Quốc Trinh
- Tạp chí:
- Geocarto International
- Năm xuất bản:
- 2018
- Trang:
- Từ trang 1 đến trang 23
- Lĩnh vực:
- Kỹ thuật xây dựng công trình giao thông
- Phạm vi:
- Quốc tế
Tóm tắt:
In this study, the main goal is to compare the predictive capability of Support Vector Machines (SVM) with four Bayesian algorithms namely Naïve Bayes Tree (NBT), Bayes network (BN), Naïve Bayes (NB), Decision Table Naïve Bayes (DTNB) for identifying landslide susceptibility zones in Pauri Garhwal district (India). First, landslide inventory map was built using 1295 historical landslide data, then in total sixteen influencing factors were selected and tested for landslide susceptibility modelling. Performance of the model was evaluated and compared using Statistical based index methods, Area under the Receiver Operating Characteristic (ROC) curve named AUC, and Chi-square method. Analysis results show that that the SVM has the highest prediction capability, followed by the NBT, DTNBT, BN and NB, respectively. Thus, this study confirms that the SVM is one of the benchmark models for the assessment of susceptibility of landslides.
