# Examination of Landslide Susceptibility Modeling Using Ensemble Learning and Factor Engineering
## 6 Conclusions
(2024) [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] Modelling landslide susceptibility prediction: A review and construction of semi-supervised imbalanced theory. _Earth-Sci. Rev._**2024**, _250_, 104700. [CrossRef]
* [PERSON] et al. (2023) [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON]; [PERSON] Uncertainties of landslide susceptibility prediction: Influences of random errors in landslide conditioning factors and errors reduction by low pass filter method. _J. Rock Mech. Geotech. Eng._**2023**, _16_, 213-230. [CrossRef]
* [PERSON] et al. (2024) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Machine learning-based predictions of current and future susceptibility to retrogressive thaw slumps across the Northern Hemisphere. _Adv. Clim. Change Res._**2024**, _15_, 253-264. [CrossRef]
* [PERSON] et al. (2022) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Inventory and Frequency of Retrogressive Thaw Slumps in Permafrost Region of the Qinghai-Tibet Plateau. _Geophys. Res. Lett._**2022**, _49_, e2022 GL099829. [CrossRef]
* [PERSON] et al. (2023) [PERSON]; [PERSON]; [PERSON]; [PERSON] Conventional data-driven landslide susceptibility models may only tell us half of the story: Potential underestimation of landslide impact areas depending on the modeling design. _Geomorphology_**2023**, _430_, 108638. [CrossRef]
* [PERSON] et al. (2020) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Integrating principal component analysis with statistically-based models for analysis of causal factors and landslide susceptibility mapping: A comparative study from the loess plateau area in Shanxi (China). _J. Clean. Prod._**2020**, _277_, 124159. [CrossRef]
* [PERSON] et al. (2021) [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON] A novel methodology for Groundwater Flooding Susceptibility assessment through Machine Learning techniques in a mixed-land use aquifer. _Sci. Total Environ._**2021**, _790_, 148067. [CrossRef]
* [PERSON] et al. (2020) [PERSON]; [PERSON]; [PERSON] Modeling landslide susceptibility using LogitBoost alternating decision trees and forest by penalizing attributes with the bagging ensemble. _Sci. Total Environ._**2020**, _718_, 137231. [CrossRef]
* [PERSON] et al. (2021) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] Modelling of shallow landslides with machine learning algorithms. _Geosci. Front._**2021**, _12_, 385-393. [CrossRef]
* [PERSON] et al. (2018) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] A review of statistically-based landslide susceptibility models. _Earth-Sci. Rev._**2018**, _180_, 60-91. [CrossRef]
* [PERSON] (2016) [PERSON]; [PERSON] National level landslide susceptibility assessment of Turkey utilizing public domain dataset. _Environ. Earth Sci._**2016**, _75_, 847. [CrossRef]
* [PERSON] and [PERSON] (2022) [PERSON]; [PERSON] [PERSON] Landslide Susceptibility Assessment in Medium-Scale: Case Studies from the Major Drainage Basins of Turkey. _Environ. Earth Sci._**2022**, _81_, 244. [CrossRef]
* [PERSON] et al. (2025) [PERSON]; [PERSON]; [PERSON]; [PERSON] Municipal landfill site selection using TOPSIS methodology: A case study for Polath, Ankara, Turkey. _Bull. Eng. Geol. Environ._**2025**, _84_, 126. [CrossRef]
* [PERSON] et al. (2021) [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Discontinuous rock slope stability analysis by limit equilibrium approaches--A review. _Int. J. Digit. Earth_**2021**, _14_, 1918-1941. [CrossRef]
* [PERSON] et al. (2023) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Double-index rainfall warning and probabilistic physically based model for fast-moving landslide hazard analysis in subtropical-typhoon area. _Landslides_**2023**, _21_, 753-773. [CrossRef]
* [PERSON] et al.