Enhancing deep learning for demand forecasting to address large data gaps

Riachy, Chirine; He, Mengda; Joneidy, Sina; Qin, Shengchao; Payne, Tim; Boulton, Graeme; Occhipinti, Annalisa; Angione, Claudio

doi:10.1016/j.eswa.2024.126200

Expert Systems with Applications

2025

DOI: 10.1016/j.eswa.2024.126200

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Enhancing deep learning for demand forecasting to address large data gaps

Chirine Riachy,

Mengda He,

Sina Joneidy

et al.

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2025

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Predicting Earthquake Casualties and Emergency Supplies Needs Based on PCA-BO-SVM

Wang,

Xu,

et al. 2025

Systems

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The prediction of casualties in earthquake disasters is a prerequisite for determining the quantity of emergency supplies needed and serves as the foundational work for the timely distribution of resources. In order to address challenges such as the large computational workload, tedious training process, and multiple influencing factors associated with predicting earthquake casualties, this study proposes a Support Vector Machine (SVM) model utilizing Principal Component Analysis (PCA) and Bayesian Optimization (BO). The original data are first subjected to dimensionality reduction using PCA, with principal components contributing cumulatively to over 80% selected as input variables for the SVM model, while earthquake casualties are designated as the output variable. Subsequently, the optimal hyperparameters for the SVM model are obtained using the Bayesian Optimization algorithm. This approach results in the development of an earthquake casualty prediction model based on PCA-BO-SVM. Experimental results indicate that compared to the GA-SVM model, the BO-SVM model, and the PCA-GA-SVM model, the PCA-BO-SVM model exhibits a reduction in average error rates by 12.86%, 9.01%, and 2%, respectively, along with improvements in average accuracy and operational efficiency by 10.1%, 7.05%, and 0.325% and 25.5%, 18.4%, and 19.2%, respectively. These findings demonstrate that the proposed PCA-BO-SVM model can effectively and scientifically predict earthquake casualties, showcasing strong generalization capabilities and high predictive accuracy.

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Predicting Earthquake Casualties and Emergency Supplies Needs Based on PCA-BO-SVM

Wang,

Xu,

et al. 2025

Systems

View full text Add to dashboard Cite

show abstract

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Enhancing deep learning for demand forecasting to address large data gaps

Cited by 1 publication

References 55 publications

Predicting Earthquake Casualties and Emergency Supplies Needs Based on PCA-BO-SVM

Predicting Earthquake Casualties and Emergency Supplies Needs Based on PCA-BO-SVM

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