Predicting urban landslides in the hilly regions of Bangladesh leveraging a hybrid machine learning model and CMIP6 climate projections
Landslides pose significant risks to infrastructure and human lives in cities, exacerbated by climate change. Therefore, a reliable predictive landslide model is crucial for mitigation, especially in resource-limited nations. This study employs hybrid machine learning (ML) techniques and climate pro...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-05-01
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| Series: | Geosystems and Geoenvironment |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772883825000044 |
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| author | Md․ Ashraful Islam Musabbir Ahmed Arrafi Mehedi Hasan Peas Tanvir Hossain Md Mehedi Hasan Sanzida Murshed Monira Jahan Tania |
| author_facet | Md․ Ashraful Islam Musabbir Ahmed Arrafi Mehedi Hasan Peas Tanvir Hossain Md Mehedi Hasan Sanzida Murshed Monira Jahan Tania |
| author_sort | Md․ Ashraful Islam |
| collection | DOAJ |
| description | Landslides pose significant risks to infrastructure and human lives in cities, exacerbated by climate change. Therefore, a reliable predictive landslide model is crucial for mitigation, especially in resource-limited nations. This study employs hybrid machine learning (ML) techniques and climate projections to predict landslides in the Chattogram development area (CDA) of Bangladesh – a rapidly growing urban city in Bangladesh. The model was trained using diverse geospatial parameters including topographical, hydrological, soil, and geological parameters, along with an updated landslide inventory, enabling spatially explicit predictions of landslide susceptibility. To incorporate future climate scenarios, we utilized the Coupled Model Intercomparison Project Phase 6 (CMIP6) Global Climate Model (GCM), projecting climate impacts under SSP1-2.6 and SSP5-8.5 scenarios for the periods of 2021–2040, 2041–2060, 2061–2080, and 2081–2100, respectively. These scenarios reflect different pathways of greenhouse gas emissions, providing a range of possible future climate conditions. We tested six ML classifiers: random forest (RF), extra trees (ExT), support vector machine (SVM), logistic regression (LR), Bernoulli Naïve Bayes (bNB), and K-nearest neighbor (KNN). Each base model demonstrated high accuracy (>90 %) but combining them improved both accuracy and computational efficiency. The LR-bNB hybrid model outperformed all others, effectively mapping landslide susceptibility in the study area for the current timeframe and future projections. Our results revealed significant variability in landslide-prone areas across the area, with 12 % of the region categorized as high to very high risk, a figure that slightly rises with predicted increased rainfall due to climate change. The present study demonstrates the efficacy of a hybrid ML model for nowcasting as well as forecasting landslide susceptibility under future climate scenarios. These findings offer valuable insights for proactive risk management and infrastructure planning in the CDA, helping to safeguard communities and improve resilience against future landslide events. |
| format | Article |
| id | doaj-art-4bd69a927b244f1d887691aaa1ffdfd7 |
| institution | DOAJ |
| issn | 2772-8838 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Geosystems and Geoenvironment |
| spelling | doaj-art-4bd69a927b244f1d887691aaa1ffdfd72025-08-20T03:05:55ZengElsevierGeosystems and Geoenvironment2772-88382025-05-014210035410.1016/j.geogeo.2025.100354Predicting urban landslides in the hilly regions of Bangladesh leveraging a hybrid machine learning model and CMIP6 climate projectionsMd․ Ashraful Islam0Musabbir Ahmed Arrafi1Mehedi Hasan Peas2Tanvir Hossain3Md Mehedi Hasan4Sanzida Murshed5Monira Jahan Tania6Department of Geology, University of Dhaka, Dhaka, Bangladesh; Corresponding author.Department of Mechanical and Aerospace Engineering, University of Dayton, Ohio, USA; Department of Robotics and Mechatronics Engineering, University of Dhaka, Dhaka, BangladeshBangladesh Space Research and Remote Sensing Organization (SPARRSO), Dhaka, BangladeshDepartment of Geology, University of Dhaka, Dhaka, BangladeshDepartment of Robotics and Mechatronics Engineering, University of Dhaka, Dhaka, BangladeshDepartment of Geology, University of Dhaka, Dhaka, BangladeshDepartment of Geology, University of Dhaka, Dhaka, BangladeshLandslides pose significant risks to infrastructure and human lives in cities, exacerbated by climate change. Therefore, a reliable predictive landslide model is crucial for mitigation, especially in resource-limited nations. This study employs hybrid machine learning (ML) techniques and climate projections to predict landslides in the Chattogram development area (CDA) of Bangladesh – a rapidly growing urban city in Bangladesh. The model was trained using diverse geospatial parameters including topographical, hydrological, soil, and geological parameters, along with an updated landslide inventory, enabling spatially explicit predictions of landslide susceptibility. To incorporate future climate scenarios, we utilized the Coupled Model Intercomparison Project Phase 6 (CMIP6) Global Climate Model (GCM), projecting climate impacts under SSP1-2.6 and SSP5-8.5 scenarios for the periods of 2021–2040, 2041–2060, 2061–2080, and 2081–2100, respectively. These scenarios reflect different pathways of greenhouse gas emissions, providing a range of possible future climate conditions. We tested six ML classifiers: random forest (RF), extra trees (ExT), support vector machine (SVM), logistic regression (LR), Bernoulli Naïve Bayes (bNB), and K-nearest neighbor (KNN). Each base model demonstrated high accuracy (>90 %) but combining them improved both accuracy and computational efficiency. The LR-bNB hybrid model outperformed all others, effectively mapping landslide susceptibility in the study area for the current timeframe and future projections. Our results revealed significant variability in landslide-prone areas across the area, with 12 % of the region categorized as high to very high risk, a figure that slightly rises with predicted increased rainfall due to climate change. The present study demonstrates the efficacy of a hybrid ML model for nowcasting as well as forecasting landslide susceptibility under future climate scenarios. These findings offer valuable insights for proactive risk management and infrastructure planning in the CDA, helping to safeguard communities and improve resilience against future landslide events.http://www.sciencedirect.com/science/article/pii/S2772883825000044LandslideClimate changeBangladeshDisaster risk reductionLandslide mappingMachine learning |
| spellingShingle | Md․ Ashraful Islam Musabbir Ahmed Arrafi Mehedi Hasan Peas Tanvir Hossain Md Mehedi Hasan Sanzida Murshed Monira Jahan Tania Predicting urban landslides in the hilly regions of Bangladesh leveraging a hybrid machine learning model and CMIP6 climate projections Geosystems and Geoenvironment Landslide Climate change Bangladesh Disaster risk reduction Landslide mapping Machine learning |
| title | Predicting urban landslides in the hilly regions of Bangladesh leveraging a hybrid machine learning model and CMIP6 climate projections |
| title_full | Predicting urban landslides in the hilly regions of Bangladesh leveraging a hybrid machine learning model and CMIP6 climate projections |
| title_fullStr | Predicting urban landslides in the hilly regions of Bangladesh leveraging a hybrid machine learning model and CMIP6 climate projections |
| title_full_unstemmed | Predicting urban landslides in the hilly regions of Bangladesh leveraging a hybrid machine learning model and CMIP6 climate projections |
| title_short | Predicting urban landslides in the hilly regions of Bangladesh leveraging a hybrid machine learning model and CMIP6 climate projections |
| title_sort | predicting urban landslides in the hilly regions of bangladesh leveraging a hybrid machine learning model and cmip6 climate projections |
| topic | Landslide Climate change Bangladesh Disaster risk reduction Landslide mapping Machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S2772883825000044 |
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