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Application of machine learning in slope stability assessment

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Application of machine learning in slope stability assessmentby Zhang Wengang ... [et al.].
其他作者:	Zhang, Wengang.
出版者:	Singapore :Springer Nature Singapore :2023.
面頁冊數:	xix, 201 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Slopes (Soil mechanics)Stability
電子資源:	https://doi.org/10.1007/978-981-99-2756-2
ISBN:	9789819927562$q(electronic bk.)

Application of machine learning in slope stability assessment
Application of machine learning in slope stability assessment[electronic resource] /by Zhang Wengang ... [et al.]. - Singapore :Springer Nature Singapore :2023. - xix, 201 p. :ill. (some col.), digital ;24 cm.

Overview -- Remote Sensing Techniques -- Machine Learning Algorithms -- Real-time Monitoring and Early Warning of Landslide -- Prediction of Slope Stability Using Ensemble Learning Techniques -- Application of LSTM and Prophet Algorithm in Slope Displacement Prediction -- Displacement Prediction of Jiuxianping Landslide using GRU Networks -- Efficient Seismic Stability Analysis of Slopes Subjected to Water Level Changes Using Gradient Boosting Algorithms -- Efficient Reliability Analysis of Slopes in Spatially Variable Soils Using XGBoost -- Efficient Time-variant Reliability Analysis of Bazimen Landslide in the TGRA Using XGBoost and LightGBM -- Future work recommendation.

This book focuses on the application of machine learning in slope stability assessment. The contents include: overview of machine learning approaches, the mainstream smart in-situ monitoring techniques, the applications of the main machine learning algorithms, including the supervised learning, unsupervised learning, semi- supervised learning, reinforcement learning, deep learning, ensemble learning, etc., in slope engineering and landslide prevention, introduction of the smart in-situ monitoring and slope stability assessment based on two well-documented case histories, the prediction of slope stability using ensemble learning techniques, the application of Long Short-Term Memory Neural Network and Prophet Algorithm in Slope Displacement Prediction, displacement prediction of Jiuxianping landslide using gated recurrent unit (GRU) networks, seismic stability analysis of slopes subjected to water level changes using gradient boosting algorithms, efficient reliability analysis of slopes in spatially variable soils using XGBoost, efficient time-variant reliability analysis of Bazimen landslide in the Three Gorges Reservoir Area using XGBoost and LightGBM algorithms, as well as the future work recommendation.The authors also provided their own thoughts learnt from these applications as well as work ongoing and future recommendations.

ISBN: 9789819927562$q(electronic bk.)

Standard No.: 10.1007/978-981-99-2756-2doiSubjects--Topical Terms:

919240
Slopes (Soil mechanics)
--Stability

LC Class. No.: TA710

Dewey Class. No.: 624.1513630285631

Application of machine learning in slope stability assessment
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505 0 $a Overview -- Remote Sensing Techniques -- Machine Learning Algorithms -- Real-time Monitoring and Early Warning of Landslide -- Prediction of Slope Stability Using Ensemble Learning Techniques -- Application of LSTM and Prophet Algorithm in Slope Displacement Prediction -- Displacement Prediction of Jiuxianping Landslide using GRU Networks -- Efficient Seismic Stability Analysis of Slopes Subjected to Water Level Changes Using Gradient Boosting Algorithms -- Efficient Reliability Analysis of Slopes in Spatially Variable Soils Using XGBoost -- Efficient Time-variant Reliability Analysis of Bazimen Landslide in the TGRA Using XGBoost and LightGBM -- Future work recommendation.
520 $a This book focuses on the application of machine learning in slope stability assessment. The contents include: overview of machine learning approaches, the mainstream smart in-situ monitoring techniques, the applications of the main machine learning algorithms, including the supervised learning, unsupervised learning, semi- supervised learning, reinforcement learning, deep learning, ensemble learning, etc., in slope engineering and landslide prevention, introduction of the smart in-situ monitoring and slope stability assessment based on two well-documented case histories, the prediction of slope stability using ensemble learning techniques, the application of Long Short-Term Memory Neural Network and Prophet Algorithm in Slope Displacement Prediction, displacement prediction of Jiuxianping landslide using gated recurrent unit (GRU) networks, seismic stability analysis of slopes subjected to water level changes using gradient boosting algorithms, efficient reliability analysis of slopes in spatially variable soils using XGBoost, efficient time-variant reliability analysis of Bazimen landslide in the Three Gorges Reservoir Area using XGBoost and LightGBM algorithms, as well as the future work recommendation.The authors also provided their own thoughts learnt from these applications as well as work ongoing and future recommendations.
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