A novel technique based on the improved firefly algorithm coupled with extreme learning machine (ELM-IFF) for predicting the thermal conductivity of soil
TL;DR: All the proposed hybrid models have a great ability to be considered as alternatives for empirical relevant models and can be employed in the initial stages of any engineering projects for fast determination of thermal conductivity.
Abstract: Thermal conductivity is a specific thermal property of soil which controls the exchange of thermal energy. If predicted accurately, the thermal conductivity of soil has a significant effect on geothermal applications. Since the thermal conductivity is influenced by multiple variables including soil type and mineralogy, dry density, and water content, its precise prediction becomes a challenging problem. In this study, novel computational approaches including hybridisation of two metaheuristic optimisation algorithms, i.e. firefly algorithm (FF) and improved firefly algorithm (IFF), with conventional machine learning techniques including extreme learning machine (ELM), adaptive neuro-fuzzy interface system (ANFIS) and artificial neural network (ANN), are proposed to predict the thermal conductivity of unsaturated soils. FF and IFF are used to optimise the internal parameters of the ELM, ANFIS and ANN. These six hybrid models are applied to the dataset of 257 soil cases considering six influential variables for predicting the thermal conductivity of unsaturated soils. Several performance parameters are used to verify the predictive performance and generalisation capability of the developed hybrid models. The obtained results from the computational process confirmed that ELM-IFF attained the best predictive performance with a coefficient of determination = 0.9615, variance account for = 96.06%, root mean square error = 0.0428, and mean absolute error = 0.0316 on the testing dataset (validation phase). The results of the models are also visualised and analysed through different approaches using Taylor diagrams, regression error characteristic curves and area under curve scores, rank analysis and a novel method called accuracy matrix. It was found that all the proposed hybrid models have a great ability to be considered as alternatives for empirical relevant models. The developed ELM-IFF model can be employed in the initial stages of any engineering projects for fast determination of thermal conductivity.
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TL;DR: The newly constructed HENSM model is very potential to be a new alternative in handling the overfitting issues of CML models and hence, can be used to predict the concrete CS, including the design of less polluting and more sustainable concrete constructions.
166 citations
TL;DR: It can be concluded that the newly constructed ELM-based ANSI models can solve the difficulties in tuning the acceleration coefficients of SPSO by the trial-and-error method for predicting the CBR of soils and be further applied to other real-time problems of geotechnical engineering.
57 citations
TL;DR: Novel hybrid models based on combination of the modified version of the equilibrium optimizer (EO) and two conventional machine learning algorithms, namely extreme learning machine (ELM) and artificial neural network (ANN) are constructed to predict the permeability of tight carbonates.
Abstract: It is a problematic task to perform petro-physical property prediction of carbonate reservoir rocks in most cases, specifically for permeability prediction since a carbonate rock most commonly contains grains of heterogeneous size distributions. Consequently, the permeability calculation of tight rocks in laboratories is costly and very time-consuming. Therefore, this study aims to tackle this issue by developing novel hybrid models based on combination of the modified version of the equilibrium optimizer (EO), i.e., MEO, and two conventional machine learning algorithms, namely extreme learning machine (ELM) and artificial neural network (ANN). The MEO employs a mutation mechanism in order to avoid trapping in local optima of EO by increasing the search capabilities. In this study, ELM-MEO and ANN-MEO, novel metaheuristic ELM-based and ANN-based algorithms, were constructed to predict the permeability of tight carbonates. In addition, ANN, ELM, RF, RVM and MARS combined with particle swarm optimization and genetic programming algorithm have a better insight into the performances for preferably predicting the permeability carbonates. The results illustrate that the proposed ELM-MEO model with R2 = 0.9323, RMSE = 0.0612 and MAE = 0.0442 in training stage and R2 = 0.8743, RMSE = 0.0806 and MAE = 0.0660 in testing stage, outperformed other ELM-based and ANN-based metaheuristic models in predicting the permeability of tight carbonates at all levels.
47 citations
TL;DR: The proposed MARS-L model is very potential to be an alternate solution to estimate the CBR value in different phases of civil engineering projects, and has the most accurate prediction in predicting the soaked CBR at all stages.
45 citations
TL;DR: The newly constructed ANFIS-APSO outperformed the standard ANFis-PSO model including other hybrid models, and hence has very potential to be a new alternative to assist engineers for predicting the PV power of solar systems at short- and long-time horizons.
Abstract: Accurate photovoltaic (PV) power prediction is necessary for future development of the micro-grids projects and the economic dispatch sector. This study investigates the potential of using a novel hybrid approach of adaptive swarm intelligence techniques and adaptive network-based fuzzy inference system (ANFIS) in estimating the PV power of a solar system at different time horizons, from 0 to 24 h. The developed approach is an integration of ANFIS and particle swarm optimization (PSO) with adaptive and time-varying acceleration coefficients, i.e., ANFIS-APSO (ANFIS-PSO with adaptive acceleration coefficients) and ANFIS-IPSO (ANFIS-PSO with time-varying acceleration coefficients), were developed. The performance of the proposed models was compared with other hybrid ANFIS models, namely ANFIS-PSO (ANFIS coupled with PSO), ANFIS-BBO (ANFIS coupled with biogeography-based optimization), ANFIS-GA (ANFIS coupled with genetic algorithm), and ANFIS-GWO (ANFIS coupled with grey wolf optimization). For this purpose, the climatic variables and historical PV power data of a 960 kWP grid-connected PV system in the south of Italy were used to design and evaluate the models. Several statistical analyses were implemented to evaluate the accuracy of the proposed models and assess the impact of variables that affects the PV power values. The experimental results show that the proposed ANFIS-APSO attained the most accurate prediction of the PV power with R2 = 0.835 and 0.657, RMSE = 0.088 kW and 0.081 kW, and MAE = 0.077 kW and 0.079 kW in the testing phase at time horizons 12 h and 24 h, respectively. Based on the obtained results, the newly constructed ANFIS-APSO outperformed the standard ANFIS-PSO model including other hybrid models, and hence very potential to be a new alternative to assist engineers for predicting the PV power of solar systems at short- and long-time horizons.
37 citations
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TL;DR: In this article, a diagram has been devised that can provide a concise statistical summary of how well patterns match each other in terms of their correlation, their root-mean-square difference, and the ratio of their variances.
Abstract: A diagram has been devised that can provide a concise statistical summary of how well patterns match each other in terms of their correlation, their root-mean-square difference, and the ratio of their variances. Although the form of this diagram is general, it is especially useful in evaluating complex models, such as those used to study geophysical phenomena. Examples are given showing that the diagram can be used to summarize the relative merits of a collection of different models or to track changes in performance of a model as it is modified. Methods are suggested for indicating on these diagrams the statistical significance of apparent differences and the degree to which observational uncertainty and unforced internal variability limit the expected agreement between model-simulated and observed behaviors. The geometric relationship between the statistics plotted on the diagram also provides some guidance for devising skill scores that appropriately weight among the various measures of pattern correspondence.
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