Comparative analysis of statistical methods for landslide susceptibility mapping in the Bostanlik District, Uzbekistan

Juliev, Mukhiddin; Mergili, Martin; Mondal, Ismail; Nurtaev, Bakhtiar; Pulatov, Alim; Hübl, Johannes

doi:10.1016/j.scitotenv.2018.10.431

Cited by 130 publications

(60 citation statements)

References 52 publications

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“…Moreover, numerous methods exist for landslide susceptibility mapping. They range from qualitative models (inventory-based and knowledge-driven methods) to quantitative models (data-driven methods and deterministic methods) (Juliev et al, 2018;Zêzere et al, 2017). The qualitative methods are mainly based on expert's opinion, and they include magnitude frequency, active mapping, Boolean logic, and fuzzy logic (Abella & Van Westen, 2007).…”

Section: Introductionmentioning

confidence: 99%

“…The literature on landslide studies confirms that different models used to predict landslide susceptibility have been widely compared globally (Chen et al, 2018a;Juliev et al, 2018). Qualitative models and data-driven models-namely, statistical methodswere explored and compared to study landslide susceptibility for different regional scales (Dou et al, 2018;Van Den Eeckhaut, Marre, & Poesen, 2010).…”

Section: Introductionmentioning

confidence: 99%

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Comparative Analysis of Deterministic and Semiquantitative Approaches for Shallow Landslide Risk Modeling in Rwanda

et al. 2019

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The use of appropriate approaches to produce risk maps is critical in landslide disaster management. The aim of this study was to investigate and compare the stability index mapping (SINMAP) and the spatial multicriteria evaluation (SMCE) models for landslide risk modeling in Rwanda. The SINMAP used the digital elevation model in conjunction with physical soil parameters to determine the factor of safety. The SMCE method used six layers of landslide conditioning factors. In total, 155 past landslide locations were used for training and model validation. The results showed that the SMCE performed better than the SINMAP model. Thus, the receiver operating characteristic and three statistical estimators—accuracy, precision, and the root mean square error (RMSE)—were used to validate and compare the predictive capabilities of the two models. Therefore, the area under the curve (AUC) values were 0.883 and 0.798, respectively, for the SMCE and SINMAP. In addition, the SMCE model produced the highest accuracy and precision values of 0.770 and 0.734, respectively. For the RMSE values, the SMCE produced better prediction than SINMAP (0.332 and 0.398, respectively). The overall comparison of results confirmed that both SINMAP and SMCE models are promising approaches for landslide risk prediction in central‐east Africa.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Deterministic and Semiquantitative Approaches for Shallow Landslide Risk Modeling in Rwanda

et al. 2019

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“…Among the abovementioned indices, elevation, topography, and slope are closely related to the DEM data [24]. In the majority of the previous studies, it has been suggested that DEM data should be consistent with the study date [25][26][27][28][29]. However, because of landscape changes and reshaping processes (e.g., earthquakes and engineering construction), the DEM data usually show obvious temporal variation.…”

Section: Introductionmentioning

confidence: 96%

Exploring the Impact of Multitemporal DEM Data on the Susceptibility Mapping of Landslides

Wang

Han

et al. 2020

Applied Sciences

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Digital elevation models (DEMs) are fundamental data models used for susceptibility assessment of landslides. Due to landscape change and reshaping processes, a DEM can show obvious temporal variation and has a significant influence on assessment results. To explore the impact of DEM temporal variation on hazard susceptibility, the southern area of Sichuan province in China is selected as a study area. Multitemporal DEM data spanning over 17 years are collected and the topographic variation of the landscape in this area is investigated. Multitemporal susceptibility maps of landslides are subsequently generated using the widely accepted logistic regression model (LRM). A positive correlation between the topographic variation and landslide susceptibility that was supported by previous studies is quantitatively verified. The ratio of the number of landslides to the susceptibility level areas (RNA) in which the hazards occur is introduced. The RNA demonstrates a general decrease in the susceptibility level from 2000 to 2009, while the ratio of the decreased level is more than fifteen times greater than that of the ratio of the increased level. The impact of the multitemporal DEM on susceptibility mapping is demonstrated to be significant. As such, susceptibility assessments should use DEM data at the time of study.

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“…SWARA-ANFIS-PSO appeared to be six times faster with respect to the processing time attained by SWARA-ANFIS-SFLA . Mukhiddin Juliev et al (2019) compared Statistical Index (SI), Frequency Ratio (FR) and Certainty Factor (CF) for landslide susceptibility mapping. Using assessment methods, they concluded that the statistical index method has the best performance in their study (Juliev et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

“…Mukhiddin Juliev et al (2019) compared Statistical Index (SI), Frequency Ratio (FR) and Certainty Factor (CF) for landslide susceptibility mapping. Using assessment methods, they concluded that the statistical index method has the best performance in their study (Juliev et al, 2019). Nsengiyumva et al (2019) used weights of evidence, logistic regression, frequency ratio and statistical index to produce susceptibility maps for Rwanda area in Africa.…”

Section: Introductionmentioning

confidence: 99%

Landslide Hazard Mapping Using a Radial Basis Function Neural Network Model: A Case Study in Semirom, Isfahan, Iran

Yavari

Pahlavani

Bigdeli

2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. In this paper, Radial Basis Function (RBF) Neural Network and Logistic Regression (LR) models were proposed for hazard prediction of landslides in a part of the Semirom area (Iran) to compare their accuracy and performance. For this purpose, a spatial database of the study area was prepared that consists of 68 landslide locations and 11 influencing information layers including slope, aspect, profile curvature, plan curvature, distance from faults, distance from roads, distance from residential regions, distance from rivers, land use, lithology and rainfall. Landslide hazard maps were prepared for the study area by applying the proposed algorithms. Performance of the models was assessed using the Receiver Operating Characteristic (ROC) curve and area under the ROC curve (AUC). The coefficient of determination (R2), the root mean square error (RMSE), and the Normal Root Mean Square Error (NRMSE) were calculated for proposed methods. The outcomes showed that the RBF Neural Network has the highest R2 (0.8224), in comparison to that of the LR model (0.5365). Also, the ROC plots, RMSEs and NRMSEs showed that the proposed RBF Neural Network is much better than the LR model. Consequently, it can be concluded that the RBF Neural Network is the best regression model in this study and it can be considered as a capable method for landslide hazard mapping in landslide-susceptible areas.

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Comparative analysis of statistical methods for landslide susceptibility mapping in the Bostanlik District, Uzbekistan

Cited by 130 publications

References 52 publications

Comparative Analysis of Deterministic and Semiquantitative Approaches for Shallow Landslide Risk Modeling in Rwanda

Comparative Analysis of Deterministic and Semiquantitative Approaches for Shallow Landslide Risk Modeling in Rwanda

Exploring the Impact of Multitemporal DEM Data on the Susceptibility Mapping of Landslides

Landslide Hazard Mapping Using a Radial Basis Function Neural Network Model: A Case Study in Semirom, Isfahan, Iran

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