Data mining and statistical approaches in debris-flow susceptibility modelling using airborne LiDAR data
Cameron Highland is a popular tourist hub in the mountainous area of Peninsular Malaysia. Most communities in this area suffer frequent incidence of debris flow, especially during monsoon seasons. Despite the loss of lives and properties recorded annually from debris flow, most studies in the region...
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| Format: | Article |
| Language: | English |
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Multidisciplinary Digital Publishing Institute
2019
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| Online Access: | http://psasir.upm.edu.my/id/eprint/81078/1/DEBRIS.pdf |
| _version_ | 1796980854666297344 |
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| author | Lay, Usman Salihu Pradhan, Biswajeet Md Yusoff, Zainuddin Abdullah, Ahmad Fikri Aryal, Jagannath Park, Hyuck Jin |
| author_facet | Lay, Usman Salihu Pradhan, Biswajeet Md Yusoff, Zainuddin Abdullah, Ahmad Fikri Aryal, Jagannath Park, Hyuck Jin |
| author_sort | Lay, Usman Salihu |
| collection | UPM |
| description | Cameron Highland is a popular tourist hub in the mountainous area of Peninsular Malaysia. Most communities in this area suffer frequent incidence of debris flow, especially during monsoon seasons. Despite the loss of lives and properties recorded annually from debris flow, most studies in the region concentrate on landslides and flood susceptibilities. In this study, debris-flow susceptibility prediction was carried out using two data mining techniques; Multivariate Adaptive Regression Splines (MARS) and Support Vector Regression (SVR) models. The existing inventory of debris-flow events (640 points) were selected for training 70% (448) and validation 30% (192). Twelve conditioning factors namely; elevation, plan-curvature, slope angle, total curvature, slope aspect, Stream Transport Index (STI), profile curvature, roughness index, Stream Catchment Area (SCA), Stream Power Index (SPI), Topographic Wetness Index (TWI) and Topographic Position Index (TPI) were selected from Light Detection and Ranging (LiDAR)-derived Digital Elevation Model (DEM) data. Multi-collinearity was checked using Information Factor, Cramer's V, and Gini Index to identify the relative importance of conditioning factors. The susceptibility models were produced and categorized into five classes; not-susceptible, low, moderate, high and very-high classes. Models performances were evaluated using success and prediction rates where the area under the curve (AUC) showed a higher performance of MARS (93% and 83%) over SVR (76% and 72%). The result of this study will be important in contingency hazards and risks management plans to reduce the loss of lives and properties in the area. |
| first_indexed | 2024-03-06T10:29:10Z |
| format | Article |
| id | upm.eprints-81078 |
| institution | Universiti Putra Malaysia |
| language | English |
| last_indexed | 2024-03-06T10:29:10Z |
| publishDate | 2019 |
| publisher | Multidisciplinary Digital Publishing Institute |
| record_format | dspace |
| spelling | upm.eprints-810782021-05-07T00:48:17Z http://psasir.upm.edu.my/id/eprint/81078/ Data mining and statistical approaches in debris-flow susceptibility modelling using airborne LiDAR data Lay, Usman Salihu Pradhan, Biswajeet Md Yusoff, Zainuddin Abdullah, Ahmad Fikri Aryal, Jagannath Park, Hyuck Jin Cameron Highland is a popular tourist hub in the mountainous area of Peninsular Malaysia. Most communities in this area suffer frequent incidence of debris flow, especially during monsoon seasons. Despite the loss of lives and properties recorded annually from debris flow, most studies in the region concentrate on landslides and flood susceptibilities. In this study, debris-flow susceptibility prediction was carried out using two data mining techniques; Multivariate Adaptive Regression Splines (MARS) and Support Vector Regression (SVR) models. The existing inventory of debris-flow events (640 points) were selected for training 70% (448) and validation 30% (192). Twelve conditioning factors namely; elevation, plan-curvature, slope angle, total curvature, slope aspect, Stream Transport Index (STI), profile curvature, roughness index, Stream Catchment Area (SCA), Stream Power Index (SPI), Topographic Wetness Index (TWI) and Topographic Position Index (TPI) were selected from Light Detection and Ranging (LiDAR)-derived Digital Elevation Model (DEM) data. Multi-collinearity was checked using Information Factor, Cramer's V, and Gini Index to identify the relative importance of conditioning factors. The susceptibility models were produced and categorized into five classes; not-susceptible, low, moderate, high and very-high classes. Models performances were evaluated using success and prediction rates where the area under the curve (AUC) showed a higher performance of MARS (93% and 83%) over SVR (76% and 72%). The result of this study will be important in contingency hazards and risks management plans to reduce the loss of lives and properties in the area. Multidisciplinary Digital Publishing Institute 2019 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/81078/1/DEBRIS.pdf Lay, Usman Salihu and Pradhan, Biswajeet and Md Yusoff, Zainuddin and Abdullah, Ahmad Fikri and Aryal, Jagannath and Park, Hyuck Jin (2019) Data mining and statistical approaches in debris-flow susceptibility modelling using airborne LiDAR data. Sensors, 19 (16). pp. 1-32. ISSN 1424-8220 https://www.mdpi.com/1424-8220/19/16/3451 10.3390/s19163451 |
| spellingShingle | Lay, Usman Salihu Pradhan, Biswajeet Md Yusoff, Zainuddin Abdullah, Ahmad Fikri Aryal, Jagannath Park, Hyuck Jin Data mining and statistical approaches in debris-flow susceptibility modelling using airborne LiDAR data |
| title | Data mining and statistical approaches in debris-flow susceptibility modelling using airborne LiDAR data |
| title_full | Data mining and statistical approaches in debris-flow susceptibility modelling using airborne LiDAR data |
| title_fullStr | Data mining and statistical approaches in debris-flow susceptibility modelling using airborne LiDAR data |
| title_full_unstemmed | Data mining and statistical approaches in debris-flow susceptibility modelling using airborne LiDAR data |
| title_short | Data mining and statistical approaches in debris-flow susceptibility modelling using airborne LiDAR data |
| title_sort | data mining and statistical approaches in debris flow susceptibility modelling using airborne lidar data |
| url | http://psasir.upm.edu.my/id/eprint/81078/1/DEBRIS.pdf |
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