GIS based landslide susceptibility zonation mapping using frequency ratio, information value and weight of evidence: a case study in Kinnaur District HP India

Qazi, A. Raziq; Singh, Kanwarpreet; Vishwakarma, Dinesh Kumar; Abdo, Hazem Ghassan

doi:10.1007/s10064-023-03344-8

Cited by 11 publications

(1 citation statement)

References 91 publications

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“…Quantitative methods are able to utilize the relationship between data and geological hazards for susceptibility assessments, which is more objective and accurate than qualitative methods, as these mainly include statistical methods and artificial intelligence methods. There are many statistically based methods of susceptibility assessment, such as the information value method, the frequency ratio method, the coefficient of determination method, and the weights of evidence method [23][24][25]. Artificial intelligence methods can learn the deep features of geological hazards and reveal the nonlinear change characteristics of geological hazards: for example, machine learning methods such as logistic regressions [26], support vector machine [27], and random forest [28], and the deep learning methods such as deep neural network [29], convolutional neural network [30], and recurrent neural network [31].…”

Section: Introductionmentioning

confidence: 99%

Geological Hazard Identification and Susceptibility Assessment Based on MT-InSAR

Lu,

Yang,

Zeng

et al. 2023

Remote Sensing

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Geological hazards often occur in mountainous areas and are sudden and hidden, so it is important to identify and assess geological hazards. In this paper, the western mountainous area of Beijing was selected as the study area. We conducted research on landslides, collapses, and unstable slopes in the study area. The surface deformation of the study area was monitored by multi-temporal interferometric synthetic aperture radar (MT-InSAR), using a combination of multi-looking point selection and permanent scatterer (PS) point selection methods. Random forest (RF), support vector machine (SVM), convolutional neural network (CNN), and recurrent neural network (RNN) models were selected for the assessment of geological hazard susceptibility. Sixteen geological hazard-influencing factors were collected, and their information values were calculated using their features. Multicollinearity analysis with the relief-F method was used to calculate the correlation and importance of the factors for factor selection. The results show that the deformation rate along the line-of-sight (LOS) direction is between −44 mm/year and 28 mm/year. A total of 60 geological hazards were identified by combining surface deformation with optical imagery and other data, including 7 collapses, 25 unstable slopes, and 28 landslides. Forty-eight of the identified geological hazards are not recorded in the Beijing geological hazards list. The most effective model in the study area was RF. The percentage of geological hazard susceptibility zoning in the study area is as follows: very low susceptibility 27.40%, low susceptibility 28.06%, moderate susceptibility 21.19%, high susceptibility 13.80%, very high susceptibility 9.57%.

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