Research on Flood Disaster Risk Assessment Based on Random Forest Algorithm

Author:

Cai Hao1

Affiliation:

1. Sichuan University,Chengdu,Sichuan,China,610065

Publisher

IEEE

Reference8 articles.

1. Machine Learning; Reports from University of New Brunswick Advance Knowledge in Machine Learning (Flood Hazard Risk Mapping Using a Pseudo Supervised Random Forest)[J];Robotics & Machine Learning,2020

2. Identification of sensitivity indicators of urban rainstorm flood disasters: A case study in China[J];meimei;Journal of Hydrology,2021

3. Flood hazard mapping in western Iran: assessment of deep learning vis-a-vis machine learning models[J];satarzadeh;Natural Hazards,2021

4. Probability prediction of short-term user-level load based on random forest and kernel density estimation[J];lu;Energy Reports,2022

5. Spatial modeling and susceptibility zonation of landslides using random forest, naïve bayes and K-nearest neighbor in a complicated terrain

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