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Integrating the artificial intelligence and hybrid machine learning algorithms for improving the accuracy of spatial prediction of landslide hazards in Kurseong Himalayan Region

The aim of the current work is to compare susceptibility maps of landslides produced using machine learning techniques i.e. multilayer perception neural nets (MLP), kernel logistic regression (KLR), random forest (RF), and multivariate adaptive regression splines (MARS); novel ensemble approaches i.e. MLP-Bagging, KLR-Bagging, RF-Bagging and MARS-Bagging in the Kurseong-Himalayan region. For the ensemble models the RF, KLR, MLP and MARS were used as base classifiers, and Bagging was used as meta classifier. Another objective of the current work is to introduce and evaluate the effectiveness of the novel KLR-Bagging and MARS-Bagging ensembles in susceptibility to landslide. Compiling 303 landslide locations to calibrate and test the models, an inventory map was created. Eighteen LCFs were chosen using the Relief-F and multi-collinearity tests for mapping the landslide susceptibility. Applying receiver operating characteristic (ROC), precision, accuracy, incorrectly categorized proportion, mean-absolute-error (MAE), and root-mean-square-error (RMSE), the LSMs were subsequently verified. The different validation results showed RF-Bagging (AUC training 88.69% & testing 92.28%) with ensemble Meta classifier gives better performance than the MLP, KLR, RF, MARS, MLP-Bagging, KLR-Bagging, and MARS-Bagging based LSMs. RF model showed that the slope, altitude, rainfall, and geomorphology played the most vital role in landslide occurrence comparing the other LCFs. These results will help to reduce the losses caused by the landslides in the Kurseong and in other areas where geo-environmental and geological conditions more or less similar.

Availability
283551Perpustakaan BIG (Eksternal Harddisk)Available
Detail Information
Series Title
Artificial Intelligence in Geosciences
Call Number
551
Publisher
Beijing : KeAi Communications Co. Ltd..,
Collation
14 hlm PDF, 5.975 KB
Language
Inggris
ISBN/ISSN
2666-5441
Classification
551
Content Type
text
Media Type
-
Carrier Type
-
Edition
Vol.3, December 2022
Subject(s)
Random forest
Multilayer perception
Kernel logistic regression
Multivariate adaptive regression splines
Hybrid algorithms
Specific Detail Info
-
Statement of Responsibility
Other version/related

No other version available

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  • Integrating the artificial intelligence and hybrid machine learning algorithms for improving the accuracy of spatial prediction of landslide hazards in Kurseong Himalayan Region
    The aim of the current work is to compare susceptibility maps of landslides produced using machine learning techniques i.e. multilayer perception neural nets (MLP), kernel logistic regression (KLR), random forest (RF), and multivariate adaptive regression splines (MARS); novel ensemble approaches i.e. MLP-Bagging, KLR-Bagging, RF-Bagging and MARS-Bagging in the Kurseong-Himalayan region. For the ensemble models the RF, KLR, MLP and MARS were used as base classifiers, and Bagging was used as meta classifier. Another objective of the current work is to introduce and evaluate the effectiveness of the novel KLR-Bagging and MARS-Bagging ensembles in susceptibility to landslide. Compiling 303 landslide locations to calibrate and test the models, an inventory map was created. Eighteen LCFs were chosen using the Relief-F and multi-collinearity tests for mapping the landslide susceptibility. Applying receiver operating characteristic (ROC), precision, accuracy, incorrectly categorized proportion, mean-absolute-error (MAE), and root-mean-square-error (RMSE), the LSMs were subsequently verified. The different validation results showed RF-Bagging (AUC training 88.69% & testing 92.28%) with ensemble Meta classifier gives better performance than the MLP, KLR, RF, MARS, MLP-Bagging, KLR-Bagging, and MARS-Bagging based LSMs. RF model showed that the slope, altitude, rainfall, and geomorphology played the most vital role in landslide occurrence comparing the other LCFs. These results will help to reduce the losses caused by the landslides in the Kurseong and in other areas where geo-environmental and geological conditions more or less similar.
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