Spatial mapping of landslide susceptibility in Jerash governorate of Jordan using genetic algorithm-based wrapper feature selection and bagging-based ensemble model
This study employs five genetic algorithm (GA)-based machine learning (ML) models, namely the Decision Tree (DT), k-Nearest Neighbors (kNN), NaïveBayes (NB), Support Vector Machine (SVM), and Extreme Learning Machine (ELM), to build a novel ensemble algorithm that is founded on the Bagging method fo...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2022-12-01
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| Series: | Geomatics, Natural Hazards & Risk |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/19475705.2022.2112096 |
| _version_ | 1818000130412904448 |
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| author | Abdel Rahman Al-Shabeeb A’kif Al-Fugara Khaled Mohamed Khedher Ali Nouh Mabdeh Rida Al-Adamat |
| author_facet | Abdel Rahman Al-Shabeeb A’kif Al-Fugara Khaled Mohamed Khedher Ali Nouh Mabdeh Rida Al-Adamat |
| author_sort | Abdel Rahman Al-Shabeeb |
| collection | DOAJ |
| description | This study employs five genetic algorithm (GA)-based machine learning (ML) models, namely the Decision Tree (DT), k-Nearest Neighbors (kNN), NaïveBayes (NB), Support Vector Machine (SVM), and Extreme Learning Machine (ELM), to build a novel ensemble algorithm that is founded on the Bagging method for landslide susceptibility mapping (LSM) in Jerash Governorate, north of Jordan. The GA-based wrapper feature selection (FS) was done based on the five individual models and in the initial stages of modeling, an inquiry for the best feature for each of the five models was made. Finally, five hybrid models, namely DT-GA, kNN-GA, NB-GA, SVM-GA, and ELM-GA were constructed and combined to create Bagging-based ensemble model. The FS outcomes uncovered that rainfall depth, distance to roads, the Stream Power Index, the Normalized Difference Vegetation Index, slope, geology, and aspect are the most influential determinants of landslides. After the significant variables were identified, they were selected as input predictors and entered into the models. GA-based Bagging ensemble model with the area under the receiver operating characteristic curve (AUROC) of 0.85 achieved the highest accuracy in the validation run, followed by SVM-GA (AUROC = 0.80), NB-GA (AUROC = 0.76), DT-GA (AUROC = 0.72), kNN-GA (AUROC = 0.70), and ELM-GA (AUROC = 0.48). |
| first_indexed | 2024-04-14T03:18:53Z |
| format | Article |
| id | doaj.art-4378f66e61f441ef8f0a29ae4f701870 |
| institution | Directory Open Access Journal |
| issn | 1947-5705 1947-5713 |
| language | English |
| last_indexed | 2024-04-14T03:18:53Z |
| publishDate | 2022-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Geomatics, Natural Hazards & Risk |
| spelling | doaj.art-4378f66e61f441ef8f0a29ae4f7018702022-12-22T02:15:23ZengTaylor & Francis GroupGeomatics, Natural Hazards & Risk1947-57051947-57132022-12-011312252228210.1080/19475705.2022.2112096Spatial mapping of landslide susceptibility in Jerash governorate of Jordan using genetic algorithm-based wrapper feature selection and bagging-based ensemble modelAbdel Rahman Al-Shabeeb0A’kif Al-Fugara1Khaled Mohamed Khedher2Ali Nouh Mabdeh3Rida Al-Adamat4Department of GIS and Remote Sensing, Institute of Earth and Environmental Sciences, Al Al-Bayt University, Mafraq, JordanDepartment of Surveying Engineering, Faculty of Engineering, Al Al-Bayt University, Mafraq, JordanDepartment of Civil Engineering, College of Engineering, King Khalid University, Abha, Saudi ArabiaDepartment of GIS and Remote Sensing, Institute of Earth and Environmental Sciences, Al Al-Bayt University, Mafraq, JordanDepartment of GIS and Remote Sensing, Institute of Earth and Environmental Sciences, Al Al-Bayt University, Mafraq, JordanThis study employs five genetic algorithm (GA)-based machine learning (ML) models, namely the Decision Tree (DT), k-Nearest Neighbors (kNN), NaïveBayes (NB), Support Vector Machine (SVM), and Extreme Learning Machine (ELM), to build a novel ensemble algorithm that is founded on the Bagging method for landslide susceptibility mapping (LSM) in Jerash Governorate, north of Jordan. The GA-based wrapper feature selection (FS) was done based on the five individual models and in the initial stages of modeling, an inquiry for the best feature for each of the five models was made. Finally, five hybrid models, namely DT-GA, kNN-GA, NB-GA, SVM-GA, and ELM-GA were constructed and combined to create Bagging-based ensemble model. The FS outcomes uncovered that rainfall depth, distance to roads, the Stream Power Index, the Normalized Difference Vegetation Index, slope, geology, and aspect are the most influential determinants of landslides. After the significant variables were identified, they were selected as input predictors and entered into the models. GA-based Bagging ensemble model with the area under the receiver operating characteristic curve (AUROC) of 0.85 achieved the highest accuracy in the validation run, followed by SVM-GA (AUROC = 0.80), NB-GA (AUROC = 0.76), DT-GA (AUROC = 0.72), kNN-GA (AUROC = 0.70), and ELM-GA (AUROC = 0.48).https://www.tandfonline.com/doi/10.1080/19475705.2022.2112096Ensemble modelshazard mappinglandslide susceptibility mappingmachine learning |
| spellingShingle | Abdel Rahman Al-Shabeeb A’kif Al-Fugara Khaled Mohamed Khedher Ali Nouh Mabdeh Rida Al-Adamat Spatial mapping of landslide susceptibility in Jerash governorate of Jordan using genetic algorithm-based wrapper feature selection and bagging-based ensemble model Geomatics, Natural Hazards & Risk Ensemble models hazard mapping landslide susceptibility mapping machine learning |
| title | Spatial mapping of landslide susceptibility in Jerash governorate of Jordan using genetic algorithm-based wrapper feature selection and bagging-based ensemble model |
| title_full | Spatial mapping of landslide susceptibility in Jerash governorate of Jordan using genetic algorithm-based wrapper feature selection and bagging-based ensemble model |
| title_fullStr | Spatial mapping of landslide susceptibility in Jerash governorate of Jordan using genetic algorithm-based wrapper feature selection and bagging-based ensemble model |
| title_full_unstemmed | Spatial mapping of landslide susceptibility in Jerash governorate of Jordan using genetic algorithm-based wrapper feature selection and bagging-based ensemble model |
| title_short | Spatial mapping of landslide susceptibility in Jerash governorate of Jordan using genetic algorithm-based wrapper feature selection and bagging-based ensemble model |
| title_sort | spatial mapping of landslide susceptibility in jerash governorate of jordan using genetic algorithm based wrapper feature selection and bagging based ensemble model |
| topic | Ensemble models hazard mapping landslide susceptibility mapping machine learning |
| url | https://www.tandfonline.com/doi/10.1080/19475705.2022.2112096 |
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