Uncertainties Analysis of Collapse Susceptibility Prediction Based on Remote Sensing and GIS: Influences of Different Data-Based Models and Connections between Collapses and Environmental Factors

Li, Wenbin; Fan, Xuanmei; Huang, Faming; Chen, Wei; Hong, Haoyuan; Huang, Jingsong; Guo, Zhengfu

doi:10.3390/rs12244134

Cited by 39 publications

(11 citation statements)

References 86 publications

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“…The RF algorithm can catch such nonlinear features among the factors but prevents overfitting [ 8 , 17 , 37 ]. Hence, the reliability of landslide susceptibility prediction mainly depends on the modeling approaches and the selection and processing of the available hazard-causative factors [ 39 , 40 , 41 ].…”

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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“…6 ). The GWR model has a low deviation, which means that the independent variables can estimate effectively and closely to the average value [ 93 ]. Table 8 also shows that the IDI, UI and PD contribute positively to the confirmed COVID-19 cases.…”

Section: Resultsmentioning

confidence: 99%

Spatial differentiation and determinants of COVID-19 in Indonesia

2022

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Background The spread of the coronavirus disease 2019 (COVID-19) has increasingly agonized daily lives worldwide. As an archipelagic country, Indonesia has various physical and social environments, which implies that each region has a different response to the pandemic. This study aims to analyze the spatial differentiation of COVID-19 in Indonesia and its interactions with socioenvironmental factors. Methods The socioenvironmental factors include seven variables, namely, the internet development index, literacy index, average temperature, urban index, poverty rate, population density (PD) and commuter worker (CW) rate. The multiple linear regression (MLR) and geographically weighted regression (GWR) models are used to analyze the impact of the socioenvironmental factors on COVID-19 cases. COVID-19 data is obtained from the Indonesian Ministry of Health until November 30th 2020. Results Results show that the COVID-19 cases in Indonesia are concentrated in Java, which is a densely populated area with high urbanization and industrialization. The other provinces with numerous confirmed COVID-19 cases include South Sulawesi, Bali, and North Sumatra. This study shows that the socioenvironmental factors, simultaneously, influence the increasing of confirmed COVID-19 cases in the 34 provinces of Indonesia. Spatial interactions between the variables in the GWR model are relatively better than those between the variables in the MLR model. The highest spatial tendency is observed outside Java, such as in East Nusa Tenggara, West Nusa Tenggara, and Bali. Conclusion Priority for mitigation and outbreak management should be high in areas with high PD, urbanized spaces, and CW.

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“…The receiver operating characteristic (ROC) curve can be used to quantitatively analyze the accuracy of the assessment model [ 51 , 52 ]. The area under the curve (AUC) value is between 0 and 1.…”

Section: Results and Analysismentioning

confidence: 99%

Refined Zoning of Landslide Susceptibility: A Case Study in Enshi County, Hubei, China

Wang

Qiu

et al. 2022

IJERPH

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At present, landslide susceptibility assessment (LSA) based on the characteristics of landslides in different areas is an effective prevention measure for landslide management. In Enshi County, China, the landslides are mainly triggered by high-intensity rainfall, which causes a large number of casualties and economic losses every year. In order to effectively control the landslide occurrence in Enshi County and mitigate the damages caused by the landslide. In this study, eight indicators were selected as assessment indicators for LSA in Enshi County. The analytic hierarchy process (AHP) model, information value (IV) model and analytic hierarchy process-information value (AHP-IV) model were, respectively, applied to assess the landslide distribution of landslides in the rainy season (RS) and non-rainy season (NRS). Based on the three models, the study area was classified into five levels of landslide susceptibility, including very high susceptibility, high susceptibility, medium susceptibility, low susceptibility, and very low susceptibility. The receiver operating characteristic (ROC) curve was applied to verify the model accuracy. The results showed that the AHP-IV model (ROC = 0.7716) was more suitable in RS, and the IV model (ROC = 0.8237) was the most appropriate model in NRS. Finally, combined with the results of landslide susceptibility in RS and NRS, an integrated landslide susceptibility map was proposed, involving year-round high susceptibility, RS high susceptibility, NRS high susceptibility and year-round low susceptibility. The integrated landslide susceptibility results provide a more detailed division in terms of the different time periods in a year, which is beneficial for the government to efficiently allocate landslide management funds and propose effective landslide management strategies. Additionally, the focused arrangement of monitoring works in landslide-prone areas enable collect landslide information efficiently, which is helpful for the subsequent landslide preventive management.

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Uncertainties Analysis of Collapse Susceptibility Prediction Based on Remote Sensing and GIS: Influences of Different Data-Based Models and Connections between Collapses and Environmental Factors

Cited by 39 publications

References 86 publications

Zonation of Landslide Susceptibility in Ruijin, Jiangxi, China

Zonation of Landslide Susceptibility in Ruijin, Jiangxi, China

Spatial differentiation and determinants of COVID-19 in Indonesia

Refined Zoning of Landslide Susceptibility: A Case Study in Enshi County, Hubei, China

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