Evaluation of various boosting ensemble algorithms for predicting flood hazard susceptibility areas

Evaluation of various boosting ensemble algorithms for predicting flood hazard susceptibility areas

The purpose of the present study was to predict the areas affected by flood hazard in the Talar watershed, Mazandaran province, Iran, using Adaptive Boosting (AdaBoost), Boosted Generalized Linear Models (BGLM), Extreme Gradient Boosting (XGB) ensemble models, and the novel ensemble framework of dee...

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Bibliographic Details
Main Authors: Quoc Bao Pham, Subodh Chandra Pal, Rabin Chakrabortty, Akbar Norouzi, Mohammad Golshan, Akinwale T. Ogunrinde, Saeid Janizadeh, Khaled Mohamed Khedher, Duong Tran Anh
Format: Article
Language:English
Published: Taylor & Francis Group 2021-01-01
Series:Geomatics, Natural Hazards & Risk
Subjects:
boosting ensemble model
deep boosting (db)
flood hazard
deep decision tree
talar watershed
Online Access:http://dx.doi.org/10.1080/19475705.2021.1968510

Internet

http://dx.doi.org/10.1080/19475705.2021.1968510

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