A novel hybrid artificial intelligence approach based on the rotation forest ensemble and naïve Bayes tree classifiers for a landslide susceptibility assessment in Langao County, China
The main objective of this study was to produce landslide susceptibility maps for Langao County, China, using a novel hybrid artificial intelligence method based on rotation forest ensembles (RFEs) and naïve Bayes tree (NBT) classifiers labeled the RF-NBT model. The spatial database consisted of eig...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2017-12-01
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| Series: | Geomatics, Natural Hazards & Risk |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/19475705.2017.1401560 |
| _version_ | 1811288782788362240 |
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| author | Wei Chen Ataollah Shirzadi Himan Shahabi Baharin Bin Ahmad Shuai Zhang Haoyuan Hong Ning Zhang |
| author_facet | Wei Chen Ataollah Shirzadi Himan Shahabi Baharin Bin Ahmad Shuai Zhang Haoyuan Hong Ning Zhang |
| author_sort | Wei Chen |
| collection | DOAJ |
| description | The main objective of this study was to produce landslide susceptibility maps for Langao County, China, using a novel hybrid artificial intelligence method based on rotation forest ensembles (RFEs) and naïve Bayes tree (NBT) classifiers labeled the RF-NBT model. The spatial database consisted of eighteen conditioning factors that were selected using the information gain ratio (IGR) method. The model was evaluated using quantitative statistical criteria, including the sensitivity, specificity, accuracy, root mean squared error (RMSE), and area under the receiver operating characteristic curve (AUC). Furthermore, the new model was compared with the NBT, functional tree (FT), logistic model tree (LMT) and reduced-error pruning tree (REPTree) soft computing benchmark models. The findings indicated that the RF-NBT model showed an increased prediction accuracy relative to the NBT model using both the training and validation datasets, and the RF-NBT model exhibited a greater capability for landslide susceptibility mapping. The new RF-NBT model also showed the most preferable results compared with the FT, LMT and REPTree models. Finally, an analysis of the landslide density (LD) using the RF-NBT model demonstrated that the very high susceptibility (VHS) class had the highest LD (3.552) among the landslide susceptibility maps. These results can be used for the planning and management of areas vulnerable to landslides in order to prevent damages caused by such natural disasters. |
| first_indexed | 2024-04-13T03:43:29Z |
| format | Article |
| id | doaj.art-4affa5f7eebe43bc8b36387e707ee861 |
| institution | Directory Open Access Journal |
| issn | 1947-5705 1947-5713 |
| language | English |
| last_indexed | 2024-04-13T03:43:29Z |
| publishDate | 2017-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Geomatics, Natural Hazards & Risk |
| spelling | doaj.art-4affa5f7eebe43bc8b36387e707ee8612022-12-22T03:04:04ZengTaylor & Francis GroupGeomatics, Natural Hazards & Risk1947-57051947-57132017-12-01821955197710.1080/19475705.2017.14015601401560A novel hybrid artificial intelligence approach based on the rotation forest ensemble and naïve Bayes tree classifiers for a landslide susceptibility assessment in Langao County, ChinaWei Chen0Ataollah Shirzadi1Himan Shahabi2Baharin Bin Ahmad3Shuai Zhang4Haoyuan Hong5Ning Zhang6Xi'an University of Science and TechnologyUniversity of KurdistanUniversity of KurdistanUniversiti Teknologi Malaysia (UTM)Xi'an University of Science and TechnologyNanjing Normal UniversityXi'an University of Science and TechnologyThe main objective of this study was to produce landslide susceptibility maps for Langao County, China, using a novel hybrid artificial intelligence method based on rotation forest ensembles (RFEs) and naïve Bayes tree (NBT) classifiers labeled the RF-NBT model. The spatial database consisted of eighteen conditioning factors that were selected using the information gain ratio (IGR) method. The model was evaluated using quantitative statistical criteria, including the sensitivity, specificity, accuracy, root mean squared error (RMSE), and area under the receiver operating characteristic curve (AUC). Furthermore, the new model was compared with the NBT, functional tree (FT), logistic model tree (LMT) and reduced-error pruning tree (REPTree) soft computing benchmark models. The findings indicated that the RF-NBT model showed an increased prediction accuracy relative to the NBT model using both the training and validation datasets, and the RF-NBT model exhibited a greater capability for landslide susceptibility mapping. The new RF-NBT model also showed the most preferable results compared with the FT, LMT and REPTree models. Finally, an analysis of the landslide density (LD) using the RF-NBT model demonstrated that the very high susceptibility (VHS) class had the highest LD (3.552) among the landslide susceptibility maps. These results can be used for the planning and management of areas vulnerable to landslides in order to prevent damages caused by such natural disasters.http://dx.doi.org/10.1080/19475705.2017.1401560landslide susceptibility mappinghybrid integration approachcomparisongischina |
| spellingShingle | Wei Chen Ataollah Shirzadi Himan Shahabi Baharin Bin Ahmad Shuai Zhang Haoyuan Hong Ning Zhang A novel hybrid artificial intelligence approach based on the rotation forest ensemble and naïve Bayes tree classifiers for a landslide susceptibility assessment in Langao County, China Geomatics, Natural Hazards & Risk landslide susceptibility mapping hybrid integration approach comparison gis china |
| title | A novel hybrid artificial intelligence approach based on the rotation forest ensemble and naïve Bayes tree classifiers for a landslide susceptibility assessment in Langao County, China |
| title_full | A novel hybrid artificial intelligence approach based on the rotation forest ensemble and naïve Bayes tree classifiers for a landslide susceptibility assessment in Langao County, China |
| title_fullStr | A novel hybrid artificial intelligence approach based on the rotation forest ensemble and naïve Bayes tree classifiers for a landslide susceptibility assessment in Langao County, China |
| title_full_unstemmed | A novel hybrid artificial intelligence approach based on the rotation forest ensemble and naïve Bayes tree classifiers for a landslide susceptibility assessment in Langao County, China |
| title_short | A novel hybrid artificial intelligence approach based on the rotation forest ensemble and naïve Bayes tree classifiers for a landslide susceptibility assessment in Langao County, China |
| title_sort | novel hybrid artificial intelligence approach based on the rotation forest ensemble and naive bayes tree classifiers for a landslide susceptibility assessment in langao county china |
| topic | landslide susceptibility mapping hybrid integration approach comparison gis china |
| url | http://dx.doi.org/10.1080/19475705.2017.1401560 |
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