Predictive Capability of Deterministic and Statistical Models in Weathered Granite Soil Watershed

Pradhan, Ananta Man Singh; Oh, Jeongrim; Jung, Min-Su; Kim, Yun-Tae

doi:10.1007/978-3-319-05050-8_78

Cited by 3 publications

(2 citation statements)

References 9 publications

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“…Lee et al (2012) compared the effects of several of the upstream contributing area estimation methods for wetness index. Many studies have applied steady-state approaches for the prediction of landslides (Acharya et al, 2005;Terhorst and Kreja, 2009;Gomes et al, 2013;Michel et al, 2014;Pradhan et al, 2014). The steady-state approaches are useful for producing spatially distributed slope stability but are limited to the temporal prediction of the slope stability because of the steady-state description of hydrological fluxes.…”

Section: Physical Approachesmentioning

confidence: 99%

Survey of spatial and temporal landslide prediction methods and techniques

2016

Korean Journal of Agricultural Science

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Landslides are one of the most common natural hazards causing significant damage and casualties every year. In Korea, the increasing trend in landslide occurrence in recent decades, caused by climate change, has set off an alarm for researchers to find more reliable methods for landslide prediction. Therefore, an accurate landslide-susceptibility assessment is fundamental for preventing landslides and minimizing damages. However, analyzing the stability of a natural slope is not an easy task because it depends on numerous factors such as those related to vegetation, soil properties, soil moisture distribution, the amount and duration of rainfall, earthquakes, etc. A variety of different methods and techniques for evaluating landslide susceptibility have been proposed, but up to now no specific method or technique has been accepted as the standard method because it is very difficult to assess different methods with entirely different intrinsic and extrinsic data. Landslide prediction methods can fall into three categories: empirical, statistical, and physical approaches. This paper reviews previous research and surveys three groups of landslide prediction methods.

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Section: Physical Approachesmentioning

confidence: 99%

Survey of spatial and temporal landslide prediction methods and techniques

2016

Korean Journal of Agricultural Science

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“…Deterministic methods, on the other hand, quantify LS based on the physical principles governing slope stability [14,15]. They are pivotal for engineering design and understanding failure mechanisms, utilizing models like TRIGRS [16], SHALSTAB [17], SINMAP [18], and FSLAM [19]. Despite their precision, deterministic approaches are constrained by their requirement for detailed geotechnical or monitoring data and their applicability to largely homogenous slopes and simple landslide scenarios [4,[20][21][22].…”

Section: Introductionmentioning

confidence: 99%

Landslide Susceptibility Assessment and Future Prediction with Land Use Change and Urbanization towards Sustainable Development: The Case of the Li River Valley in Yongding, China

Yang,

Wang,

et al. 2024

Sustainability

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The land use change (LUC) and urbanization caused by human activities have markedly increased the occurrence of landslides, presenting significant challenges in accurately predicting landslide susceptibility despite decades of model advancements. This study, focusing on the Li River Valley (LRV) within the Yongding District, China, employs two common models, namely an analytic hierarchy process–comprehensive index (AHP-CI) model and a logistic regression (LR) model to assess landslide susceptibility (LS). The AHP-CI model is empirically based, with the advantage of being constructible and applicable at various scales without a dataset, though it remains highly subjective. The LR model is a statistical model that requires a training set. The two models represent heuristic and statistical approaches, respectively, to assessing LS. Meanwhile, the basic geological and environmental conditions are considered in the AHP-CI model, while the LR model accounts for the conditions of LUC and urbanization. The results of the multicollinearity diagnostics reflect the rationality of the predisposing factor selection (1.131 < VIF < 4.441). The findings reveal that the AHP-CI model underperforms in LUC and urbanization conditions (AUROC = 0.645, 0.628, and 0.667 for different validation datasets). However, when all the time-varying human activity predisposing factors are considered, the LR model (AUROC = 0.852) performs significantly better under the conditions of solely considering 2010 (AUROC = 0.744) and 2020 (AUROC = 0.810). The CA–Markov model was employed to project the future land use for the short-term (2025), mid-term (2030), and long-term (2040) planning periods. Based on these projections, maps of future LS were created. Importantly, this paper discussed the relationships between landslide management and regional sustainable development under the framework of the UN SDGs, which are relevant to Goal 1, Goal 11, Goal 13, and Goal 15. Finally, this study highlights the importance of integrating strategic land planning, reforestation efforts, and a thorough assessment of human impact predisposing factors with SDG-aligned LS predictions, advocating for a comprehensive, multi-stakeholder strategy to promote sustainable landslide mitigation.

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GIS-Based Deterministic and Statistical Modelling of Rainfall-Induced Landslides: A Comparative Study

Bartelletti

Arnedo

Barsanti

et al. 2017

Advancing Culture of Living With Landslides

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In this work, the role of different landslide inventories in susceptibility assessment was evaluated using a non linear regression technique, namely the generalized additive model (GAM).The investigation was carried out in three study areas: the Versa catchment (Oltrepò Pavese, Southern Lombardy, Italy), the Vernazza catchment (Cinque Terre, Eastern Liguria, Italy) and the Pogliaschina catchment (Vara Valley, Eastern Liguria, Italy). Two landslide inventories related to the 2009 and 2013 rainfall events were taken into account in the Versa catchment, whereas two landslide inventories (referred to the same 2011 rainfall event) which differ for methods of detection and criteria adopted for the landslide mapping were considered in the Vernazza and Pogliaschina catchments. The predictive performance of GAM for each landslide inventory was evaluated. The results related to different inventories were compared. The results show that the predictive capability of the model and the landslide susceptibility are significantly influenced by the type of landslide inventory. Thus, the work highlights that a standard criterion for preparing inventories should be adopted in order to produce landslide susceptibility assessment as reliable as possible

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Predictive Capability of Deterministic and Statistical Models in Weathered Granite Soil Watershed

Cited by 3 publications

References 9 publications

Survey of spatial and temporal landslide prediction methods and techniques

Survey of spatial and temporal landslide prediction methods and techniques

Landslide Susceptibility Assessment and Future Prediction with Land Use Change and Urbanization towards Sustainable Development: The Case of the Li River Valley in Yongding, China

GIS-Based Deterministic and Statistical Modelling of Rainfall-Induced Landslides: A Comparative Study

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