Comparison of optimized data-driven models for landslide susceptibility mapping.
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| Title: | Comparison of optimized data-driven models for landslide susceptibility mapping. |
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| Authors: | Ghayur Sadigh, Armin1 (AUTHOR), Alesheikh, Ali Asghar1 (AUTHOR), Bateni, Sayed M.2 (AUTHOR), Jun, Changhyun3 (AUTHOR) cjun@cau.ac.kr, Lee, Saro4,5 (AUTHOR), Nielson, Jeffrey R.6 (AUTHOR), Panahi, Mahdi7,8 (AUTHOR), Rezaie, Fatemeh4,5 (AUTHOR) rezaie@kigam.re.kr |
| Source: | Environment, Development & Sustainability. Jun2024, Vol. 26 Issue 6, p14665-14692. 28p. |
| Subject Terms: | *Landslide hazard analysis, *Landslides, Artificial neural networks, Receiver operating characteristic curves, Deep learning, Standard deviations, Convolutional neural networks |
| Geographic Terms: | Kermanshah (Kermanshahan, Iran), Iran |
| Abstract: | Locations prone to landslides must be identified and mapped to prevent landslide-related damage and casualties. Machine learning approaches have proven effective for such tasks and have thus been widely applied. However, owing to the rapid development of data-driven approaches, deep learning methods that can exhibit enhanced prediction accuracies have not been fully evaluated. Several researchers have compared different methods without optimizing them, whereas others optimized a single method using different algorithms and compared them. In this study, the performances of different fully optimized methods for landslide susceptibility mapping within the landslide-prone Kermanshah province of Iran were compared. The models, i.e., convolutional neural networks (CNNs), deep neural networks (DNNs), and support vector machine (SVM) frameworks were developed using 14 conditioning factors and a landslide inventory containing 110 historical landslide points. The models were optimized to maximize the area under the receiver operating characteristic curve (AUC), while maintaining their stability. The results showed that the CNN (accuracy = 0.88, root mean square error (RMSE) = 0.37220, and AUC = 0.88) outperformed the DNN (accuracy = 0.79, RMSE = 0.40364, and AUC = 0.82) and SVM (accuracy = 0.80, RMSE = 0.42827, and AUC = 0.80) models using the same testing dataset. Moreover, the CNN model exhibiting the highest robustness among the three models, given its smallest AUC difference between the training and testing datasets. Notably, the dataset used in this study had a low spatial accuracy and limited sample points, and thus, the CNN approach can be considered useful for susceptibility assessment in other landslide-prone regions worldwide, particularly areas with poor data quality and quantity. The most important conditioning factors for all models were rainfall and the distances from roads and drainages. [ABSTRACT FROM AUTHOR] |
| Copyright of Environment, Development & Sustainability is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
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| Items | – Name: Title Label: Title Group: Ti Data: Comparison of optimized data-driven models for landslide susceptibility mapping. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Ghayur+Sadigh%2C+Armin%22">Ghayur Sadigh, Armin</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Alesheikh%2C+Ali+Asghar%22">Alesheikh, Ali Asghar</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Bateni%2C+Sayed+M%2E%22">Bateni, Sayed M.</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Jun%2C+Changhyun%22">Jun, Changhyun</searchLink><relatesTo>3</relatesTo> (AUTHOR)<i> cjun@cau.ac.kr</i><br /><searchLink fieldCode="AR" term="%22Lee%2C+Saro%22">Lee, Saro</searchLink><relatesTo>4,5</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Nielson%2C+Jeffrey+R%2E%22">Nielson, Jeffrey R.</searchLink><relatesTo>6</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Panahi%2C+Mahdi%22">Panahi, Mahdi</searchLink><relatesTo>7,8</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Rezaie%2C+Fatemeh%22">Rezaie, Fatemeh</searchLink><relatesTo>4,5</relatesTo> (AUTHOR)<i> rezaie@kigam.re.kr</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Environment%2C+Development+%26+Sustainability%22">Environment, Development & Sustainability</searchLink>. Jun2024, Vol. 26 Issue 6, p14665-14692. 28p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Landslide+hazard+analysis%22">Landslide hazard analysis</searchLink><br />*<searchLink fieldCode="DE" term="%22Landslides%22">Landslides</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Receiver+operating+characteristic+curves%22">Receiver operating characteristic curves</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Standard+deviations%22">Standard deviations</searchLink><br /><searchLink fieldCode="DE" term="%22Convolutional+neural+networks%22">Convolutional neural networks</searchLink> – Name: SubjectGeographic Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Kermanshah+%28Kermanshahan%2C+Iran%29%22">Kermanshah (Kermanshahan, Iran)</searchLink><br /><searchLink fieldCode="DE" term="%22Iran%22">Iran</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Locations prone to landslides must be identified and mapped to prevent landslide-related damage and casualties. Machine learning approaches have proven effective for such tasks and have thus been widely applied. However, owing to the rapid development of data-driven approaches, deep learning methods that can exhibit enhanced prediction accuracies have not been fully evaluated. Several researchers have compared different methods without optimizing them, whereas others optimized a single method using different algorithms and compared them. In this study, the performances of different fully optimized methods for landslide susceptibility mapping within the landslide-prone Kermanshah province of Iran were compared. The models, i.e., convolutional neural networks (CNNs), deep neural networks (DNNs), and support vector machine (SVM) frameworks were developed using 14 conditioning factors and a landslide inventory containing 110 historical landslide points. The models were optimized to maximize the area under the receiver operating characteristic curve (AUC), while maintaining their stability. The results showed that the CNN (accuracy = 0.88, root mean square error (RMSE) = 0.37220, and AUC = 0.88) outperformed the DNN (accuracy = 0.79, RMSE = 0.40364, and AUC = 0.82) and SVM (accuracy = 0.80, RMSE = 0.42827, and AUC = 0.80) models using the same testing dataset. Moreover, the CNN model exhibiting the highest robustness among the three models, given its smallest AUC difference between the training and testing datasets. Notably, the dataset used in this study had a low spatial accuracy and limited sample points, and thus, the CNN approach can be considered useful for susceptibility assessment in other landslide-prone regions worldwide, particularly areas with poor data quality and quantity. The most important conditioning factors for all models were rainfall and the distances from roads and drainages. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Environment, Development & Sustainability is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s10668-023-03212-1 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 28 StartPage: 14665 Subjects: – SubjectFull: Landslide hazard analysis Type: general – SubjectFull: Landslides Type: general – SubjectFull: Artificial neural networks Type: general – SubjectFull: Receiver operating characteristic curves Type: general – SubjectFull: Deep learning Type: general – SubjectFull: Standard deviations Type: general – SubjectFull: Convolutional neural networks Type: general – SubjectFull: Kermanshah (Kermanshahan, Iran) Type: general – SubjectFull: Iran Type: general Titles: – TitleFull: Comparison of optimized data-driven models for landslide susceptibility mapping. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Ghayur Sadigh, Armin – PersonEntity: Name: NameFull: Alesheikh, Ali Asghar – PersonEntity: Name: NameFull: Bateni, Sayed M. – PersonEntity: Name: NameFull: Jun, Changhyun – PersonEntity: Name: NameFull: Lee, Saro – PersonEntity: Name: NameFull: Nielson, Jeffrey R. – PersonEntity: Name: NameFull: Panahi, Mahdi – PersonEntity: Name: NameFull: Rezaie, Fatemeh IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 1387585X Numbering: – Type: volume Value: 26 – Type: issue Value: 6 Titles: – TitleFull: Environment, Development & Sustainability Type: main |
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