A Brief Report of Pingdi Landslide (23 July 2019) in Guizhou Province, China

Yan, Tao; Shen, Shui‐Long; Zhou, Annan; Chen, Jun

doi:10.3390/geosciences9090368

Cited by 10 publications

(4 citation statements)

References 32 publications

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“…The advancement of satellite remote sensing (RS) and geographic information systems (GIS) technologies have meaningfully transformed the hazard mitigation planning perception for any future events [21]. The extensive application of RS and GIS techniques has emerged as a significant contributor for evaluating natural hazards [22][23][24][25]. RS and GIS have been used in many analyses for analyzing landslide susceptibility [11,[26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Predicting Landslides Susceptible Zones in the Lesser Himalayas by Ensemble of Per Pixel and Object-Based Models

et al. 2022

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Landslide susceptibility is a contemporary method for delineation of landslide hazard zones and holistically mitigating the future landslides risks for planning and decision-making. The significance of this study is that it would be the first instance when the ‘geon’ model will be attempted to delineate landslide susceptibility map (LSM) for the complex lesser Himalayan topography as a contemporary LSM technique. This study adopted the per-pixel-based ensemble approaches through modified frequency ratio (MFR) and fuzzy analytical hierarchy process (FAHP) and compared it with the ‘geons’ (object-based) aggregation method to produce an LSM for the lesser Himalayan Kalsi-Chakrata road corridor. For the landslide susceptibility models, 14 landslide conditioning factors were carefully chosen; namely, slope, slope aspect, elevation, lithology, rainfall, seismicity, normalized differential vegetation index, stream power index, land use/land cover, soil, topographical wetness index, and proximity to drainage, road, and fault. The inventory data for the past landslides were derived from preceding satellite images, intensive field surveys, and validation surveys. These inventory data were divided into training and test datasets following the commonly accepted 70:30 ratio. The GIS-based statistical techniques were adopted to establish the correlation between landslide training sites and conditioning factors. To determine the accuracy of the model output, the LSMs accuracy was validated through statistical methods of receiver operating characteristics (ROC) and relative landslide density index (R-index). The accuracy results indicate that the object-based geon methods produced higher accuracy (geon FAHP: 0.934; geon MFR: 0.910) over the per-pixel approaches (FAHP: 0.887; MFR: 0.841). The results noticeably showed that the geon method constructs significant regional units for future mitigation strategies and development. The present study may significantly benefit the decision-makers and regional planners in selecting the appropriate risk mitigation procedures at a local scale to counter the potential damages and losses from landslides in the area.

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Section: Introductionmentioning

confidence: 99%

Predicting Landslides Susceptible Zones in the Lesser Himalayas by Ensemble of Per Pixel and Object-Based Models

et al. 2022

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“…A landslide is a kind of global natural disaster that is mainly caused by rainfall, earthquake, and human activities that can lead to huge damage to infrastructure and the economy even the loss of life [1]. For example, the Shuicheng landslide that happened in China in July 2019 caused 42 deaths and 9 missing people [2]. Likewise, the Kavalappara landslide that happened in India in 2019 caused 46 deaths and 11 missings [3].…”

Section: Introductionmentioning

confidence: 99%

“…However, the x(1) 1 (2) in Equation ( 20) is an unknown data, in order to get the x(1) 1 (3), the x(1) 1 (2) needs to be replaced by Equation (19).…”

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confidence: 99%

Research on the Landslide Prediction Based on the Dual Mutual-Inductance Deep Displacement 3D Measuring Sensor

Shentu

Yang

et al. 2022

Applied Sciences

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Landslides are frequent and catastrophic geological hazards, and forecasting their movement is an important aspect of risk assessment and engineering prevention. Based on the integrated deep displacement three-dimensional measuring sensor with sensing unit array structure, an improved multivariable grey model based on dynamic background value and multivariable feedback is proposed to build predictive models for the evolutionary condition of landslides. In the modeling process, the traditional grey model was replaced by extracting the trend information of each variable, instead of summing up each independent variable after assigning weights to it, besides, the Whale Optimization Algorithm (WOA) is used to modify the default value in the model’s background variables. By predicting more than 1000 sets of deep displacement monitoring data collected in the landslide simulation test conducted at the landslide simulation test device, the displacement prediction accuracy of our purposed model is 26%, 47%, and 87% respectively higher than the optimizing grey model (OGM) for three sensing units at different depths. Moreover, a new landslide risk assessment approach based on the orientation vector angle is proposed to make stability discriminations which is less susceptible to volatile data than the TOPSIS-Entropy weight theory and avoids the problem of lack of uniform standards due to the complexity of environmental factors.

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“…Urbanization leads to a continuous increase in demand for urban infrastructures, including bridges, highways, water supply and sewage systems, electric and electronic pipelines and metro systems [1][2][3][4][5][6][7][8]. However, the service life of these infrastructural facilities often falls short of the designed life due to natural phenomena (e.g., earthquakes, floods, typhoons, ground subsidence) and lack of proper expertise and sustainable concept of the management layer [9][10][11][12][13][14][15][16]. Some facilities collapsed during the construction period, while others failed during their service life [17][18][19][20][21][22][23][24][25], which had harmful impacts on the social community and environment [26,27].…”

Section: Introductionmentioning

confidence: 99%

Lessons Learnt from Bridge Collapse: A View of Sustainable Management

et al. 2020

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Bridge failure is one of the worst infrastructural disasters. This paper investigates the risk of bridge infrastructures in the view of sustainable management. Statistics on bridge failures from 2009 to 2019 in China show that most of these failures are related to anthropic factors. The collapse of the Zijin Bridge on 14 June 2019 in Heyuan City of Guangdong Province, China is used as a case to perform detailed analysis. Superficially, bridge collapse is a technical problem rather than a management problem. However, the deep reason for this kind of bridge failure may be due to the lack of sustainable management. In order to verify this point of view, both fault tree analysis (FTA) and strategic environmental assessment (SEA) for the bridge failure and later impact on society are conducted. According to the FTA results, the failure of the arch foot is the direct trigger of the Zijin Bridge collapse. Since a lack of real-time monitoring, risk assessment and other management issues are potential factors causing bridge collapse, strategic environmental assessment (SEA) is used to investigate the management issues related to the economy, culture, human health and environmental sustainability in more depth. The low total SEA result shows poor project management and a high safety risk. Finally, the specific managerial measures are proposed to improve the sustainability of infrastructures.

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A Brief Report of Pingdi Landslide (23 July 2019) in Guizhou Province, China

Cited by 10 publications

References 32 publications

Predicting Landslides Susceptible Zones in the Lesser Himalayas by Ensemble of Per Pixel and Object-Based Models

Predicting Landslides Susceptible Zones in the Lesser Himalayas by Ensemble of Per Pixel and Object-Based Models

Research on the Landslide Prediction Based on the Dual Mutual-Inductance Deep Displacement 3D Measuring Sensor

Lessons Learnt from Bridge Collapse: A View of Sustainable Management

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