標(biāo)題: Titlebook: Application of Machine Learning in Slope Stability Assessment; Zhang Wengang,Liu Hanlong,Zhang Yanmei Book 2023 Science Press 2023 Slope S [打印本頁] 作者: 頻率 時間: 2025-3-21 17:50
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書目名稱Application of Machine Learning in Slope Stability Assessment被引頻次
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書目名稱Application of Machine Learning in Slope Stability Assessment讀者反饋學(xué)科排名
作者: 滲透 時間: 2025-3-21 23:00
Application of Machine Learning in Slope Stability Assessment作者: Buttress 時間: 2025-3-22 02:59
Application of Machine Learning in Slope Stability Assessment978-981-99-2756-2作者: 懦夫 時間: 2025-3-22 04:58
Book 2023to 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 fut作者: 貨物 時間: 2025-3-22 09:19 作者: opinionated 時間: 2025-3-22 14:35 作者: 暗指 時間: 2025-3-22 17:55 作者: 才能 時間: 2025-3-22 22:22 作者: Orthodontics 時間: 2025-3-23 03:55 作者: 大罵 時間: 2025-3-23 06:23
Xia Wang,Yi Zhao,Wolfgang A. Halangengineering slopes. This chapter focuses on review of the slope stability analysis methods including the theoretical solutions, numerical simulations, physical experimentations, the in-situ monitoring methods as well as the machine learning approaches.作者: septicemia 時間: 2025-3-23 10:05 作者: 單調(diào)性 時間: 2025-3-23 16:21 作者: Bridle 時間: 2025-3-23 20:14 作者: 變異 時間: 2025-3-24 01:06 作者: MENT 時間: 2025-3-24 05:39 作者: 侵害 時間: 2025-3-24 10:14 作者: 繁忙 時間: 2025-3-24 14:34
Efficient Reliability Analysis of Slopes in Spatially Variable Soils Using XGBoost,perties. This chapter develops an efficient extreme gradient boosting (XGBoost)-based reliability analysis approach for evaluating the earth dam slope failure probability. With the aid of the proposed approach, the failure probability of earth dam slope can be evaluated rationally and efficiently.作者: Truculent 時間: 2025-3-24 15:03
ng case history.Encloses some source codes as supplementary 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 作者: periodontitis 時間: 2025-3-24 20:09 作者: 軌道 時間: 2025-3-25 01:44
C. H. C. Leung,J. Liu,A. Milani,W. S. Chancal landslide occurrences into consideration and the further analysis of the key features was always lacking. This chapter aims to combine qualitative and quantitative analysis and examine its effect on mapping accuracy. The key features for identifying landslide/non-landslide points of different sub-zones are further analyzed.作者: 取之不竭 時間: 2025-3-25 03:44 作者: lymphedema 時間: 2025-3-25 10:16 作者: recede 時間: 2025-3-25 13:51 作者: 淘氣 時間: 2025-3-25 17:56
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.作者: 善于騙人 時間: 2025-3-25 20:15 作者: Peak-Bone-Mass 時間: 2025-3-26 03:02
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作者: anniversary 時間: 2025-3-26 07:15
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作者: 鋼筆尖 時間: 2025-3-26 09:32
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.作者: jet-lag 時間: 2025-3-26 16:00
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作者: ticlopidine 時間: 2025-3-26 19:05 作者: sed-rate 時間: 2025-3-26 22:54 作者: 并置 時間: 2025-3-27 03:31 作者: 暴發(fā)戶 時間: 2025-3-27 06:54
https://doi.org/10.1007/978-3-658-03958-5ity since the failure events may lead to disastrous consequences. This chapter proposes an efficient seismic slope stability analysis approach by introducing advanced gradient boosting algorithms, namely categorical boosting (CatBoost),light gradient boosting machine (LightGBM), and extreme gradient作者: optional 時間: 2025-3-27 11:29
Emergenzen auf der Stufe des Lebendigenive. However, it suffers from a known criticism of extensive computational requirements and poor efficiency, which hinders its application in the reliability analysis of earth dam slope stability. Until now, the effects of spatially variable soil properties on the earth dam slope reliability remain 作者: Inexorable 時間: 2025-3-27 16:40 作者: 陶瓷 時間: 2025-3-27 21:32
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.作者: Cholagogue 時間: 2025-3-27 22:47 作者: Palter 時間: 2025-3-28 05:02 作者: 迫擊炮 時間: 2025-3-28 08:24
Real-Time Monitoring and Early Warning of Landslide, 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.作者: 蛙鳴聲 時間: 2025-3-28 12:05
Overview,e, 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作者: Condescending 時間: 2025-3-28 14:44 作者: precede 時間: 2025-3-28 19:06 作者: 愛花花兒憤怒 時間: 2025-3-29 02:59
Prediction of Slope Stability Using Ensemble Learning Techniques,method 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作者: 施魔法 時間: 2025-3-29 06:48
Landslide Susceptibility Research Combining Qualitative Analysis and Quantitative Evaluation: A Cass, i.e., improving data processing methods, comparing and perfecting the data-driven model itself, but rarely taking the qualitative aspects of the local landslide occurrences into consideration and the further analysis of the key features was always lacking. This chapter aims to combine qualitative作者: intoxicate 時間: 2025-3-29 09:07
Application of Transfer Learning to Improve Landslide Susceptibility Modeling Performance,ional long short-term memory model on the basis of LandslideNet to deal with the input shaped as a one-dimensional array. It was used to extract the characteristics of the areas prone to landslides based on an area with dense data points (source domain) first, then the obtained knowledge was transfe
