Assessing Flood and Landslide Susceptibility Using XGBoost: Case Study of the Basento River in Southern Italy
Floods and landslides are two distinct natural phenomena influenced by different conditioning factors, though some environmental triggers may overlap. This study applied eXtreme Gradient Boosting (XGBoost) to develop susceptibility maps for both phenomena, using a unified approach based on the same...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/15/10/5290 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850127235416064000 |
|---|---|
| author | Marica Rondinone Silvano Fortunato Dal Sasso Htay Htay Aung Lucia Contillo Giusy Dimola Marcello Schiattarella Mauro Fiorentino Vito Telesca |
| author_facet | Marica Rondinone Silvano Fortunato Dal Sasso Htay Htay Aung Lucia Contillo Giusy Dimola Marcello Schiattarella Mauro Fiorentino Vito Telesca |
| author_sort | Marica Rondinone |
| collection | DOAJ |
| description | Floods and landslides are two distinct natural phenomena influenced by different conditioning factors, though some environmental triggers may overlap. This study applied eXtreme Gradient Boosting (XGBoost) to develop susceptibility maps for both phenomena, using a unified approach based on the same geospatial predictors. The approach integrated topographical, geological, and remote sensing datasets. Flood event data were collected from institutional sources using multi-source and high-resolution remotely sensed data. The landslide inventory was compiled based on historical records and geomorphological analysis. Key conditioning factors such as elevation, slope, lithology, and land cover were analyzed to identify areas prone to floods and landslides. The methodology was applied to the Basento River basin in Southern Italy, a region frequently impacted by both hazards, to assess its vulnerability and inform risk management strategies. While flood susceptibility is primarily associated with low-lying areas near river networks, landslides are more influenced by steep slopes and geological instability. The XGBoost model achieved a classification accuracy close to 1 for flood-prone areas and 0.92 for landslide-prone areas. Results showed that flood susceptibility was primarily associated with low Elevation and Relative Elevation, and high Drainage Density, whereas landslide susceptibility was more influenced by a broader and balanced set of factors, including Elevation, Drainage Density, Relative Elevation, Distance and Lithology. The resulting susceptibility maps offered critical approaches for land use planning, emergency management, and risk mitigation. Overall, the results demonstrated the effectiveness of XGBoost in multi-hazard assessments, offering a scalable and transferable approach for similar at-risk regions worldwide. |
| format | Article |
| id | doaj-art-d7baed7d663746ac9da49db6471c92ad |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-d7baed7d663746ac9da49db6471c92ad2025-08-20T02:33:43ZengMDPI AGApplied Sciences2076-34172025-05-011510529010.3390/app15105290Assessing Flood and Landslide Susceptibility Using XGBoost: Case Study of the Basento River in Southern ItalyMarica Rondinone0Silvano Fortunato Dal Sasso1Htay Htay Aung2Lucia Contillo3Giusy Dimola4Marcello Schiattarella5Mauro Fiorentino6Vito Telesca7Department of Engineering, University of Basilicata, 85100 Potenza, ItalyDepartment for Humanistic, Scientific, and Social Innovation, University of Basilicata, 75100 Matera, ItalyDepartment for Humanistic, Scientific, and Social Innovation, University of Basilicata, 75100 Matera, ItalyDepartment for Humanistic, Scientific, and Social Innovation, University of Basilicata, 75100 Matera, ItalyDepartment for Humanistic, Scientific, and Social Innovation, University of Basilicata, 75100 Matera, ItalyDepartment for Humanistic, Scientific, and Social Innovation, University of Basilicata, 75100 Matera, ItalyDepartment of Engineering, University of Basilicata, 85100 Potenza, ItalyDepartment of Engineering, University of Basilicata, 85100 Potenza, ItalyFloods and landslides are two distinct natural phenomena influenced by different conditioning factors, though some environmental triggers may overlap. This study applied eXtreme Gradient Boosting (XGBoost) to develop susceptibility maps for both phenomena, using a unified approach based on the same geospatial predictors. The approach integrated topographical, geological, and remote sensing datasets. Flood event data were collected from institutional sources using multi-source and high-resolution remotely sensed data. The landslide inventory was compiled based on historical records and geomorphological analysis. Key conditioning factors such as elevation, slope, lithology, and land cover were analyzed to identify areas prone to floods and landslides. The methodology was applied to the Basento River basin in Southern Italy, a region frequently impacted by both hazards, to assess its vulnerability and inform risk management strategies. While flood susceptibility is primarily associated with low-lying areas near river networks, landslides are more influenced by steep slopes and geological instability. The XGBoost model achieved a classification accuracy close to 1 for flood-prone areas and 0.92 for landslide-prone areas. Results showed that flood susceptibility was primarily associated with low Elevation and Relative Elevation, and high Drainage Density, whereas landslide susceptibility was more influenced by a broader and balanced set of factors, including Elevation, Drainage Density, Relative Elevation, Distance and Lithology. The resulting susceptibility maps offered critical approaches for land use planning, emergency management, and risk mitigation. Overall, the results demonstrated the effectiveness of XGBoost in multi-hazard assessments, offering a scalable and transferable approach for similar at-risk regions worldwide.https://www.mdpi.com/2076-3417/15/10/5290XGBoostmulti-hazard mappingflood susceptibilitylandslide susceptibilitymachine learningremote sensing |
| spellingShingle | Marica Rondinone Silvano Fortunato Dal Sasso Htay Htay Aung Lucia Contillo Giusy Dimola Marcello Schiattarella Mauro Fiorentino Vito Telesca Assessing Flood and Landslide Susceptibility Using XGBoost: Case Study of the Basento River in Southern Italy Applied Sciences XGBoost multi-hazard mapping flood susceptibility landslide susceptibility machine learning remote sensing |
| title | Assessing Flood and Landslide Susceptibility Using XGBoost: Case Study of the Basento River in Southern Italy |
| title_full | Assessing Flood and Landslide Susceptibility Using XGBoost: Case Study of the Basento River in Southern Italy |
| title_fullStr | Assessing Flood and Landslide Susceptibility Using XGBoost: Case Study of the Basento River in Southern Italy |
| title_full_unstemmed | Assessing Flood and Landslide Susceptibility Using XGBoost: Case Study of the Basento River in Southern Italy |
| title_short | Assessing Flood and Landslide Susceptibility Using XGBoost: Case Study of the Basento River in Southern Italy |
| title_sort | assessing flood and landslide susceptibility using xgboost case study of the basento river in southern italy |
| topic | XGBoost multi-hazard mapping flood susceptibility landslide susceptibility machine learning remote sensing |
| url | https://www.mdpi.com/2076-3417/15/10/5290 |
| work_keys_str_mv | AT maricarondinone assessingfloodandlandslidesusceptibilityusingxgboostcasestudyofthebasentoriverinsouthernitaly AT silvanofortunatodalsasso assessingfloodandlandslidesusceptibilityusingxgboostcasestudyofthebasentoriverinsouthernitaly AT htayhtayaung assessingfloodandlandslidesusceptibilityusingxgboostcasestudyofthebasentoriverinsouthernitaly AT luciacontillo assessingfloodandlandslidesusceptibilityusingxgboostcasestudyofthebasentoriverinsouthernitaly AT giusydimola assessingfloodandlandslidesusceptibilityusingxgboostcasestudyofthebasentoriverinsouthernitaly AT marcelloschiattarella assessingfloodandlandslidesusceptibilityusingxgboostcasestudyofthebasentoriverinsouthernitaly AT maurofiorentino assessingfloodandlandslidesusceptibilityusingxgboostcasestudyofthebasentoriverinsouthernitaly AT vitotelesca assessingfloodandlandslidesusceptibilityusingxgboostcasestudyofthebasentoriverinsouthernitaly |