Titlebook: Application of Machine Learning in Slope Stability Assessment; Zhang Wengang,Liu Hanlong,Zhang Yanmei Book 2023 Science Press 2023 Slope S

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Application of Transfer Learning to Improve Landslide Susceptibility Modeling Performance,haracteristics of the areas prone to landslides based on an area with dense data points (source domain) first, then the obtained knowledge was transferred to Chongqing for local landslide susceptibility analysis.

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Xia Wang,Yi Zhao,Wolfgang A. Halange, loss of life. The ability to monitor and forecast failure is a major concern for risk management, and it is generally hindered by lack of data. Recent technological advances enable the use of multiple sources of information, such as earth observation, imagery analysis, real-time monitoring, which

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Web-Based Support by Thin-Client Co-browsingion and the stability assessment via VOSviewer, which is a software for constructing and visualizing bibliometric networks. These networks may include journals, researchers, or individual publications, and they can be constructed based on citation, bibliographic coupling, cocitation, or co-authorshi

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https://doi.org/10.1007/978-1-84996-077-9 is a powerful tool for landslide risk reduction. This chapter presents a successful case of early warning for a large disastrous rockslide in Southwestern China, which helps to predict the large rockslide, eventually achieving zero casualties or injuries and almost no property losses.

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Web-Based Support by Thin-Client Co-browsingmethod to predict the slope stability by introducing the random forest (RF) and extreme gradient boosting (XGBoost). As an illustration, the proposed approach is applied to the stability prediction of 786 landslide cases in Yunyang County, Chongqing, China. For comparison, the predictive performance

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