Landslide Susceptibility Mapping Using Single Machine Learning Models: A Case Study from Pithoragarh District, India
Landslides are one of the most devastating natural hazards causing huge loss of life and damage to properties and infrastructures and adversely affecting the socioeconomy of the country. Landslides occur in hilly and mountainous areas all over the world. Single, ensemble, and hybrid machine learning...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Advances in Civil Engineering |
| Online Access: | http://dx.doi.org/10.1155/2021/9934732 |
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| author | Trinh Quoc Ngo Nguyen Duc Dam Nadhir Al-Ansari Mahdis Amiri Tran Van Phong Indra Prakash Hiep Van Le Hanh Bich Thi Nguyen Binh Thai Pham |
| author_facet | Trinh Quoc Ngo Nguyen Duc Dam Nadhir Al-Ansari Mahdis Amiri Tran Van Phong Indra Prakash Hiep Van Le Hanh Bich Thi Nguyen Binh Thai Pham |
| author_sort | Trinh Quoc Ngo |
| collection | DOAJ |
| description | Landslides are one of the most devastating natural hazards causing huge loss of life and damage to properties and infrastructures and adversely affecting the socioeconomy of the country. Landslides occur in hilly and mountainous areas all over the world. Single, ensemble, and hybrid machine learning (ML) models have been used in landslide studies for better landslide susceptibility mapping and risk management. In the present study, we have used three single ML models, namely, linear discriminant analysis (LDA), logistic regression (LR), and radial basis function network (RBFN), for landslide susceptibility mapping at Pithoragarh district, as these models are easy to apply and so far they have not been used for landslide study in this area. The main objective of this study is to evaluate the performance of these single models for correctly identifying landslide susceptible zones for their further application in other areas. For this, ten important landslide affecting factors, namely, slope, aspect, curvature, elevation, land cover, lithology, geomorphology, distance to rivers, distance to roads, and overburden depth based on the local geoenvironmental conditions, were considered for the modeling. Landslide inventory of past 398 landslide events was used in the development of models. The data of past landslide events (locations) was randomly divided into a 70/30 ratio for training (70%) and validation (30%) of the models. Standard statistical measures, namely, accuracy (ACC), specificity (SPF), sensitivity (SST), positive predictive value (PPV), negative predictive value (NPV), Kappa, root mean square error (RMSE), and area under the receiver operating characteristic curve (AUC), were used to evaluate the performance of the models. Results indicated that the performance of all the models is very good (AUC > 0.90) and that of the LR model is the best (AUC = 0.926). Therefore, these single ML models can be used for the development of accurate landslide susceptibility maps. Our study demonstrated that the single models which are easy to use and can compete with the complex ensemble/hybrid models can be applied for landslide susceptibility mapping in landslide-prone areas. |
| format | Article |
| id | doaj-art-453f883182dd4a8cb5db86a23e4c8826 |
| institution | Kabale University |
| issn | 1687-8086 1687-8094 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Civil Engineering |
| spelling | doaj-art-453f883182dd4a8cb5db86a23e4c88262025-02-03T06:10:45ZengWileyAdvances in Civil Engineering1687-80861687-80942021-01-01202110.1155/2021/99347329934732Landslide Susceptibility Mapping Using Single Machine Learning Models: A Case Study from Pithoragarh District, IndiaTrinh Quoc Ngo0Nguyen Duc Dam1Nadhir Al-Ansari2Mahdis Amiri3Tran Van Phong4Indra Prakash5Hiep Van Le6Hanh Bich Thi Nguyen7Binh Thai Pham8University of Transport Technology, Hanoi 100000, VietnamUniversity of Transport Technology, Hanoi 100000, VietnamDepartment of Civil, Environmental and Natural Resources Engineering, Lulea University of Technology, Lulea 97187, SwedenDepartment of Watershed & Arid Zone Management, Gorgan University of Agricultural Sciences & Natural Resources, Gorgan 4918943464, IranInstitute of Geological Sciences, Vietnam Academy of Science and Technology (VAST), 84 Chua Lang, Dong Da, Hanoi, VietnamDDG (R) Geological Survey of India, Gandhinagar 382010, IndiaUniversity of Transport Technology, Hanoi 100000, VietnamUniversity of Transport Technology, Hanoi 100000, VietnamUniversity of Transport Technology, Hanoi 100000, VietnamLandslides are one of the most devastating natural hazards causing huge loss of life and damage to properties and infrastructures and adversely affecting the socioeconomy of the country. Landslides occur in hilly and mountainous areas all over the world. Single, ensemble, and hybrid machine learning (ML) models have been used in landslide studies for better landslide susceptibility mapping and risk management. In the present study, we have used three single ML models, namely, linear discriminant analysis (LDA), logistic regression (LR), and radial basis function network (RBFN), for landslide susceptibility mapping at Pithoragarh district, as these models are easy to apply and so far they have not been used for landslide study in this area. The main objective of this study is to evaluate the performance of these single models for correctly identifying landslide susceptible zones for their further application in other areas. For this, ten important landslide affecting factors, namely, slope, aspect, curvature, elevation, land cover, lithology, geomorphology, distance to rivers, distance to roads, and overburden depth based on the local geoenvironmental conditions, were considered for the modeling. Landslide inventory of past 398 landslide events was used in the development of models. The data of past landslide events (locations) was randomly divided into a 70/30 ratio for training (70%) and validation (30%) of the models. Standard statistical measures, namely, accuracy (ACC), specificity (SPF), sensitivity (SST), positive predictive value (PPV), negative predictive value (NPV), Kappa, root mean square error (RMSE), and area under the receiver operating characteristic curve (AUC), were used to evaluate the performance of the models. Results indicated that the performance of all the models is very good (AUC > 0.90) and that of the LR model is the best (AUC = 0.926). Therefore, these single ML models can be used for the development of accurate landslide susceptibility maps. Our study demonstrated that the single models which are easy to use and can compete with the complex ensemble/hybrid models can be applied for landslide susceptibility mapping in landslide-prone areas.http://dx.doi.org/10.1155/2021/9934732 |
| spellingShingle | Trinh Quoc Ngo Nguyen Duc Dam Nadhir Al-Ansari Mahdis Amiri Tran Van Phong Indra Prakash Hiep Van Le Hanh Bich Thi Nguyen Binh Thai Pham Landslide Susceptibility Mapping Using Single Machine Learning Models: A Case Study from Pithoragarh District, India Advances in Civil Engineering |
| title | Landslide Susceptibility Mapping Using Single Machine Learning Models: A Case Study from Pithoragarh District, India |
| title_full | Landslide Susceptibility Mapping Using Single Machine Learning Models: A Case Study from Pithoragarh District, India |
| title_fullStr | Landslide Susceptibility Mapping Using Single Machine Learning Models: A Case Study from Pithoragarh District, India |
| title_full_unstemmed | Landslide Susceptibility Mapping Using Single Machine Learning Models: A Case Study from Pithoragarh District, India |
| title_short | Landslide Susceptibility Mapping Using Single Machine Learning Models: A Case Study from Pithoragarh District, India |
| title_sort | landslide susceptibility mapping using single machine learning models a case study from pithoragarh district india |
| url | http://dx.doi.org/10.1155/2021/9934732 |
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