Predicting Earthquake Casualties and Emergency Supplies Needs Based on PCA-BO-SVM
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 pro...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
|
| Series: | Systems |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-8954/13/1/24 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832587428756455424 |
|---|---|
| author | Fuyu Wang Huiying Xu Huifen Ye Yan Li Yibo Wang |
| author_facet | Fuyu Wang Huiying Xu Huifen Ye Yan Li Yibo Wang |
| author_sort | Fuyu Wang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-66f92d53c96140e895d84f5558cc0714 |
| institution | Kabale University |
| issn | 2079-8954 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Systems |
| spelling | doaj-art-66f92d53c96140e895d84f5558cc07142025-01-24T13:50:31ZengMDPI AGSystems2079-89542025-01-011312410.3390/systems13010024Predicting Earthquake Casualties and Emergency Supplies Needs Based on PCA-BO-SVMFuyu Wang0Huiying Xu1Huifen Ye2Yan Li3Yibo Wang4School of Management Science and Engineering, Anhui University of Technology, Ma’anshan 243032, ChinaSchool of Management Science and Engineering, Anhui University of Technology, Ma’anshan 243032, ChinaSchool of Management Science and Engineering, Anhui University of Technology, Ma’anshan 243032, ChinaSchool of Management Science and Engineering, Anhui University of Technology, Ma’anshan 243032, ChinaSchool of Management Science and Engineering, Anhui University of Technology, Ma’anshan 243032, ChinaThe 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.https://www.mdpi.com/2079-8954/13/1/24principal component analysisprediction of earthquake casualtiesBayesian optimizationsupport vector machineemergency material demand forecasting |
| spellingShingle | Fuyu Wang Huiying Xu Huifen Ye Yan Li Yibo Wang Predicting Earthquake Casualties and Emergency Supplies Needs Based on PCA-BO-SVM Systems principal component analysis prediction of earthquake casualties Bayesian optimization support vector machine emergency material demand forecasting |
| title | Predicting Earthquake Casualties and Emergency Supplies Needs Based on PCA-BO-SVM |
| title_full | Predicting Earthquake Casualties and Emergency Supplies Needs Based on PCA-BO-SVM |
| title_fullStr | Predicting Earthquake Casualties and Emergency Supplies Needs Based on PCA-BO-SVM |
| title_full_unstemmed | Predicting Earthquake Casualties and Emergency Supplies Needs Based on PCA-BO-SVM |
| title_short | Predicting Earthquake Casualties and Emergency Supplies Needs Based on PCA-BO-SVM |
| title_sort | predicting earthquake casualties and emergency supplies needs based on pca bo svm |
| topic | principal component analysis prediction of earthquake casualties Bayesian optimization support vector machine emergency material demand forecasting |
| url | https://www.mdpi.com/2079-8954/13/1/24 |
| work_keys_str_mv | AT fuyuwang predictingearthquakecasualtiesandemergencysuppliesneedsbasedonpcabosvm AT huiyingxu predictingearthquakecasualtiesandemergencysuppliesneedsbasedonpcabosvm AT huifenye predictingearthquakecasualtiesandemergencysuppliesneedsbasedonpcabosvm AT yanli predictingearthquakecasualtiesandemergencysuppliesneedsbasedonpcabosvm AT yibowang predictingearthquakecasualtiesandemergencysuppliesneedsbasedonpcabosvm |