東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Application of machine learning in s...

Zhang, Wengang.

FindBook

Google Book

Amazon

博客來

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 assessment/ by 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.
內容註:	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.
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

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

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

3592304
Slopes (Soil mechanics)
--Stability

LC Class. No.: TA710

Dewey Class. No.: 624.1513630285631

Application of machine learning in slope stability assessment
LDR:03031nmm a2200325 a 4500 001 2332994
003 DE-He213
005 20230708124452.0
006 m d
007 cr nn 008maaau
008 240402s2023 si s 0 eng d
020 $a 9789819927562 $q (electronic bk.)
020 $a 9789819927555 $q (paper)
024 7 $a 10.1007/978-981-99-2756-2 $2 doi
035 $a 978-981-99-2756-2
040 $a GP $c GP
041 0 $a eng
050 4 $a TA710
072 7 $a TN $2 bicssc
072 7 $a TEC009020 $2 bisacsh
072 7 $a TN $2 thema
082 0 4 $a 624.1513630285631 $2 23
090 $a TA710 $b .A652 2023
245 0 0 $a Application of machine learning in slope stability assessment $h [electronic resource] / $c by Zhang Wengang ... [et al.].
260 $a Singapore : $b Springer Nature Singapore : $b Imprint: Springer, $c 2023.
300 $a xix, 201 p. : $b ill. (some col.), digital ; $c 24 cm.
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.
650 0 $a Slopes (Soil mechanics) $x Stability $x Mathematical models. $3 3592304
650 0 $a Slopes (Soil mechanics) $x Stability $x Data processing. $3 3663367
650 0 $a Landslide hazard analysis $x Mathematical models. $3 3663368
650 0 $a Landslide hazard analysis $x Data processing. $3 2156497
650 0 $a Machine learning $x Industrial applications. $3 1361578
650 0 $a Artificial intelligence $x Engineering applications. $3 1567162
650 1 4 $a Civil Engineering. $3 891037
650 2 4 $a Artificial Intelligence. $3 769149
650 2 4 $a Geoengineering. $3 3538885
700 1 $a Zhang, Wengang. $3 3442641
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-981-99-2756-2
950 $a Engineering (SpringerNature-11647)