Slope stability prediction using ensemble learning techniques: A case study in Yunyang County, Chongqing, China

Zhang, Wengang; Li, Hongrui; Han, Liang; Chen, Longlong; Wang, Lin

doi:10.1016/j.jrmge.2021.12.011

Cited by 156 publications

(71 citation statements)

References 37 publications

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“…Zhang et al [141] used RF and XGBoost ensemble method to predict slope stability for Yunyang County, Chongqing, China considering twelve LCFs. The prediction performance of the ensemble methods was compared with SVM and LGR.…”

Section: Author Year Ensemble Methodsmentioning

confidence: 99%

“…It was developed to provide a more general approach to the probabilistic prediction method. XGBoost [129,140,141] belongs to the gradient boosting family and is an effective supervised classification model. It is a preferred ensemble technique mainly because of its ability to prevent over-fitting issues by using bagging-bootstrap aggregation.…”

Section: Ensemble Techniquesmentioning

confidence: 99%

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Landslide Susceptibility Mapping Using Machine Learning: A Literature Survey

et al. 2022

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Landslide is a devastating natural disaster, causing loss of life and property. It is likely to occur more frequently due to increasing urbanization, deforestation, and climate change. Landslide susceptibility mapping is vital to safeguard life and property. This article surveys machine learning (ML) models used for landslide susceptibility mapping to understand the current trend by analyzing published articles based on the ML models, landslide causative factors (LCFs), study location, datasets, evaluation methods, and model performance. Existing literature considered in this comprehensive survey is systematically selected using the ROSES protocol. The trend indicates a growing interest in the field. The choice of LCFs depends on data availability and case study location; China is the most studied location, and area under the receiver operating characteristic curve (AUC) is considered the best evaluation metric. Many ML models have achieved an AUC value > 0.90, indicating high reliability of the susceptibility map generated. This paper also discusses the recently developed hybrid, ensemble, and deep learning (DL) models in landslide susceptibility mapping. Generally, hybrid, ensemble, and DL models outperform conventional ML models. Based on the survey, a few recommendations and future works which may help the new researchers in the field are also presented.

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Section: Author Year Ensemble Methodsmentioning

confidence: 99%

Section: Ensemble Techniquesmentioning

confidence: 99%

Landslide Susceptibility Mapping Using Machine Learning: A Literature Survey

et al. 2022

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“…With their expertise in non-linear modelling, machine learning (ML) algorithms can capture the complex behavior of influencing parameters and provide feasible tools for simulating many complex problems. In the past, several ML algorithms, namely ANN, adaptive neuro-fuzzy inference system (ANFIS), fuzzy inference system (FIS); extreme gradient boosting machine (XGBoost), multivariate adaptive regression splines (MARS), extreme learning machine (ELM), ensemble learning techniques, support vector machine (SVM), and so on, have been employed to estimate the desired output including rock strength, landslide displacement, slope stability analysis, prediction of soil-water characteristic curve, inverse analysis of soil and wall properties in braced excavation, concrete compressive strength and so on (Armaghani et al 2016a, b;Asterisand and Kolovos 2017;Zhang et al 2017Zhang et al , 2018Zhang et al , 2020Zhang et al , 2021Zhang et al , 2022aZhang and Phoon 2022;Zhang and Liu 2022;.…”

Section: Research Significancementioning

confidence: 99%

Closed-Form Equation for Estimating Unconfined Compressive Strength of Granite from Three Non-destructive Tests Using Soft Computing Models

et al. 2022

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The use of three artificial neural network (ANN)-based models for the prediction of unconfined compressive strength (UCS) of granite using three non-destructive test indicators, namely pulse velocity, Schmidt hammer rebound number, and effective porosity, has been investigated in this study. For this purpose, a sum of 274 datasets was compiled and used to train and validate three ANN models including ANN constructed using Levenberg–Marquardt algorithm (ANN-LM), a combination of ANN and particle swarm optimization (ANN-PSO), and a combination of ANN and imperialist competitive algorithm (ANN-ICA). The constructed ANN-LM model was proven to be the most accurate based on experimental findings. In the validation phase, the ANN-LM model has achieved the best predictive performance with R = 0.9607 and RMSE = 14.8272. Experimental results show that the developed ANN-LM outperforms a number of existing models available in the literature. Furthermore, a Graphical User Interface (GUI) has been developed which can be readily used to estimate the UCS of granite through the ANN-LM model. The developed GUI is made available as a supplementary material.

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“…Other algorithms, like Adaptive Boosting (AdaBoost) for water parameters' estimations [19] or Light Gradient Boosting Machines for evapotranspiration modeling [20], are also recommended. For a broader explanation of the applied algorithms, one could have a look into [21] for the Extra Trees method or into [22,23] for Random Forest or Extreme Gradient Boosting implementation descriptions.…”

Section: Modeling Using Ensemble Machine Learningmentioning

confidence: 99%

Soil Moisture a Posteriori Measurements Enhancement Using Ensemble Learning

Ruszczak

Boguszewska-Mańkowska

2022

Sensors

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This work aimed to assess the recalibration and accurate characterization of commonly used smart soil-moisture sensors using computational methods. The paper describes an ensemble learning algorithm that boosts the performance of potato root moisture estimation and increases the simple moisture sensors’ performance. It was prepared using several month-long everyday actual outdoor data and validated on the separated part of that dataset. To obtain conclusive results, two different potato varieties were grown on 24 separate plots on two distinct soil profiles and, besides natural precipitation, several different watering strategies were applied, and the experiment was monitored during the whole season. The acquisitions on every plot were performed using simple moisture sensors and were supplemented with reference manual gravimetric measurements and meteorological data. Next, a group of machine learning algorithms was tested to extract the information from this measurements dataset. The study showed the possibility of decreasing the median moisture estimation error from 2.035% for the baseline model to 0.808%, which was achieved using the Extra Trees algorithm.

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Slope stability prediction using ensemble learning techniques: A case study in Yunyang County, Chongqing, China

Cited by 156 publications

References 37 publications

Landslide Susceptibility Mapping Using Machine Learning: A Literature Survey

Landslide Susceptibility Mapping Using Machine Learning: A Literature Survey

Closed-Form Equation for Estimating Unconfined Compressive Strength of Granite from Three Non-destructive Tests Using Soft Computing Models

Soil Moisture a Posteriori Measurements Enhancement Using Ensemble Learning

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