Prediction of the ground temperature with ANN, LS-SVM and fuzzy LS-SVM for GSHP application

Zhou, Shiyu; Chu, Xin; Cao, Shiwei; Li, Xiaoping; Zhou, Yucheng

doi:10.1016/j.geothermics.2019.101757

Cited by 46 publications

(11 citation statements)

References 21 publications

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“…Support vector machine (SVM) presents a number of superiorities in comparison to ANN, it can map the nonlinear relationship between inputs and output(s) avoiding to be stocked into local minima, it can solve problems using only support vector and it can deal with small data set. 20,21 The performance of SVM model can be determined based on the selected kernel function and its parameters. 16 The predicted output can be expressed via SVM model as follows: 16 (2)…”

Section: Ls-svm and Svm Modelmentioning

confidence: 99%

“…Compared to the conventional SVM, LS-SVM can convert the inequality constraints into equality constraints. 21 3 ANN, LS-SVR, and SVR modelling…”

Section: Ls-svm and Svm Modelmentioning

confidence: 99%

“…In order to ensure the rapid convergence of the models, a normalisation stage of inputs was done using the proposed function expressed by expression 4, while the outputs were normalised/post-normalised using the two functions programmed in MATLAB {premnmx(Y)/postmnmx(Y)} as represented in expressions 5 and 6, respectively. The aim of the normalisation was to have the data in the same range and avoid greater errors 21.…”

mentioning

confidence: 99%

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Ternary Multicomponent Adsorption Modelling Using ANN, LS-SVR, and SVR Approach – Case Study

Yettou

Laidi

Bey

et al. 2021

Kemija U Industriji

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The aim of this work was to develop three artificial intelligence-based methods to model the ternary adsorption of heavy metal ions {Pb 2+ , Hg 2+ , Cd 2+ , Cu 2+ , Zn 2+ , Ni 2+ , Cr 4+ } on different adsorbates {activated carbon, chitosan, Danish peat, Heilongjiang peat, carbon sunflower head, and carbon sunflower stem). Results show that support vector regression (SVR) performed slightly better, more accurate, stable, and more rapid than least-square support vector regression (LS-SVR) and artificial neural networks (ANN). The SVR model is highly recommended for estimating the ternary adsorption kinetics of a multicomponent system.

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Section: Ls-svm and Svm Modelmentioning

confidence: 99%

“…Compared to the conventional SVM, LS-SVM can convert the inequality constraints into equality constraints. 21 3 ANN, LS-SVR, and SVR modelling…”

Section: Ls-svm and Svm Modelmentioning

confidence: 99%

mentioning

confidence: 99%

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Ternary Multicomponent Adsorption Modelling Using ANN, LS-SVR, and SVR Approach – Case Study

Yettou

Laidi

Bey

et al. 2021

Kemija U Industriji

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“…This optimal hyperplane is determined by the existing support vectors and constraints. Eqs 10 and 11 indicate the constraints [82][83].…”

Section: Support Vector Machinementioning

confidence: 99%

Evaluation of Contributing Factors Affecting Number of Vehicles Involved in Crashes Using Machine Learning Techniques in Rural Roads of Cosenza, Italy

Guido¹,

Haghshenas²,

Haghshenas³

et al. 2021

Preprint

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Evaluation of road safety is a critical issue having to be conducted for successful safety management in road transport systems, whereas safety management is considered in road transportation systems as a challenging task according to the dynamic of this issue and the presence of a large number of effective parameters on road safety. Therefore, evaluation and analysis of important contributing factors affecting the number of crashes play a key role in increasing the efficiency of road safety. For this purpose, in this research work, two machine learning algorithms including the group method of data handling (GMDH)-type neural network and a combination of support vector machine (SVM) and the grasshopper optimization algorithm (GOA) are employed for evaluating the number of vehicles involved in the accident based on the seven factors affecting transport safety including the Daylight (DL), Weekday (W), Type of accident (TA), Location (L), Speed limit (SL), Average speed (AS) and Annual average daily traffic (AADT) of rural roads of Cosenza in southern Italy. In this study, 564 data sets of rural areas were investigated and relevant effective parameters were measured. In the next stage, several models were developed to investigate the parameters affecting the safety management of road transportation for rural areas. The results obtained demonstrated that "Average speed" has the highest level and "Weekday" has the lowest level of importance in the investigated rural area. Finally, although the results of both algorithms were the same, the GOA-SVM model showed a better degree of accuracy and robustness than the GMDH model.

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“…4 Architecture representation for the final design of the artificial neural network used in the current work been applied. It was found that this method is convenient [62] and preferable than the random generation method [14,47]. In this method, the initial values of the weights and biases are selected so that the active regions of the transfer functions are used.…”

Section: Hidden Layers and Neurons Sizementioning

confidence: 99%

Simulating the response of ionization chamber system to 137Cs irradiator using the artificial neural network modeling algorithm

Sadek

2020

SN Appl. Sci.

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The artificial neural network algorithm has been used to simulate the response of an ionization chamber dosimetric system to the 137 Cs irradiator facility at the National Institute for Standards, Egypt. The performance of the designed model has been investigated over different processes functions and a wide range of parameters till the optimum performance has been approached. The standard uncertainty of the designed model has been evaluated for different dose-rate levels. It has been found that the dose-rate can be evaluated using the designed model with an uncertainty < 2.0% for the dose-rate level < 10 2 mGy/h and with an uncertainty < 0.5% for the dose-rate level [10 2 −10 3 ] mGy/h . The bias of the designed model has been evaluated and compared with the usual interpolation algorithms. The errors in the evaluated dose-rate using these methods have exceeded the 100% error level and approached the 20% error level for the dose rates less and greater than 10 mGy/h , respectively. While, the error in the evaluated dose-rate using the designed model did not exceed 7% and 2% for the same dose-rate levels, respectively.

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Prediction of the ground temperature with ANN, LS-SVM and fuzzy LS-SVM for GSHP application

Cited by 46 publications

References 21 publications

Ternary Multicomponent Adsorption Modelling Using ANN, LS-SVR, and SVR Approach – Case Study

Ternary Multicomponent Adsorption Modelling Using ANN, LS-SVR, and SVR Approach – Case Study

Evaluation of Contributing Factors Affecting Number of Vehicles Involved in Crashes Using Machine Learning Techniques in Rural Roads of Cosenza, Italy

Simulating the response of ionization chamber system to 137Cs irradiator using the artificial neural network modeling algorithm

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