A novel technique based on the improved firefly algorithm coupled with extreme learning machine (ELM-IFF) for predicting the thermal conductivity of soil

Kardani, Navid; Bardhan, Abidhan; Samui, Pijush; Nazem, Majidreza; Zhou, Annan; Armaghani, Danial Jahed

doi:10.1007/s00366-021-01329-3

Cited by 95 publications

(33 citation statements)

References 59 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…where FPR and TPR are the false positive rate and true positive rate. The AUC is computed by using Equation (19).…”

Section: Validation Methodsmentioning

confidence: 99%

“…Among the advanced artificial intelligence approaches, neural network (NN) models are found to be capable and reliable for big data processing and modelling [18]. Effective applications of ANNs have been achieved in various fields of domains [19]. ANN has been acknowledged as an effective and powerful approach in different studies, such as slope stability analysis [20], soil analysis [21] and rock characteristic studies [22], due to its capacity to manage data without relying on the measurement scale or the way data are organised.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Comparison between Deep Learning and Tree-Based Machine Learning Approaches for Landslide Susceptibility Mapping

Saha

Hembram²,

Pradhan

et al. 2021

Water

View full text Add to dashboard Cite

The efficiency of deep learning and tree-based machine learning approaches has gained immense popularity in various fields. One deep learning model viz. convolution neural network (CNN), artificial neural network (ANN) and four tree-based machine learning models, namely, alternative decision tree (ADTree), classification and regression tree (CART), functional tree and logistic model tree (LMT), were used for landslide susceptibility mapping in the East Sikkim Himalaya region of India, and the results were compared. Landslide areas were delimited and mapped as landslide inventory (LIM) after gathering information from historical records and periodic field investigations. In LIM, 91 landslides were plotted and classified into training (64 landslides) and testing (27 landslides) subsets randomly to train and validate the models. A total of 21 landslide conditioning factors (LCFs) were considered as model inputs, and the results of each model were categorised under five susceptibility classes. The receiver operating characteristics curve and 21 statistical measures were used to evaluate and prioritise the models. The CNN deep learning model achieved the priority rank 1 with area under the curve of 0.918 and 0.933 by using the training and testing data, quantifying 23.02% and 14.40% area as very high and highly susceptible followed by ANN, ADtree, CART, FTree and LMT models. This research might be useful in landslide studies, especially in locations with comparable geophysical and climatological characteristics, to aid in decision making for land use planning.

show abstract

“…where FPR and TPR are the false positive rate and true positive rate. The AUC is computed by using Equation (19).…”

Section: Validation Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Comparison between Deep Learning and Tree-Based Machine Learning Approaches for Landslide Susceptibility Mapping

Saha

Hembram²,

Pradhan

et al. 2021

Water

View full text Add to dashboard Cite

show abstract

“…When the variances associated with the frequency distribution of error magnitudes increase, the error values will be increased. The mentioned performance indices were utilized and computed in different studies and their formulas and process of calculation can be found in the literature [80][81][82][83]. In this study, the mentioned performance indices will be used to evaluate the model's prediction capacity.…”

Section: Performance Indicesmentioning

confidence: 99%

Factors Influencing Pile Friction Bearing Capacity: Proposing a Novel Procedure Based on Gradient Boosted Tree Technique

et al. 2021

Self Cite

View full text Add to dashboard Cite

In geotechnical engineering, there is a need to propose a practical, reliable and accurate way for the estimation of pile bearing capacity. A direct measure of this parameter is difficult and expensive to achieve on-site, and needs a series of machine settings. This study aims to introduce a process for selecting the most important parameters in the area of pile capacity and to propose several tree-based techniques for forecasting the pile bearing capacity, all of which are fully intelligent. In terms of the first objective, pile length, hammer drop height, pile diameter, hammer weight, and N values of the standard penetration test were selected as the most important factors for estimating pile capacity. These were then used as model inputs in different tree-based techniques, i.e., decision tree (DT), random forest (RF), and gradient boosted tree (GBT) in order to predict pile friction bearing capacity. This was implemented with the help of 130 High Strain Dynamic Load tests which were conducted in the Kepong area, Malaysia. The developed tree-based models were assessed using various statistical indices and the best performance with the lowest system error was obtained by the GBT technique. The coefficient of determination (R2) values of 0.901 and 0.816 for the train and test parts of the GBT model, respectively, showed the power and capability of this tree-based model in estimating pile friction bearing capacity. The GBT model and the input selection process proposed in this research can be introduced as a new, powerful, and practical methodology to predict pile capacity in real projects.

show abstract

“…Information exchange between the top fireflies, or the optimal solutions during the process of the light intensity updating was developed in [11]. The Levy distribution was applied in FA instead of traditional uniform distribution to strengthen the exploration in global space in [12] and [13]. Xu et al [14] combined the binary firefly algorithm with opposition-based learning to select features in classification.…”

Section: Introductionmentioning

confidence: 99%

Feature Selection and Parameter Optimization of Support Vector Machines Based on a Local Search Based Firefly Algorithm for Classification of Formulas in Traditional Chinese Medicine

Shi

Liu

Zhang

et al. 2022

IEICE Trans. Fundamentals

View full text Add to dashboard Cite

A novel technique based on the improved firefly algorithm coupled with extreme learning machine (ELM-IFF) for predicting the thermal conductivity of soil

Cited by 95 publications

References 59 publications

Comparison between Deep Learning and Tree-Based Machine Learning Approaches for Landslide Susceptibility Mapping

Comparison between Deep Learning and Tree-Based Machine Learning Approaches for Landslide Susceptibility Mapping

Factors Influencing Pile Friction Bearing Capacity: Proposing a Novel Procedure Based on Gradient Boosted Tree Technique

Feature Selection and Parameter Optimization of Support Vector Machines Based on a Local Search Based Firefly Algorithm for Classification of Formulas in Traditional Chinese Medicine

Contact Info

Product

Resources

About