Landslide susceptibility assessment of national highway 1D from Sonamarg to Kargil, Jammu and Kashmir, India using frequency ratio method

Nanda, Aadil Manzoor; Hassan, Zahoor Ul; Ahmed, Pervez; Kanth, T. A.

doi:10.1007/s10708-020-10235-y

Cited by 18 publications

(7 citation statements)

References 29 publications

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“…In order to overcome the problem of handwritten digit recognition in bank check, Nanda et al applied back propagation in neural network and further constructed LeNet-5 [13]. It includes the input layer and other basic structural layer models [14].…”

Section: Literature Reviewmentioning

confidence: 99%

Slope Shape and Edge Intelligent Recognition Technology Based on Deep Neural Sensing Network

Huang

Yu²,

Wu³

et al. 2022

Journal of Sensors

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In order to solve the problem that the slope surface diseases cannot be accurately identified, which cannot be repaired in time and cause serious slope disasters, a slope intelligent recognition technology based on deep neural network is proposed. Based on convolutional neural network (CNN) theory, the technology adopts the transfer learning method to solve the overfitting problem of slope surface samples, which is difficult to obtain a large number of marked samples, and verifies the proposed model by experiment. The results are as follows: the recognition results of various slope surface diseases by ResNet-18 network are higher than AlexNet and VGG-16, with an average accuracy of 84.1%, and the recognition effect of cracks is the best. Under the same migration strategy, the detection accuracy of ResNet-18 is 96.3%, which is much higher than the other two, and the detection time is reduced by 15% on average. It is proved that the ResNet-18 model proposed can identify slope changes very effectively, so that workers can be timely dispatched for maintenance, reducing the possibility of disaster, which has great significance.

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Section: Literature Reviewmentioning

confidence: 99%

Slope Shape and Edge Intelligent Recognition Technology Based on Deep Neural Sensing Network

Huang

Yu²,

Wu³

et al. 2022

Journal of Sensors

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“…The decrease of the shear strength of the soil slope is also an important reason for the occurrence of landslides (Guo et al, 2019;Yang et al, 2019;Fu et al, 2020). The MNDWI and the terrain wetness index reflect the characterization of the surface moisture distribution in the study area, which can effectively indicate the influence of hydrological environmental factors on landslide susceptibility (Li Z.-L. et al, 2021). The FR values of MNDWI in nearly 90% of the units are positively correlated with the MNDWI, indicating that the water body has a great influence on the occurrence of landslides.…”

Section: Hydrological Environment and Land Cover Factorsmentioning

confidence: 99%

“…It is conducive to the occurrence of landslides, when the environmental factors are in these ranges where, the elevation is 139.7-308.5 m, the slope aspect is smaller than 202.5 °, and the slope is high At 7.0 °, the terrain wetness index is 2.5-6.6, the MNDWI is 0.39-0.62 or 0.74-0.86, the NDVI is 0.73-0.86, the NDBI is 0.16-0.27, and the lithology are Mesoproterozoic Pt2 (1), Late Paleoproterozoic (Z), Pt3, Silurian S, Triassic T, Quaternary Q. The influence degree of environmental factors on landslide susceptibility can more intuitively reflect the main environmental factors considered by different models and provide a theoretical reference for the LSP (Wu Y. et al, 2020;Chang et al, 2020;Zhao et al, 2020;Pan et al, 2021).…”

Section: Analysis Of Environmental Factorsmentioning

confidence: 99%

“…According to many studies (Chang et al, 2020;Huang et al, 2021a;Guo Z. et al, 2021), the accuracy of the LSP model without considering the nonlinear connection is slightly lower than that of the coupled model considering the connection. The connection methods include the weight of evidence (Wu R. et al, 2020), IV model (Zhao B. et al, 2021) and the frequency ratio (FR) method (Nanda et al, 2020). FR method is used to characterize the spatial relationship between landslide distribution and condition factors.…”

Section: Introductionmentioning

confidence: 99%

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Landslide Susceptibility Prediction Based on Frequency Ratio Method and C5.0 Decision Tree Model

et al. 2022

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This paper aims to propose an efficient landslide susceptibility prediction (LSP) model based on the frequency ratio method and C5.0 Decision Tree (C5.0 DT) model. Taking Ruijin City as the study area, local landslide inventory and 12 environmental factors are collected. Then the nonlinear correlations between landslide inventory and environmental factors are established by frequency ratio (FR) method. Thirdly, the FR values of these environmental factors are taken as the input variables of the C5.0 DT/SVM models; landslide samples and non-landslide samples are set as the output variables with values of 1 and 0, respectively. The mathematical relationship between input variables and output variables is established by C5.0 DT/SVM models. Finally, the performance of LSP of both models is evaluated by the Area Under Receiver Operation Characteristic Curve (AUC). Results show that: 1) The landslide susceptibility mapping (LSM) of the C5.0 DT and the SVM models are similar on the whole, high and very high susceptibility levels are mainly distributed in the north and in the edge of the study area. 2) The AUC values of C5.0 DT and SVM are 0.886 and 0.819, respectively. Both models have good LSP accuracy, however, the overall LSP accuracy of the C5.0 DT model is better than that of SVM. 3) It is significant and reliable to carry out LSP based on frequency ratio method and C5.0 DT model.

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“…Landslides are one of the most common geohazard in many parts of the world. Landslides in ict loss of human lives, properties worth billions of dollars, injuries, and fatalities, and environmental losses each year worldwide (Das et al, 2012, Wu et al, 2015, Nanda et al, 2021a. Landslide damage can be reduced with a high degree of certainty by employing all kinds of prediction models .…”

Section: Introductionmentioning

confidence: 99%

Landslide Susceptibility Zonation along NH 1D from Sonamarg to Kargil, North Western Himalaya

Nanda

Ahmed

et al. 2022

Preprint

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The National Highway 1D from Sonamarg to Kargil is known as one of the most landslide susceptible areas in Kashmir and Ladakh divisions. The aim of the present study is to delineate landslide susceptibility zones along National Highway 1D from Sonamarg to Kargil, north western (NH) Himalaya. Based on landslide influencing geo-environmental factors such as slope angle, landuse/landcover, distance to faults, precipitation, soil, slope aspect, lithology, altitude, distance to streams, and distance to road a detailed landslide susceptible map is prepared. A weighted pair wise comparison matrix is generated using Analytical Hierarchy Process (AHP). The geo-environmental factors and their derived weights through AHP were then overlaid using the index overlay module in ArcGIS 10.2 supplemented by MS Excel and MATLAB. The relationships between the conditional and trigger factors was carried out by analyzing landslide events. The results show that the area of 913.55 Km2 is covered by very high and high landslide susceptibility zones constituting 55% of the study area. These are the most tortuous and rugged zones with high occurrence and impact of landslides. The study also identified and mapped a total of 317 landslides along National Highway 1D through rigorous multiple field surveys and secondary sources. There is a frequent incidence of traffic disruptions along the road. Thus there is an urgent need to mitigate the landslide hazard particularly to avert the disruption from Sonamarg to Leh road, which causes huge inconvenience in terms of human and economic losses.

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Landslide susceptibility assessment of national highway 1D from Sonamarg to Kargil, Jammu and Kashmir, India using frequency ratio method

Cited by 18 publications

References 29 publications

Slope Shape and Edge Intelligent Recognition Technology Based on Deep Neural Sensing Network

Slope Shape and Edge Intelligent Recognition Technology Based on Deep Neural Sensing Network

Landslide Susceptibility Prediction Based on Frequency Ratio Method and C5.0 Decision Tree Model

Landslide Susceptibility Zonation along NH 1D from Sonamarg to Kargil, North Western Himalaya

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