Exploring Influence of Sampling Strategies on Event-Based Landslide Susceptibility Modeling

Lai, Jhe Syuan; Chiang, Shou Hao; Tsai, Fuan

doi:10.3390/ijgi8090397

Cited by 19 publications

(17 citation statements)

References 93 publications

(127 reference statements)

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“…This makes hazard prediction complicated and requires consideration of as many of the hazard-causative factors as possible while dealing with susceptibility assessment. Recently, remote sensing (RS) and geographic information system (GIS) have been taking an active part in the study of disaster risk zoning [ 22 , 23 , 24 , 25 , 26 ]. RS techniques can not only provide multitemporal and time-series spatial information of large and even inaccessible areas over a span of decades but also timely pre- or post-hazard spatial data [ 8 , 27 , 28 ].…”

Section: Introductionmentioning

confidence: 99%

Zonation of Landslide Susceptibility in Ruijin, Jiangxi, China

Zhou

Lin

et al. 2021

IJERPH

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Landslides are one of the major geohazards threatening human society. The objective of this study was to conduct a landslide hazard susceptibility assessment for Ruijin, Jiangxi, China, and to provide technical support to the local government for implementing disaster reduction and prevention measures. Machine learning approaches, e.g., random forests (RFs) and support vector machines (SVMs) were employed and multiple geo-environmental factors such as land cover, NDVI, landform, rainfall, lithology, and proximity to faults, roads, and rivers, etc., were utilized to achieve our purposes. For categorical factors, three processing approaches were proposed: simple numerical labeling (SNL), weight assignment (WA)-based and frequency ratio (FR)-based. Then 19 geo-environmental factors were respectively converted into raster to constitute three 19-band datasets, i.e., DS1, DS2, and DS3 from three different processes. Then, 155 observed landslides that occurred in the past decades were vectorized, among which 70% were randomly selected to compose a training set (TS1) and the remaining 30% to form a validation set (VS1). A number of non-landslide (no-risk) samples distributed in the whole study area were identified in low slope (<1–3°) zones such as urban areas and croplands, and also added to the TS1 and VS1 in the same ratio. For comparison, we used the FR approach to identify the no-risk samples in both flat and non-flat areas, and merged them into the field-observed landslides to constitute another pair of training and validation sets (TS2 and VS2) using the same ratio of 7:3. The RF algorithm was applied to model the probability of the landslide occurrence using DS1, DS2, and DS3 as predictive variables and TS1 and TS2 for training to obtain the SNL-based, WA-based, and FR-based RF models, respectively. Verified against VS1 and VS2, the three models have similar overall accuracy (OA) and Kappa coefficient (KC), which are 89.61%, 91.47%, and 94.54%, and 0.7926, 0.8299, and 0.8908, respectively. All of them are much better than the three models obtained by SVM algorithm with OA of 81.79%, 82.86%, and 83%, and KC of 0.6337, 0.655, and 0.660. New case verification with the recent 26 landslide events of 2017–2020 revealed that the landslide susceptibility map from WA-based RF modeling was able to properly identify the high and very high susceptibility zones where 23 new landslides had occurred, and performed better than the SNL-based and FR-based RF modeling, though the latter has a slightly higher OA and KC. Hence, we concluded that all three RF models achieve reasonable risk prediction, but WA-based and FR-based RF modeling deserves a recommendation for application elsewhere. The results of this study may serve as reference for the local authorities in prevention and early warning of landslide hazards.

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Section: Introductionmentioning

confidence: 99%

Zonation of Landslide Susceptibility in Ruijin, Jiangxi, China

Zhou

Lin

et al. 2021

IJERPH

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“…The effect of these different landslide mapping choices on susceptibility models has remained largely unexplored. This choice may be important to aid decisions of whether we should consider the scar, body, toe, if not the entire landslide-affected, areas to contribute most to robust susceptibility models (Lai et al 2019). Assuming that we have only limited information about the extent of each landslide, which parts of a landslide then can and should we include in susceptibility models?…”

Section: Introductionmentioning

confidence: 99%

How robust are landslide susceptibility estimates?

et al. 2020

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Much of contemporary landslide research is concerned with predicting and mapping susceptibility to slope failure. Many studies rely on generalised linear models with environmental predictors that are trained with data collected from within and outside of the margins of mapped landslides. Whether and how the performance of these models depends on sample size, location, or time remains largely untested. We address this question by exploring the sensitivity of a multivariate logistic regression—one of the most widely used susceptibility models—to data sampled from different portions of landslides in two independent inventories (i.e. a historic and a multi-temporal) covering parts of the eastern rim of the Fergana Basin, Kyrgyzstan. We find that considering only areas on lower parts of landslides, and hence most likely their deposits, can improve the model performance by >10% over the reference case that uses the entire landslide areas, especially for landslides of intermediate size. Hence, using landslide toe areas may suffice for this particular model and come in useful where landslide scars are vague or hidden in this part of Central Asia. The model performance marginally varied after progressively updating and adding more landslides data through time. We conclude that landslide susceptibility estimates for the study area remain largely insensitive to changes in data over about a decade. Spatial or temporal stratified sampling contributes only minor variations to model performance. Our findings call for more extensive testing of the concept of dynamic susceptibility and its interpretation in data-driven models, especially within the broader framework of landslide risk assessment under environmental and land-use change.

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“…We also compared these landslide susceptibility maps, in order to analyze the correlations in the spatial distribution of susceptibility index between the multi-year PM ensemble model and other models. Rather than performing the mutual subtraction algorithm [6,[58][59][60] or the histogram matching method [55,56], we calculated the Spearman's rank correlation coefficient to assess the degree of difference between the susceptibility maps of the optimal model and other models. As shown in Table 13, the correlation coefficient of the single models ranged from 0.811 to 0.946, with an average of 0.912, which was lower than the 0.940-1.00 correlation coefficient range of the ensemble models.…”

Section: Correlations Between the Susceptibility Maps Of The Optimal ...mentioning

confidence: 99%

Performance Assessment of Event-Based Ensemble Landslide Susceptibility Models in Shihmen Watershed, Taiwan

Lin

2022

Water

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While multi-year and event-based landslide inventories are both commonly used in landslide susceptibility analysis, most areas lack multi-year landslide inventories, and the analysis results obtained from the use of event-based landslide inventories are very sensitive to the choice of event. Based on 24 event-based landslide inventories for the Shihmen watershed from 1996 to 2015, this study established five event-based single landslide susceptibility models employing logistic regression, random forest, support vector machine, kernel logistic regression, and gradient boosting decision tree methods. The ensemble methods, involving calculating the mean of the susceptibility indexes (PM), median of the susceptibility indexes (PME), weighted mean of the susceptibility indexes (PMW), and committee average of binary susceptibility values (CA) of the five single models were then used to establish four event-based ensemble landslide susceptibility models. After establishing nine landslide susceptibility models, using each inventory from the 24 event-based landslide inventories or a multi-year landslide inventory, we identified the differences in landslide susceptibility maps attributable to the different landslide inventories and modeling methods, and used the area under the receiver operating characteristic curve to assess the accuracy of the models. The results indicated that an ensemble model based on a multi-year inventory can obtain excellent predictive accuracy. The predictive accuracy of multi-year landslide susceptibility models is found to be superior to that of event-based models. In addition, the higher predictive accuracy of ensemble landslide susceptibility models than that of single models implied that these ensemble methods were robust for enhancing the model’s predictive performance in the study area. When employing event-based landslide inventories in modeling, PM ensemble models offer the best predictive ability, according to the Kruskal–Wallis test results. Areas with a high mean susceptibility index and low standard deviation, identified using the 24 PM ensemble models based on different event-based landslide inventories, constitute places where landslide mitigation measures should be prioritized.

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Exploring Influence of Sampling Strategies on Event-Based Landslide Susceptibility Modeling

Cited by 19 publications

References 93 publications

Zonation of Landslide Susceptibility in Ruijin, Jiangxi, China

Zonation of Landslide Susceptibility in Ruijin, Jiangxi, China

How robust are landslide susceptibility estimates?

Performance Assessment of Event-Based Ensemble Landslide Susceptibility Models in Shihmen Watershed, Taiwan

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