Geohazards susceptibility assessment along the upper indus basin using four machine learning and statistical models
The China-Pakistan Economic Corridor (CPEC) project passes through the Karakoram Highway in northern Pakistan, which is one of the most hazardous regions of the world. The most common hazards in this region are landslides and debris flows, which result in loss of life and severe infrastructure damag...
| Main Authors: | , , , , , , , , , , |
|---|---|
| Format: | Journal Article |
| Language: | English |
| Published: |
MDPI
2021
|
| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.11937/86449 |
| _version_ | 1848764825542328320 |
|---|---|
| author | Ahmad, H. Ningsheng, C. Rahman, M. Islam, M.M. Pourghasemi, H.R. Hussain, S.F. Habumugisha, J.M. Liu, E. Zheng, H. Ni, H. Dewan, Ashraf |
| author_facet | Ahmad, H. Ningsheng, C. Rahman, M. Islam, M.M. Pourghasemi, H.R. Hussain, S.F. Habumugisha, J.M. Liu, E. Zheng, H. Ni, H. Dewan, Ashraf |
| author_sort | Ahmad, H. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | The China-Pakistan Economic Corridor (CPEC) project passes through the Karakoram Highway in northern Pakistan, which is one of the most hazardous regions of the world. The most common hazards in this region are landslides and debris flows, which result in loss of life and severe infrastructure damage every year. This study assessed geohazards (landslides and debris flows) and developed susceptibility maps by considering four standalone machine-learning and statistical approaches, namely, Logistic Regression (LR), Shannon Entropy (SE),Weights-of-Evidence (WoE), and Frequency Ratio (FR) models. To this end, geohazard inventories were prepared using remote sensing techniques with field observations and historical hazard datasets. The spatial relationship of thirteen conditioning factors, namely, slope (degree), distance to faults, geology, elevation, distance to rivers, slope aspect, distance to road, annual mean rainfall, normalized difference vegetation index, profile curvature, stream power index, topographic wetness index, and land cover, with hazard distribution was analyzed. The results showed that faults, slope angles, elevation, lithology, land cover, and mean annual rainfall play a key role in controlling the spatial distribution of geohazards in the study area. The final susceptibility maps were validated against ground truth points and by plotting Area Under the Receiver Operating Characteristic (AUROC) curves. According to the AUROC curves, the success rates of the LR, WoE, FR, and SE models were 85.30%, 76.00, 74.60%, and 71.40%, and their prediction rates were 83.10%, 75.00%, 73.50%, and 70.10%, respectively; these values show higher performance of LR over the other three models. Furthermore, 11.19%, 9.24%, 10.18%, 39.14%, and 30.25% of the areas corresponded to classes of very-high, high, moderate, low, and very-low susceptibility, respectively. The developed geohazard susceptibility map can be used by relevant government officials for the smooth implementation of the CPEC project at the regional scale. |
| first_indexed | 2025-11-14T11:25:31Z |
| format | Journal Article |
| id | curtin-20.500.11937-86449 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T11:25:31Z |
| publishDate | 2021 |
| publisher | MDPI |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-864492021-11-25T07:02:50Z Geohazards susceptibility assessment along the upper indus basin using four machine learning and statistical models Ahmad, H. Ningsheng, C. Rahman, M. Islam, M.M. Pourghasemi, H.R. Hussain, S.F. Habumugisha, J.M. Liu, E. Zheng, H. Ni, H. Dewan, Ashraf Science & Technology Technology Physical Sciences Computer Science, Information Systems Geography, Physical Remote Sensing Computer Science Physical Geography China– Pakistan economic corridor landslides debris flows geohazards remote sensing LOGISTIC-REGRESSION FREQUENCY RATIO LANDSLIDE RISK DEBRIS FLOWS GIS HIMALAYA EARTHQUAKE PAKISTAN AREA LIFE The China-Pakistan Economic Corridor (CPEC) project passes through the Karakoram Highway in northern Pakistan, which is one of the most hazardous regions of the world. The most common hazards in this region are landslides and debris flows, which result in loss of life and severe infrastructure damage every year. This study assessed geohazards (landslides and debris flows) and developed susceptibility maps by considering four standalone machine-learning and statistical approaches, namely, Logistic Regression (LR), Shannon Entropy (SE),Weights-of-Evidence (WoE), and Frequency Ratio (FR) models. To this end, geohazard inventories were prepared using remote sensing techniques with field observations and historical hazard datasets. The spatial relationship of thirteen conditioning factors, namely, slope (degree), distance to faults, geology, elevation, distance to rivers, slope aspect, distance to road, annual mean rainfall, normalized difference vegetation index, profile curvature, stream power index, topographic wetness index, and land cover, with hazard distribution was analyzed. The results showed that faults, slope angles, elevation, lithology, land cover, and mean annual rainfall play a key role in controlling the spatial distribution of geohazards in the study area. The final susceptibility maps were validated against ground truth points and by plotting Area Under the Receiver Operating Characteristic (AUROC) curves. According to the AUROC curves, the success rates of the LR, WoE, FR, and SE models were 85.30%, 76.00, 74.60%, and 71.40%, and their prediction rates were 83.10%, 75.00%, 73.50%, and 70.10%, respectively; these values show higher performance of LR over the other three models. Furthermore, 11.19%, 9.24%, 10.18%, 39.14%, and 30.25% of the areas corresponded to classes of very-high, high, moderate, low, and very-low susceptibility, respectively. The developed geohazard susceptibility map can be used by relevant government officials for the smooth implementation of the CPEC project at the regional scale. 2021 Journal Article http://hdl.handle.net/20.500.11937/86449 10.3390/ijgi10050315 English http://creativecommons.org/licenses/by/4.0/ MDPI fulltext |
| spellingShingle | Science & Technology Technology Physical Sciences Computer Science, Information Systems Geography, Physical Remote Sensing Computer Science Physical Geography China– Pakistan economic corridor landslides debris flows geohazards remote sensing LOGISTIC-REGRESSION FREQUENCY RATIO LANDSLIDE RISK DEBRIS FLOWS GIS HIMALAYA EARTHQUAKE PAKISTAN AREA LIFE Ahmad, H. Ningsheng, C. Rahman, M. Islam, M.M. Pourghasemi, H.R. Hussain, S.F. Habumugisha, J.M. Liu, E. Zheng, H. Ni, H. Dewan, Ashraf Geohazards susceptibility assessment along the upper indus basin using four machine learning and statistical models |
| title | Geohazards susceptibility assessment along the upper indus basin using four machine learning and statistical models |
| title_full | Geohazards susceptibility assessment along the upper indus basin using four machine learning and statistical models |
| title_fullStr | Geohazards susceptibility assessment along the upper indus basin using four machine learning and statistical models |
| title_full_unstemmed | Geohazards susceptibility assessment along the upper indus basin using four machine learning and statistical models |
| title_short | Geohazards susceptibility assessment along the upper indus basin using four machine learning and statistical models |
| title_sort | geohazards susceptibility assessment along the upper indus basin using four machine learning and statistical models |
| topic | Science & Technology Technology Physical Sciences Computer Science, Information Systems Geography, Physical Remote Sensing Computer Science Physical Geography China– Pakistan economic corridor landslides debris flows geohazards remote sensing LOGISTIC-REGRESSION FREQUENCY RATIO LANDSLIDE RISK DEBRIS FLOWS GIS HIMALAYA EARTHQUAKE PAKISTAN AREA LIFE |
| url | http://hdl.handle.net/20.500.11937/86449 |