Modeling flood susceptibility using data-driven approaches of naive bayes tree, alternating decision tree, and random forest methods

• Main Authors: Chen, W., Li, Y., Xue, W., Shahabi, H., Li, S., Hong, H., Wang, X., Bian, H., Zhang, S., Pradhan, B., Ahmad, B. B.
• Format: Article
• Published: Elsevier B. V. 2020
• Subjects: SB469-476 Landcsape architecture
• Online Access: http://eprints.utm.my/id/eprint/86440/; https://dx.doi.org/10.1016/j.scitotenv.2019.134979
• Institution: Universiti Teknologi Malaysia

Floods are one of the most devastating types of disasters that cause loss of lives and property worldwide each year. This study aimed to evaluate and compare the prediction capability of the naïve Bayes tree (NBTree), alternating decision tree (ADTree), and random forest (RF) methods for the spatial...