MLSEL: A tuned multilayer stacking ensemble learning with meta feature for flood risk prediction
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Abstract
While stacking ensemble methods have been widely adopted for disaster risk classification, most traditional implementations are limited to shallow single-layer architectures, lacking generalization capacity and adaptive meta-feature design. This study proposes Multilayer Stacking Ensemble Learning (MLSEL), a novel approach that addresses these limitations through three key innovations: (i) a deep three-layer stacking architecture, (ii) Meta Feature Augmentation (MFA) to enrich inter-layer representations, and (iii) automated hyperparameter optimization using Optuna to enhance meta-learner performance. The model is evaluated on a multiclass flood risk dataset consisting of 50,000 structured records. Results reveal that the proposed MLSEL particularly the configuration using XGBoost + Optuna + MFA achieves superior accuracy of 98.69%, significantly outperforming both the best-performing baseline and conventional stacking models. This research demonstrates that combining deep-layered learning, meta-feature engineering, and adaptive optimization effectively overcomes overfitting, feature redundancy, and scalability issues inherent in traditional stacking. The MLSEL framework establishes a robust foundation for accurate and reliable disaster risk prediction systems.
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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
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