Development of integrated deep learning and machine learning algorithm for the assessment of landslide hazard potential
Abstract In mountainous regions subjected to landslides, susceptibility mapping of these geohazards is necessary for averting and alleviating perilous dangers. The present study applies an integrated methodology for assessing landslide susceptibility of northern Pakistan (Mansehra and Muzaffarabad d...
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Gespeichert in:
| Autor*in: |
Aslam, Bilal [verfasserIn] Zafar, Adeel [verfasserIn] Khalil, Umer [verfasserIn] |
|---|
| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021. corrected publication 2021 |
|---|
| Übergeordnetes Werk: |
Enthalten in: Soft Computing - Springer-Verlag, 2003, 25(2021), 21 vom: 13. Aug., Seite 13493-13512 |
|---|---|
| Übergeordnetes Werk: |
volume:25 ; year:2021 ; number:21 ; day:13 ; month:08 ; pages:13493-13512 |
| Links: |
|---|
| DOI / URN: |
10.1007/s00500-021-06105-5 |
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SPR045276412 |
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| 520 | |a Abstract In mountainous regions subjected to landslides, susceptibility mapping of these geohazards is necessary for averting and alleviating perilous dangers. The present study applies an integrated methodology for assessing landslide susceptibility of northern Pakistan (Mansehra and Muzaffarabad districts). Three orthodox machine learning (ML) classification techniques, including support vector machine (SVM), logistic regression (LR), and random forest (RF), are integrated with convolutional neural network (CNN) used. For training and testing of the models, spatial datasets consisting of 3251 sites of historical slopes are used in a ratio of 70:30. Initially, a total of 16 influencing factors for landslide modelling were established. The training dataset specifically constructs three hybrid models CNN-SVM, CNN-LR, and CNN-RF. Then, final susceptibility maps (LSMs) will be built using these trained models. These models will be implemented. For having a comparison, the LSMs are also prepared using the considered ML models individually. In the end, multiple statistical methods are used to validate and compare the performance of these models. The results of the analysis have revealed the efficiency of applying the projected ML models by combining them with the CNN technique. Therefore, in other sensitive regions with comparable geo-environmental conditions, the future hybrid designs can be used effectively for landslide susceptibility studies. | ||
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10.1007/s00500-021-06105-5 doi (DE-627)SPR045276412 (SPR)s00500-021-06105-5-e DE-627 ger DE-627 rakwb eng Aslam, Bilal verfasserin aut Development of integrated deep learning and machine learning algorithm for the assessment of landslide hazard potential 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021. corrected publication 2021 Abstract In mountainous regions subjected to landslides, susceptibility mapping of these geohazards is necessary for averting and alleviating perilous dangers. The present study applies an integrated methodology for assessing landslide susceptibility of northern Pakistan (Mansehra and Muzaffarabad districts). Three orthodox machine learning (ML) classification techniques, including support vector machine (SVM), logistic regression (LR), and random forest (RF), are integrated with convolutional neural network (CNN) used. For training and testing of the models, spatial datasets consisting of 3251 sites of historical slopes are used in a ratio of 70:30. Initially, a total of 16 influencing factors for landslide modelling were established. The training dataset specifically constructs three hybrid models CNN-SVM, CNN-LR, and CNN-RF. Then, final susceptibility maps (LSMs) will be built using these trained models. These models will be implemented. For having a comparison, the LSMs are also prepared using the considered ML models individually. In the end, multiple statistical methods are used to validate and compare the performance of these models. The results of the analysis have revealed the efficiency of applying the projected ML models by combining them with the CNN technique. Therefore, in other sensitive regions with comparable geo-environmental conditions, the future hybrid designs can be used effectively for landslide susceptibility studies. Northern Pakistan (dpeaa)DE-He213 Spatial datasets (dpeaa)DE-He213 Feature extraction (dpeaa)DE-He213 Orthodox machine learning (dpeaa)DE-He213 Landslide susceptibility maps (dpeaa)DE-He213 CNN (dpeaa)DE-He213 Zafar, Adeel verfasserin aut Khalil, Umer verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 25(2021), 21 vom: 13. Aug., Seite 13493-13512 (DE-627)SPR006469531 nnns volume:25 year:2021 number:21 day:13 month:08 pages:13493-13512 https://dx.doi.org/10.1007/s00500-021-06105-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 25 2021 21 13 08 13493-13512 |
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10.1007/s00500-021-06105-5 doi (DE-627)SPR045276412 (SPR)s00500-021-06105-5-e DE-627 ger DE-627 rakwb eng Aslam, Bilal verfasserin aut Development of integrated deep learning and machine learning algorithm for the assessment of landslide hazard potential 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021. corrected publication 2021 Abstract In mountainous regions subjected to landslides, susceptibility mapping of these geohazards is necessary for averting and alleviating perilous dangers. The present study applies an integrated methodology for assessing landslide susceptibility of northern Pakistan (Mansehra and Muzaffarabad districts). Three orthodox machine learning (ML) classification techniques, including support vector machine (SVM), logistic regression (LR), and random forest (RF), are integrated with convolutional neural network (CNN) used. For training and testing of the models, spatial datasets consisting of 3251 sites of historical slopes are used in a ratio of 70:30. Initially, a total of 16 influencing factors for landslide modelling were established. The training dataset specifically constructs three hybrid models CNN-SVM, CNN-LR, and CNN-RF. Then, final susceptibility maps (LSMs) will be built using these trained models. These models will be implemented. For having a comparison, the LSMs are also prepared using the considered ML models individually. In the end, multiple statistical methods are used to validate and compare the performance of these models. The results of the analysis have revealed the efficiency of applying the projected ML models by combining them with the CNN technique. Therefore, in other sensitive regions with comparable geo-environmental conditions, the future hybrid designs can be used effectively for landslide susceptibility studies. Northern Pakistan (dpeaa)DE-He213 Spatial datasets (dpeaa)DE-He213 Feature extraction (dpeaa)DE-He213 Orthodox machine learning (dpeaa)DE-He213 Landslide susceptibility maps (dpeaa)DE-He213 CNN (dpeaa)DE-He213 Zafar, Adeel verfasserin aut Khalil, Umer verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 25(2021), 21 vom: 13. Aug., Seite 13493-13512 (DE-627)SPR006469531 nnns volume:25 year:2021 number:21 day:13 month:08 pages:13493-13512 https://dx.doi.org/10.1007/s00500-021-06105-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 25 2021 21 13 08 13493-13512 |
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10.1007/s00500-021-06105-5 doi (DE-627)SPR045276412 (SPR)s00500-021-06105-5-e DE-627 ger DE-627 rakwb eng Aslam, Bilal verfasserin aut Development of integrated deep learning and machine learning algorithm for the assessment of landslide hazard potential 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021. corrected publication 2021 Abstract In mountainous regions subjected to landslides, susceptibility mapping of these geohazards is necessary for averting and alleviating perilous dangers. The present study applies an integrated methodology for assessing landslide susceptibility of northern Pakistan (Mansehra and Muzaffarabad districts). Three orthodox machine learning (ML) classification techniques, including support vector machine (SVM), logistic regression (LR), and random forest (RF), are integrated with convolutional neural network (CNN) used. For training and testing of the models, spatial datasets consisting of 3251 sites of historical slopes are used in a ratio of 70:30. Initially, a total of 16 influencing factors for landslide modelling were established. The training dataset specifically constructs three hybrid models CNN-SVM, CNN-LR, and CNN-RF. Then, final susceptibility maps (LSMs) will be built using these trained models. These models will be implemented. For having a comparison, the LSMs are also prepared using the considered ML models individually. In the end, multiple statistical methods are used to validate and compare the performance of these models. The results of the analysis have revealed the efficiency of applying the projected ML models by combining them with the CNN technique. Therefore, in other sensitive regions with comparable geo-environmental conditions, the future hybrid designs can be used effectively for landslide susceptibility studies. Northern Pakistan (dpeaa)DE-He213 Spatial datasets (dpeaa)DE-He213 Feature extraction (dpeaa)DE-He213 Orthodox machine learning (dpeaa)DE-He213 Landslide susceptibility maps (dpeaa)DE-He213 CNN (dpeaa)DE-He213 Zafar, Adeel verfasserin aut Khalil, Umer verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 25(2021), 21 vom: 13. Aug., Seite 13493-13512 (DE-627)SPR006469531 nnns volume:25 year:2021 number:21 day:13 month:08 pages:13493-13512 https://dx.doi.org/10.1007/s00500-021-06105-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 25 2021 21 13 08 13493-13512 |
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10.1007/s00500-021-06105-5 doi (DE-627)SPR045276412 (SPR)s00500-021-06105-5-e DE-627 ger DE-627 rakwb eng Aslam, Bilal verfasserin aut Development of integrated deep learning and machine learning algorithm for the assessment of landslide hazard potential 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021. corrected publication 2021 Abstract In mountainous regions subjected to landslides, susceptibility mapping of these geohazards is necessary for averting and alleviating perilous dangers. The present study applies an integrated methodology for assessing landslide susceptibility of northern Pakistan (Mansehra and Muzaffarabad districts). Three orthodox machine learning (ML) classification techniques, including support vector machine (SVM), logistic regression (LR), and random forest (RF), are integrated with convolutional neural network (CNN) used. For training and testing of the models, spatial datasets consisting of 3251 sites of historical slopes are used in a ratio of 70:30. Initially, a total of 16 influencing factors for landslide modelling were established. The training dataset specifically constructs three hybrid models CNN-SVM, CNN-LR, and CNN-RF. Then, final susceptibility maps (LSMs) will be built using these trained models. These models will be implemented. For having a comparison, the LSMs are also prepared using the considered ML models individually. In the end, multiple statistical methods are used to validate and compare the performance of these models. The results of the analysis have revealed the efficiency of applying the projected ML models by combining them with the CNN technique. Therefore, in other sensitive regions with comparable geo-environmental conditions, the future hybrid designs can be used effectively for landslide susceptibility studies. Northern Pakistan (dpeaa)DE-He213 Spatial datasets (dpeaa)DE-He213 Feature extraction (dpeaa)DE-He213 Orthodox machine learning (dpeaa)DE-He213 Landslide susceptibility maps (dpeaa)DE-He213 CNN (dpeaa)DE-He213 Zafar, Adeel verfasserin aut Khalil, Umer verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 25(2021), 21 vom: 13. Aug., Seite 13493-13512 (DE-627)SPR006469531 nnns volume:25 year:2021 number:21 day:13 month:08 pages:13493-13512 https://dx.doi.org/10.1007/s00500-021-06105-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 25 2021 21 13 08 13493-13512 |
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10.1007/s00500-021-06105-5 doi (DE-627)SPR045276412 (SPR)s00500-021-06105-5-e DE-627 ger DE-627 rakwb eng Aslam, Bilal verfasserin aut Development of integrated deep learning and machine learning algorithm for the assessment of landslide hazard potential 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021. corrected publication 2021 Abstract In mountainous regions subjected to landslides, susceptibility mapping of these geohazards is necessary for averting and alleviating perilous dangers. The present study applies an integrated methodology for assessing landslide susceptibility of northern Pakistan (Mansehra and Muzaffarabad districts). Three orthodox machine learning (ML) classification techniques, including support vector machine (SVM), logistic regression (LR), and random forest (RF), are integrated with convolutional neural network (CNN) used. For training and testing of the models, spatial datasets consisting of 3251 sites of historical slopes are used in a ratio of 70:30. Initially, a total of 16 influencing factors for landslide modelling were established. The training dataset specifically constructs three hybrid models CNN-SVM, CNN-LR, and CNN-RF. Then, final susceptibility maps (LSMs) will be built using these trained models. These models will be implemented. For having a comparison, the LSMs are also prepared using the considered ML models individually. In the end, multiple statistical methods are used to validate and compare the performance of these models. The results of the analysis have revealed the efficiency of applying the projected ML models by combining them with the CNN technique. Therefore, in other sensitive regions with comparable geo-environmental conditions, the future hybrid designs can be used effectively for landslide susceptibility studies. Northern Pakistan (dpeaa)DE-He213 Spatial datasets (dpeaa)DE-He213 Feature extraction (dpeaa)DE-He213 Orthodox machine learning (dpeaa)DE-He213 Landslide susceptibility maps (dpeaa)DE-He213 CNN (dpeaa)DE-He213 Zafar, Adeel verfasserin aut Khalil, Umer verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 25(2021), 21 vom: 13. Aug., Seite 13493-13512 (DE-627)SPR006469531 nnns volume:25 year:2021 number:21 day:13 month:08 pages:13493-13512 https://dx.doi.org/10.1007/s00500-021-06105-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 25 2021 21 13 08 13493-13512 |
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Development of integrated deep learning and machine learning algorithm for the assessment of landslide hazard potential |
| abstract |
Abstract In mountainous regions subjected to landslides, susceptibility mapping of these geohazards is necessary for averting and alleviating perilous dangers. The present study applies an integrated methodology for assessing landslide susceptibility of northern Pakistan (Mansehra and Muzaffarabad districts). Three orthodox machine learning (ML) classification techniques, including support vector machine (SVM), logistic regression (LR), and random forest (RF), are integrated with convolutional neural network (CNN) used. For training and testing of the models, spatial datasets consisting of 3251 sites of historical slopes are used in a ratio of 70:30. Initially, a total of 16 influencing factors for landslide modelling were established. The training dataset specifically constructs three hybrid models CNN-SVM, CNN-LR, and CNN-RF. Then, final susceptibility maps (LSMs) will be built using these trained models. These models will be implemented. For having a comparison, the LSMs are also prepared using the considered ML models individually. In the end, multiple statistical methods are used to validate and compare the performance of these models. The results of the analysis have revealed the efficiency of applying the projected ML models by combining them with the CNN technique. Therefore, in other sensitive regions with comparable geo-environmental conditions, the future hybrid designs can be used effectively for landslide susceptibility studies. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021. corrected publication 2021 |
| abstractGer |
Abstract In mountainous regions subjected to landslides, susceptibility mapping of these geohazards is necessary for averting and alleviating perilous dangers. The present study applies an integrated methodology for assessing landslide susceptibility of northern Pakistan (Mansehra and Muzaffarabad districts). Three orthodox machine learning (ML) classification techniques, including support vector machine (SVM), logistic regression (LR), and random forest (RF), are integrated with convolutional neural network (CNN) used. For training and testing of the models, spatial datasets consisting of 3251 sites of historical slopes are used in a ratio of 70:30. Initially, a total of 16 influencing factors for landslide modelling were established. The training dataset specifically constructs three hybrid models CNN-SVM, CNN-LR, and CNN-RF. Then, final susceptibility maps (LSMs) will be built using these trained models. These models will be implemented. For having a comparison, the LSMs are also prepared using the considered ML models individually. In the end, multiple statistical methods are used to validate and compare the performance of these models. The results of the analysis have revealed the efficiency of applying the projected ML models by combining them with the CNN technique. Therefore, in other sensitive regions with comparable geo-environmental conditions, the future hybrid designs can be used effectively for landslide susceptibility studies. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021. corrected publication 2021 |
| abstract_unstemmed |
Abstract In mountainous regions subjected to landslides, susceptibility mapping of these geohazards is necessary for averting and alleviating perilous dangers. The present study applies an integrated methodology for assessing landslide susceptibility of northern Pakistan (Mansehra and Muzaffarabad districts). Three orthodox machine learning (ML) classification techniques, including support vector machine (SVM), logistic regression (LR), and random forest (RF), are integrated with convolutional neural network (CNN) used. For training and testing of the models, spatial datasets consisting of 3251 sites of historical slopes are used in a ratio of 70:30. Initially, a total of 16 influencing factors for landslide modelling were established. The training dataset specifically constructs three hybrid models CNN-SVM, CNN-LR, and CNN-RF. Then, final susceptibility maps (LSMs) will be built using these trained models. These models will be implemented. For having a comparison, the LSMs are also prepared using the considered ML models individually. In the end, multiple statistical methods are used to validate and compare the performance of these models. The results of the analysis have revealed the efficiency of applying the projected ML models by combining them with the CNN technique. Therefore, in other sensitive regions with comparable geo-environmental conditions, the future hybrid designs can be used effectively for landslide susceptibility studies. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021. corrected publication 2021 |
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21 |
| title_short |
Development of integrated deep learning and machine learning algorithm for the assessment of landslide hazard potential |
| url |
https://dx.doi.org/10.1007/s00500-021-06105-5 |
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| author2 |
Zafar, Adeel Khalil, Umer |
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Zafar, Adeel Khalil, Umer |
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| doi_str |
10.1007/s00500-021-06105-5 |
| up_date |
2024-07-03T14:57:08.270Z |
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