Neural-XGBoost: A Hybrid Approach for Disaster Prediction and Management Using Machine Learning

Neural-XGBoost: A Hybrid Approach for Disaster Prediction and Management Using Machine Learning

Effective disaster prediction is essential for disaster management and mitigation. This study addresses a multi-classification problem and proposes the Neural-XGBoost disaster prediction model (N-XGB), a hybrid model that combines neural networks (NN) for feature extraction with XGBoost for classifi...

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Bibliographic Details
Main Authors: Muhammad Asim Saleem, Ashir Javeed, Watit Benjapolakul, Wattanasak Srisiri, Surachai Chaitusaney, Pasu Kaewplung
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Disaster prediction
feature extraction
XGBoost
SMOTE
machine learning
Online Access:https://ieeexplore.ieee.org/document/11002470/
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https://ieeexplore.ieee.org/document/11002470/

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