Numerical Simulation of Na-Tech Cascading Disasters in a Large Oil Depot

Zhang, Shaobiao; Xu, Dong; Shen, Gansu; Liu, Junguo; Yang, Lili

doi:10.3390/ijerph17228620

Cited by 3 publications

(2 citation statements)

References 27 publications

(37 reference statements)

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“…Rainfall-induced landslides are a typical cascading geo-hazard and are attracting the increasing attention of many researchers [5,6]. Rainfall-induced landslides commonly occur after heavy rainfall and may develop into potentially catastrophic movements [7,8].…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Landslide Susceptibility Mapping Incorporating the Effects of Heavy Rainfall: A Case Study of the Heavy Rainfall in August 2021 in Kitakyushu, Fukuoka, Japan

Wang

Han

et al. 2021

Water

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Landslide represents an increasing menace causing huge casualties and economic losses, and rainfall is a predominant factor inducing landslides. Landslide susceptibility assessment (LSA) is a commonly used and effective method to prevent landslide risk, however, the LSA does not analyze the impact of the rainfall on landslides which is significant and non-negligible. Therefore, the spatiotemporal LSA considering the inducing effect of rainfall is proposed to improve accuracy and applicability. In this study, the influencing factors are selected using the chi-square test, out-of-bag error and multicollinearity test. The spatial LSA are thus obtained using the random forest (RF) model, deep belief networks model and support vector machine, and compared using receiver operating characteristic curve and seed cell area index to determine the optimal assessment result. According to the heavy rainfall characteristics in the study area, the rainfall period is divided into four stages, and the effective rainfall model is employed to generate the rainfall impact (RI) maps of the four stages. The spatiotemporal LSAs are obtained by coupling the optimal spatial LSA and various RI maps and verified using the landslide warning map. The results demonstrate that the optimal spatiotemporal LSA is obtained using the spatial LSA of the RF model and temporal LSA of the rainfall data in the peak stage. It can predict the area where rainfall-induced landslides are likely to occur and prevent landslide risk.

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Section: Introductionmentioning

confidence: 99%

Spatiotemporal Landslide Susceptibility Mapping Incorporating the Effects of Heavy Rainfall: A Case Study of the Heavy Rainfall in August 2021 in Kitakyushu, Fukuoka, Japan

Wang

Han

et al. 2021

Water

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“…Zhang et al [ 11 ] developed a full-scale, three-dimensional model of a large oil depot site in Shenzhen, China. They used a combination of numerical methods to simulate the progression, starting with heavy rainfall, and then followed by landslide, pipeline breakage, oil leak, and explosion.…”

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confidence: 99%