Evaluation and comparison of the advanced metaheuristic and conventional machine learning methods for the prediction of landslide occurrence

Yuan, Chi; Moayedi, Hossein

doi:10.1007/s00366-019-00798-x

Cited by 46 publications

(25 citation statements)

References 56 publications

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“…With this new compatibility, the algorithm is predicted again, and new compatibility can be selected per each particle. In many studies (Zhang,et al [42], Yuan and Moayedi [43], Yuan and Moayedi [44], Xi, et al [45] and Moayedi, et al [46]), the learning approach of hybrid PSO combined with the ANN algorithm (called PSO-ANN in this study) was suggested ( Figure 3).…”

Section: Particle Swarm Optimization (Pso)mentioning

confidence: 89%

Development of Two Novel Hybrid Prediction Models Estimating Ultimate Bearing Capacity of the Shallow Circular Footing

et al. 2019

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In the present work, we employed artificial neural network (ANN) that is optimized with two hybrid models, namely imperialist competition algorithm (ICA) as well as particle swarm optimization (PSO) in the case of the problem of bearing capacity of shallow circular footing systems. Many types of research have shown that ANNs are valuable techniques for estimating the bearing capacity of the soils. However, most ANN training models have some drawbacks. This study aimed to focus on the application of two well-known hybrid ICA–ANN and PSO–ANN models to the estimation of bearing capacity of the circular footing lied in layered soils. In order to provide the training and testing datasets for the predictive network models, extensive finite element (FE) modelling (a database includes 2810 training datasets and 703 testing datasets) are performed on 16 soil layer sets (weaker soil rested on stronger soil and vice versa). Note that all the independent variables of ICA and PSO algorithms are optimized utilizing a trial and error method. The input includes upper layer thickness/foundation width (h/B) ratio, footing width (B), top and bottom soil layer properties (e.g., six of the most critical soil characteristics), vertical settlement of circular footing (s), where the output was taken ultimate bearing capacity of the circular footing (Fult). Based on coefficient of determination (R2) and Root Mean Square Error (RMSE), amounts of (0.979, 0.076) and (0.984, 0.066) predicted for training dataset and amounts of (0.978, 0.075) and (0.983, 0.066) indicated in the case of the testing dataset of proposed PSO–ANN and ICA–ANN models of prediction network, respectively. It demonstrates a higher reliability of the presented PSO–ANN model for predicting ultimate bearing capacity of circular footing located on double sandy layer soils.

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Section: Particle Swarm Optimization (Pso)mentioning

confidence: 89%

Development of Two Novel Hybrid Prediction Models Estimating Ultimate Bearing Capacity of the Shallow Circular Footing

et al. 2019

Self Cite

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“…Artificial neural networks (ANNs) are capable of predictive tools that were introduced by [32], mimicking the biological neural network. multi-layer perceptron (MLP) is a common type of ANNs that have shown a satisfying performance dealing with many engineering simulations [33][34][35][36][37]. This is due to their ability in generating non-linear equations between the set of inputs and outputs [38,39].…”

Section: Multiple Linear Regression (Mlr)mentioning

confidence: 99%

Predicting Slope Stability Failure through Machine Learning Paradigms

Bui

Moayedi

Gör

et al. 2019

IJGI

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In this study, we employed various machine learning-based techniques in predicting factor of safety against slope failures. Different regression methods namely, multi-layer perceptron (MLP), Gaussian process regression (GPR), multiple linear regression (MLR), simple linear regression (SLR), support vector regression (SVR) were used. Traditional methods of slope analysis (e.g., first established in the first half of the twentieth century) used widely as engineering design tools. Offering more progressive design tools, such as machine learning-based predictive algorithms, they draw the attention of many researchers. The main objective of the current study is to evaluate and optimize various machine learning-based and multilinear regression models predicting the safety factor. To prepare training and testing datasets for the predictive models, 630 finite limit equilibrium analysis modelling (i.e., a database including 504 training datasets and 126 testing datasets) were employed on a single-layered cohesive soil layer. The estimated results for the presented database from GPR, MLR, MLP, SLR, and SVR were assessed by various methods. Firstly, the efficiency of applied models was calculated employing various statistical indices. As a result, obtained total scores 20, 35, 50, 10, and 35, respectively for GPR, MLR, MLP, SLR, and SVR, revealed that the MLP outperformed other machine learning-based models. In addition, SVR and MLR presented an almost equal accuracy in estimation, for both training and testing phases. Note that, an acceptable degree of efficiency was obtained for GPR and SLR models. However, GPR showed more precision. Following this, the equation of applied MLP and MLR models (i.e., in their optimal condition) was derived, due to the reliability of their results, to be used in similar slope stability problems.

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“…The BBO and GOA are nature-inspired algorithms which respectively mimic the biogeography knowledge (i.e., the distribution of different species) and herding behavior of grasshoppers to achieve the optimal solutions of a mathematically defined problem. As a common trait, both methods draw on two major steps, namely migration and mutation in the BBO and exploration and exploitation in the GOA [37,38]. As the first action, some individuals (i.e., the initial population) are generated randomly which represent possible solutions to the problem.…”

Section: Benchmark Modelsmentioning

confidence: 99%

Application of Three Metaheuristic Techniques in Simulation of Concrete Slump

et al. 2019

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Slump is a workability-related characteristic of concrete mixture. This paper investigates the efficiency of a novel optimizer, namely ant lion optimization (ALO), for fine-tuning of a neural network (NN) in the field of concrete slump prediction. Two well-known optimization techniques, biogeography-based optimization (BBO) and grasshopper optimization algorithm (GOA), are also considered as benchmark models to be compared with ALO. Considering seven slump effective factors, namely cement, slag, water, fly ash, superplasticizer (SP), fine aggregate (FA), and coarse aggregate (CA), the mentioned algorithms are synthesized with a neural network to determine the best-fitted neural parameters. The most appropriate complexity of each ensemble is also found by a population-based sensitivity analysis. The findings revealed that the proposed ALO-NN model acquires a good approximation of concrete slump, regarding the calculated root mean square error (RMSE = 3.7788) and mean absolute error (MAE = 3.0286). It also outperformed both BBO-NN (RMSE = 4.1859 and MAE = 3.3465) and GOA-NN (RMSE = 4.9553 and MAE = 3.8576) ensembles.

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Evaluation and comparison of the advanced metaheuristic and conventional machine learning methods for the prediction of landslide occurrence

Cited by 46 publications

References 56 publications

Development of Two Novel Hybrid Prediction Models Estimating Ultimate Bearing Capacity of the Shallow Circular Footing

Development of Two Novel Hybrid Prediction Models Estimating Ultimate Bearing Capacity of the Shallow Circular Footing

Predicting Slope Stability Failure through Machine Learning Paradigms

Application of Three Metaheuristic Techniques in Simulation of Concrete Slump

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