Enhancing flood susceptibility mapping in Meghna River basin by introducing ensemble Naive Bayes with stacking algorithms
This article intends to assess flood susceptibility mapping in Meghna River basin (MRB) and identified flood susceptible regions using three benchmark models including random forest (RF), support vector machine (SVM) and bagging with Naïve Bayes (NB) stacking ensemble algorithms (e.g. RF-NB; SVM-NB...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-12-01
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| Series: | Geomatics, Natural Hazards & Risk |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/19475705.2025.2464049 |
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| author | Abu Reza Md. Towfiqul Islam Md. Uzzal Mia Nourin Akter Nova Rabin Chakrabortty Md. Sanjid Islam Khan Bonosri Ghose Subodh Chandra Pal A. B. M. Mainul Bari Edris Alam Md Kamrul Islam Mohammed Ali Alshehri Hazem Ghassan Abdo Romulus Costache |
| author_facet | Abu Reza Md. Towfiqul Islam Md. Uzzal Mia Nourin Akter Nova Rabin Chakrabortty Md. Sanjid Islam Khan Bonosri Ghose Subodh Chandra Pal A. B. M. Mainul Bari Edris Alam Md Kamrul Islam Mohammed Ali Alshehri Hazem Ghassan Abdo Romulus Costache |
| author_sort | Abu Reza Md. Towfiqul Islam |
| collection | DOAJ |
| description | This article intends to assess flood susceptibility mapping in Meghna River basin (MRB) and identified flood susceptible regions using three benchmark models including random forest (RF), support vector machine (SVM) and bagging with Naïve Bayes (NB) stacking ensemble algorithms (e.g. RF-NB; SVM-NB and Bagging-NB). The flood sample was partitioned into a training set (70%), and a validation set (30%), and the capability of prediction of flood-influencing variables was quantified by the multi-collinearity test. Several statistical metrics and Area Under the Receiver Operating Characteristics (AUROC) technique were applied to evaluate the techniques’ performance and precision. The outcomes showed that the significant factors influencing flash floods include rainfall, distance from the river and river density. The NB-Bagging outperforms (≈ a prediction accuracy of 95.1%) than other models in predicting the risk of flooding in the MRB. Results obtained from NB-Bagging showed that 12% and 21% of the basin were demarcated as having high and very high flood susceptibility, respectively. This article identified that rainfall and distance from the river were the two most driving factors influencing flooding in the MRB. The present work will aid decision-makers and local authorities determine flood conditioning problems and efficient flood risk management to lessen the consequences. |
| format | Article |
| id | doaj-art-e8510fe991f74de1ae3dd8a343baf8ff |
| institution | OA Journals |
| issn | 1947-5705 1947-5713 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Geomatics, Natural Hazards & Risk |
| spelling | doaj-art-e8510fe991f74de1ae3dd8a343baf8ff2025-08-20T02:38:06ZengTaylor & Francis GroupGeomatics, Natural Hazards & Risk1947-57051947-57132025-12-0116110.1080/19475705.2025.2464049Enhancing flood susceptibility mapping in Meghna River basin by introducing ensemble Naive Bayes with stacking algorithmsAbu Reza Md. Towfiqul Islam0Md. Uzzal Mia1Nourin Akter Nova2Rabin Chakrabortty3Md. Sanjid Islam Khan4Bonosri Ghose5Subodh Chandra Pal6A. B. M. Mainul Bari7Edris Alam8Md Kamrul Islam9Mohammed Ali Alshehri10Hazem Ghassan Abdo11Romulus Costache12Department of Disaster Management, Begum Rokeya University, Rangpur, BangladeshDepartment of Disaster Management, Begum Rokeya University, Rangpur, BangladeshDepartment of Disaster Management, Begum Rokeya University, Rangpur, BangladeshSchool of Environment Resources and Development, Asian Institute of Technology (AIT), Phathum Thani, ThailanDepartment of Disaster Management, Begum Rokeya University, Rangpur, BangladeshDepartment of Disaster Management, Begum Rokeya University, Rangpur, BangladeshDepartment of Geography, The University of Burdwan, Bardhaman, West Bengal, IndiaDepartment of Industrial and Production Engineering, Bangladesh University of Engineering and Technology, Dhaka, BangladeshFaculty of Resilience, Rabdan Academy, Abu Dhabi, UAEDepartment of Civil and Environmental Engineering, College of Engineering, King Faisal University, Hofuf, Saudi ArabiaDepartment of Biology, Faculty of Science, University of Tabuk, Tabuk, Saudi ArabiaGeography Department, Faculty of Arts and Humanities, Tartous University, Tartous, SyriaDepartment of Civil Engineering, Transilvania University of Brasov, Brasov, RomaniaThis article intends to assess flood susceptibility mapping in Meghna River basin (MRB) and identified flood susceptible regions using three benchmark models including random forest (RF), support vector machine (SVM) and bagging with Naïve Bayes (NB) stacking ensemble algorithms (e.g. RF-NB; SVM-NB and Bagging-NB). The flood sample was partitioned into a training set (70%), and a validation set (30%), and the capability of prediction of flood-influencing variables was quantified by the multi-collinearity test. Several statistical metrics and Area Under the Receiver Operating Characteristics (AUROC) technique were applied to evaluate the techniques’ performance and precision. The outcomes showed that the significant factors influencing flash floods include rainfall, distance from the river and river density. The NB-Bagging outperforms (≈ a prediction accuracy of 95.1%) than other models in predicting the risk of flooding in the MRB. Results obtained from NB-Bagging showed that 12% and 21% of the basin were demarcated as having high and very high flood susceptibility, respectively. This article identified that rainfall and distance from the river were the two most driving factors influencing flooding in the MRB. The present work will aid decision-makers and local authorities determine flood conditioning problems and efficient flood risk management to lessen the consequences.https://www.tandfonline.com/doi/10.1080/19475705.2025.2464049Flood susceptibilityNaïve Bayesbaggingflood risk modellingBangladesh |
| spellingShingle | Abu Reza Md. Towfiqul Islam Md. Uzzal Mia Nourin Akter Nova Rabin Chakrabortty Md. Sanjid Islam Khan Bonosri Ghose Subodh Chandra Pal A. B. M. Mainul Bari Edris Alam Md Kamrul Islam Mohammed Ali Alshehri Hazem Ghassan Abdo Romulus Costache Enhancing flood susceptibility mapping in Meghna River basin by introducing ensemble Naive Bayes with stacking algorithms Geomatics, Natural Hazards & Risk Flood susceptibility Naïve Bayes bagging flood risk modelling Bangladesh |
| title | Enhancing flood susceptibility mapping in Meghna River basin by introducing ensemble Naive Bayes with stacking algorithms |
| title_full | Enhancing flood susceptibility mapping in Meghna River basin by introducing ensemble Naive Bayes with stacking algorithms |
| title_fullStr | Enhancing flood susceptibility mapping in Meghna River basin by introducing ensemble Naive Bayes with stacking algorithms |
| title_full_unstemmed | Enhancing flood susceptibility mapping in Meghna River basin by introducing ensemble Naive Bayes with stacking algorithms |
| title_short | Enhancing flood susceptibility mapping in Meghna River basin by introducing ensemble Naive Bayes with stacking algorithms |
| title_sort | enhancing flood susceptibility mapping in meghna river basin by introducing ensemble naive bayes with stacking algorithms |
| topic | Flood susceptibility Naïve Bayes bagging flood risk modelling Bangladesh |
| url | https://www.tandfonline.com/doi/10.1080/19475705.2025.2464049 |
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