The influence of land use and land cover change on landslide susceptibility: A case study in Zhushan Town, Xuanen County (Hubei, China)

Chen, Lixia; Guo, Zizheng; Yin, Kunlong; Shrestha, Dhruba Pikha; Jin, Sanli

doi:10.5194/nhess-2019-203

Cited by 8 publications

(7 citation statements)

References 62 publications

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“…A number of studies have actually started looking in this direction, with interesting examples on soil moisture (Gorsevski et al, 2006), land use/land cover (Meusburger and Alewell, 2009;Reichenbach et al, 2014;Chen et al, 2019b;Shu et al, 2019), and climatic variables (Hua et al, 2020;Scheidl et al, 2020;Fan et al, 2021). Samia et al (2017) proposed that an appropriate susceptibility assessment for an area in Collazzone (Italy) may require the information of previous landslide occurrences as a predisposing factor.…”

Section: Introductionmentioning

confidence: 99%

Space-time landslide susceptibility modelling in Taiwan

Fang¹,

Wang²,

Westen³

et al. 2022

Preprint

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Portraying spatiotemporal variations in landslide susceptibility patterns is crucial for landslide prevention and management. In this study, we implement a space-time modeling approach to predict the landslide susceptibility on a yearly basis across the main island of Taiwan, from 2004 to 2018. We use a Bayesian version of a binomial generalized additive model, which assumes that landslide occurrences follow a Bernoulli distribution. We generate 46,074 slope units to partition the island of Taiwan and divided the time domain into 14 annual units. The binary landslide label assigned to each slope unit and their temporal replicates come from an available landslide database, that contains an inventory for every year. We only consider new landslides or reactivations of previous mass movements in the yearly inventories. This information and its absence counterpart are regressed against a set of static and dynamic covariates. Our modeling strategy features an initial explanatory model to test the goodness-of-fit and interpret the effect of covariates. Then, five cross-validation (CV) schemes are tested to provide a full spectrum of the predictive capacity of our model. Specifically, we implement a fully randomized 10-fold CV, a spatially constrained CV, two temporal CV (a leave one year out and a sequential temporal aggregation), together with a spatio-temporal CV. We summarize the performance in each of these tests, through their pure numerical expression as well as their residual representation in space and time. Overall, our space-time model produces excellent and interpretable results. We consider this type of dynamic prediction the new direction to take to finally move away from the static view provided by traditional susceptibility models. And, we consider such analyses just a stepping stone for further improvements, the most natural of which would lead to statistical simulations for future scenarios.

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Section: Introductionmentioning

confidence: 99%

Space-time landslide susceptibility modelling in Taiwan

Fang¹,

Wang²,

Westen³

et al. 2022

Preprint

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“…The occurrence of landslides mainly occurred at the erodibility value reaching an index value of 0.4 with a high susceptibility category on land use for agriculture. The increase in the value of soil vulnerability is mostly due to human land use activities for agriculture and settlement [21].…”

Section: Discussionmentioning

confidence: 99%

Soil erodibility mapping for soil susceptibility in the upstream of Kelara Subwatershed in Jeneponto Regency

Ahmad

Farida

Juita

et al. 2022

IOP Conf. Ser.: Earth Environ. Sci.

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Landslides and flash floods in Rumbia Village, Rumbia District, Jeneponto Regency on June 11-12, 2020, have caused material and non-material losses to the local population. The incident occurred very quickly with an area with a disaster impact on seven sub-districts and 18 villages. This study aims to map soil erodibility to assess soil susceptibility to landslides in the Upper Kelara Sub-watershed. Calculate soil erodibility using the Wischmeier and Smith method, texture with hydrometer method, c-organic with Walkley and Black, mapping of soil erodibility with the kriging approach, and expert judgment for soil susceptibility category. The results showed that c-organic value content (1.19 to 2.47%) has low in landslides areas, with soil permeability ranging from 0.23 to 1.16 cm/hour and soil texture dominated silty clay. Soil erodibility in the landslides area reaches a value of 0.4 with the high category of soil susceptibility. Soil erodibility is in line with soil susceptibility value, the high erodibility value, the high soil susceptibility category. Soil erodibility mapping showed a distribution of erodibility index increase in the bottom part of the Kelara Subwatershed. The Mitigation actions through government assistance and socialization of disaster-aware communities need to be carried out immediately so that incidents can be minimized and prevented in the future.

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“…ey established a ground settlement prediction model, which overcame the disadvantages of slow convergence and easy falling into local minima of the BP neural network model. Chen et al established a residual settlement prediction model for the old mining area by using the existing partial observation data after the maximum subsidence velocity of the observatory and the BP algorithm of L-M [21].…”

Section: Related Workmentioning

confidence: 99%

An Improved Gray Neural Network Method to Optimize Spatial and Temporal Characteristics Analysis of Land-Use Change

Yang

Wang

Jia-jun

et al. 2022

Computational Intelligence and Neuroscience

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In this article, the principles of the gray model and BP neural network model are analyzed, and the characteristics of land-use change and spatial and temporal distribution are studied in-depth, and at the same time, to explore the influence of land-use change on ESV, the relationship between the two is analyzed using gray correlation degree, and a mathematical model is constructed to maximize the benefits of the regional system, coupling economic and ecological benefits, combined with Geo SOS-FLUS model to achieve the optimization of land use. This article constructs a combined prediction model of a gray neural network. The gray differential equation parameters correspond to the weights and thresholds of the neural network, and the optimized parameters are determined by training the neural network to make it stable. Then the training results of the BP neural network are fitted with the results obtained from the gray GM (1.1) model. Finally, the prediction results of the three models, gray GM (1.1), BP God Meridian, and gray neural network model, are compared and analyzed. The global spatial autocorrelation and local spatial aggregation patterns of regional soil erosion and its erosion factors are analyzed using the Exploratory Spatial Data Analysis (ESDA) method in spatial measurement theory.

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The influence of land use and land cover change on landslide susceptibility: A case study in Zhushan Town, Xuanen County (Hubei, China)

Cited by 8 publications

References 62 publications

Space-time landslide susceptibility modelling in Taiwan

Space-time landslide susceptibility modelling in Taiwan

Soil erodibility mapping for soil susceptibility in the upstream of Kelara Subwatershed in Jeneponto Regency

An Improved Gray Neural Network Method to Optimize Spatial and Temporal Characteristics Analysis of Land-Use Change

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