Flood susceptibility modelling using novel hybrid approach of reduced-error pruning trees with bagging and random subspace ensembles-Reference-Cited by-同舟云学术

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Flood susceptibility modelling using novel hybrid approach of reduced-error pruning trees with bagging and random subspace ensembles

Published:2019-08 Issue: Volume:575 Page:864-873
ISSN:0022-1694
Container-title:Journal of Hydrology
language:en
Short-container-title:Journal of Hydrology

Author:

Chen Wei,Hong Haoyuan^ORCID,Li Shaojun,Shahabi Himan,Wang Yi,Wang Xiaojing,Ahmad Baharin Bin

Funder

Chinese Academy of Sciences

National Natural Science Foundation of China

Natural Science Basic Research Program of Shaanxi

China Postdoctoral Science Foundation

Shaanxi Province Postdoctoral Science Foundation

Iran National Science Foundation

Publisher

Elsevier BV

Subject

Water Science and Technology

Reference55 articles.

1. An empirical comparison of voting classification algorithms: bagging, boosting, and variants;Bauer;Mach. Learn.,1999

2. Bagging predictors;Breiman;Mach. Learn.,1996

3. A novel hybrid artificial intelligence approach for flood susceptibility assessment;Chapi;Environ. Modell. Software,2017

4. GIS-based groundwater potential analysis using novel ensemble weights-of-evidence with logistic regression and functional tree models;Chen;Sci. Total Environ.,2018

5. Spatial prediction of groundwater potentiality using ANFIS ensembled with teaching-learning-based and biogeography-based optimization;Chen;J. Hydrol.,2019

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