Evaluation of the landslide susceptibility and its spatial difference in the whole Qinghai-Tibetan Plateau region by five learning algorithms
Landslides are considered as major natural hazards that cause enormous property damages and fatalities in Qinghai-Tibetan Plateau (QTP). In this article, we evaluated the landslide susceptibility, and its spatial differencing in the whole Qinghai-Tibetan Plateau region using five state-of-the-art le...
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| Published in | Geoscience letters Vol. 9; no. 1; pp. 1 - 25 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Cham
Springer International Publishing
14.02.2022
Springer Nature B.V SpringerOpen |
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| Online Access | Get full text |
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| Abstract | Landslides are considered as major natural hazards that cause enormous property damages and fatalities in Qinghai-Tibetan Plateau (QTP). In this article, we evaluated the landslide susceptibility, and its spatial differencing in the whole Qinghai-Tibetan Plateau region using five state-of-the-art learning algorithms; deep neural network (DNN), logistic regression (LR), Naïve Bayes (NB), random forest (RF), and support vector machine (SVM), differing from previous studies only in local areas of QTP. The 671 landslide events were considered, and thirteen landslide conditioning factors (LCFs) were derived for database generation, including annual rainfall, distance to drainage
(
Ds
d
)
, distance to faults
(
Ds
f
)
, drainage density (
D
d
)
, elevation (Elev), fault density
(
F
d
)
, lithology, normalized difference vegetation index (NDVI), plan curvature
(
Pl
c
)
, profile curvature
(
Pr
c
)
, slope
(
S
∘
)
, stream power index (SPI), and topographic wetness index (TWI). The multi-collinearity analysis and mean decrease Gini (MDG) were used to assess the suitability and predictability of these factors. Consequently, five landslide susceptibility prediction (LSP) maps were generated and validated using accuracy, area under the receiver operatic characteristic curve, sensitivity, and specificity. The MDG results demonstrated that the rainfall, elevation, and lithology were the most significant landslide conditioning factors ruling the occurrence of landslides in Qinghai-Tibetan Plateau. The LSP maps depicted that the north-northwestern and south-southeastern regions (< 32% of total area) were at a higher risk to landslide compared to the center, west, and northwest of the area (> 45% of total area). Moreover, among the five models with a high goodness-of-fit, RF model was highlighted as the superior one, by which higher accuracy of landslide susceptibility assessment and better prone areas management in QTP can be achieved compared to previous results.
Graphical Abstract |
|---|---|
| AbstractList | Landslides are considered as major natural hazards that cause enormous property damages and fatalities in Qinghai-Tibetan Plateau (QTP). In this article, we evaluated the landslide susceptibility, and its spatial differencing in the whole Qinghai-Tibetan Plateau region using five state-of-the-art learning algorithms; deep neural network (DNN), logistic regression (LR), Naïve Bayes (NB), random forest (RF), and support vector machine (SVM), differing from previous studies only in local areas of QTP. The 671 landslide events were considered, and thirteen landslide conditioning factors (LCFs) were derived for database generation, including annual rainfall, distance to drainage
(
Ds
d
)
, distance to faults
(
Ds
f
)
, drainage density (
D
d
)
, elevation (Elev), fault density
(
F
d
)
, lithology, normalized difference vegetation index (NDVI), plan curvature
(
Pl
c
)
, profile curvature
(
Pr
c
)
, slope
(
S
∘
)
, stream power index (SPI), and topographic wetness index (TWI). The multi-collinearity analysis and mean decrease Gini (MDG) were used to assess the suitability and predictability of these factors. Consequently, five landslide susceptibility prediction (LSP) maps were generated and validated using accuracy, area under the receiver operatic characteristic curve, sensitivity, and specificity. The MDG results demonstrated that the rainfall, elevation, and lithology were the most significant landslide conditioning factors ruling the occurrence of landslides in Qinghai-Tibetan Plateau. The LSP maps depicted that the north-northwestern and south-southeastern regions (< 32% of total area) were at a higher risk to landslide compared to the center, west, and northwest of the area (> 45% of total area). Moreover, among the five models with a high goodness-of-fit, RF model was highlighted as the superior one, by which higher accuracy of landslide susceptibility assessment and better prone areas management in QTP can be achieved compared to previous results.
Graphical Abstract Abstract Landslides are considered as major natural hazards that cause enormous property damages and fatalities in Qinghai-Tibetan Plateau (QTP). In this article, we evaluated the landslide susceptibility, and its spatial differencing in the whole Qinghai-Tibetan Plateau region using five state-of-the-art learning algorithms; deep neural network (DNN), logistic regression (LR), Naïve Bayes (NB), random forest (RF), and support vector machine (SVM), differing from previous studies only in local areas of QTP. The 671 landslide events were considered, and thirteen landslide conditioning factors (LCFs) were derived for database generation, including annual rainfall, distance to drainage $${(\mathrm{Ds}}_{\mathrm{d}})$$ ( Ds d ) , distance to faults $${(\mathrm{Ds}}_{\mathrm{f}})$$ ( Ds f ) , drainage density ( $${D}_{d})$$ D d ) , elevation (Elev), fault density $$({F}_{d})$$ ( F d ) , lithology, normalized difference vegetation index (NDVI), plan curvature $${(\mathrm{Pl}}_{\mathrm{c}})$$ ( Pl c ) , profile curvature $${(\mathrm{Pr}}_{\mathrm{c}})$$ ( Pr c ) , slope $${(S}^{^\circ })$$ ( S ∘ ) , stream power index (SPI), and topographic wetness index (TWI). The multi-collinearity analysis and mean decrease Gini (MDG) were used to assess the suitability and predictability of these factors. Consequently, five landslide susceptibility prediction (LSP) maps were generated and validated using accuracy, area under the receiver operatic characteristic curve, sensitivity, and specificity. The MDG results demonstrated that the rainfall, elevation, and lithology were the most significant landslide conditioning factors ruling the occurrence of landslides in Qinghai-Tibetan Plateau. The LSP maps depicted that the north-northwestern and south-southeastern regions (< 32% of total area) were at a higher risk to landslide compared to the center, west, and northwest of the area (> 45% of total area). Moreover, among the five models with a high goodness-of-fit, RF model was highlighted as the superior one, by which higher accuracy of landslide susceptibility assessment and better prone areas management in QTP can be achieved compared to previous results. Graphical Abstract Landslides are considered as major natural hazards that cause enormous property damages and fatalities in Qinghai-Tibetan Plateau (QTP). In this article, we evaluated the landslide susceptibility, and its spatial differencing in the whole Qinghai-Tibetan Plateau region using five state-of-the-art learning algorithms; deep neural network (DNN), logistic regression (LR), Naïve Bayes (NB), random forest (RF), and support vector machine (SVM), differing from previous studies only in local areas of QTP. The 671 landslide events were considered, and thirteen landslide conditioning factors (LCFs) were derived for database generation, including annual rainfall, distance to drainage (Dsd), distance to faults (Dsf), drainage density (Dd), elevation (Elev), fault density (Fd), lithology, normalized difference vegetation index (NDVI), plan curvature (Plc), profile curvature (Prc), slope (S∘), stream power index (SPI), and topographic wetness index (TWI). The multi-collinearity analysis and mean decrease Gini (MDG) were used to assess the suitability and predictability of these factors. Consequently, five landslide susceptibility prediction (LSP) maps were generated and validated using accuracy, area under the receiver operatic characteristic curve, sensitivity, and specificity. The MDG results demonstrated that the rainfall, elevation, and lithology were the most significant landslide conditioning factors ruling the occurrence of landslides in Qinghai-Tibetan Plateau. The LSP maps depicted that the north-northwestern and south-southeastern regions (< 32% of total area) were at a higher risk to landslide compared to the center, west, and northwest of the area (> 45% of total area). Moreover, among the five models with a high goodness-of-fit, RF model was highlighted as the superior one, by which higher accuracy of landslide susceptibility assessment and better prone areas management in QTP can be achieved compared to previous results. Landslides are considered as major natural hazards that cause enormous property damages and fatalities in Qinghai-Tibetan Plateau (QTP). In this article, we evaluated the landslide susceptibility, and its spatial differencing in the whole Qinghai-Tibetan Plateau region using five state-of-the-art learning algorithms; deep neural network (DNN), logistic regression (LR), Naïve Bayes (NB), random forest (RF), and support vector machine (SVM), differing from previous studies only in local areas of QTP. The 671 landslide events were considered, and thirteen landslide conditioning factors (LCFs) were derived for database generation, including annual rainfall, distance to drainage $${(\mathrm{Ds}}_{\mathrm{d}})$$ ( Ds d ) , distance to faults $${(\mathrm{Ds}}_{\mathrm{f}})$$ ( Ds f ) , drainage density ( $${D}_{d})$$ D d ) , elevation (Elev), fault density $$({F}_{d})$$ ( F d ) , lithology, normalized difference vegetation index (NDVI), plan curvature $${(\mathrm{Pl}}_{\mathrm{c}})$$ ( Pl c ) , profile curvature $${(\mathrm{Pr}}_{\mathrm{c}})$$ ( Pr c ) , slope $${(S}^{^\circ })$$ ( S ∘ ) , stream power index (SPI), and topographic wetness index (TWI). The multi-collinearity analysis and mean decrease Gini (MDG) were used to assess the suitability and predictability of these factors. Consequently, five landslide susceptibility prediction (LSP) maps were generated and validated using accuracy, area under the receiver operatic characteristic curve, sensitivity, and specificity. The MDG results demonstrated that the rainfall, elevation, and lithology were the most significant landslide conditioning factors ruling the occurrence of landslides in Qinghai-Tibetan Plateau. The LSP maps depicted that the north-northwestern and south-southeastern regions (< 32% of total area) were at a higher risk to landslide compared to the center, west, and northwest of the area (> 45% of total area). Moreover, among the five models with a high goodness-of-fit, RF model was highlighted as the superior one, by which higher accuracy of landslide susceptibility assessment and better prone areas management in QTP can be achieved compared to previous results. Graphical Abstract |
| ArticleNumber | 9 |
| Author | Gholamnia, Mehdi Mukherjee, Saumitra Bonafoni, Stefania Sajadi, Payam Sang, Yan-Fang |
| Author_xml | – sequence: 1 givenname: Payam surname: Sajadi fullname: Sajadi, Payam organization: Key Laboratory of Water Cycle & Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences – sequence: 2 givenname: Yan-Fang orcidid: 0000-0001-6770-9311 surname: Sang fullname: Sang, Yan-Fang email: sangyf@igsnrr.ac.cn, sunsangyf@gmail.com organization: Key Laboratory of Water Cycle & Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Key Laboratory of Compound and Chained Natural Hazards Dynamics, Ministry of Emergency Management of China – sequence: 3 givenname: Mehdi surname: Gholamnia fullname: Gholamnia, Mehdi organization: Department of Civil Engineering, Sanandaj Branch – sequence: 4 givenname: Stefania surname: Bonafoni fullname: Bonafoni, Stefania organization: Department of Engineering, University of Perugia – sequence: 5 givenname: Saumitra surname: Mukherjee fullname: Mukherjee, Saumitra organization: School of Environmental Sciences, Jawaharlal Nehru University |
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| Snippet | Landslides are considered as major natural hazards that cause enormous property damages and fatalities in Qinghai-Tibetan Plateau (QTP). In this article, we... Abstract Landslides are considered as major natural hazards that cause enormous property damages and fatalities in Qinghai-Tibetan Plateau (QTP). In this... |
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| SubjectTerms | Accuracy Algorithms Annual rainfall Artificial neural networks Atmospheric Sciences Bayesian analysis Biogeosciences Collinearity Curvature Distance Drainage Drainage density Earth and Environmental Science Earth Sciences Elevation Evaluation Feature selection technique Geophysics/Geodesy Goodness of fit Landslide susceptibility Landslides Landslides & mudslides Learning algorithms Lithology Machine learning Machine learning algorithm Neural networks Normalized difference vegetative index Oceanography Planetology Plateaus Property damage Qinghai-Tibetan Plateau Rain Research Letter Spatial differencing Specificity Support vector machines Susceptibility |
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| Title | Evaluation of the landslide susceptibility and its spatial difference in the whole Qinghai-Tibetan Plateau region by five learning algorithms |
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