Artificial neural network models for predicting electrical resistivity of soils from their thermal resistivity

Erzin, Yusuf; Rao, B. Hanumantha; Patel, Anjan; Gumaste, Suchit D.; Singh, Devendrá

doi:10.1016/j.ijthermalsci.2009.06.008

Cited by 67 publications

(25 citation statements)

References 48 publications

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“…(9), and the root mean square error RMSE, represented by Eq. (10), were also calculated to determine the performance of the developed model, as employed by Grima and Babuska [41], Finol et al [70], Gokceoglu [71], Erzin [7], and Erzin et al [9,47,48] VAF…”

Section: Resultsmentioning

confidence: 99%

“…Several learning algorithms have been developed. The back-propagation learning algorithm, the most commonly used neural network algorithm [9,35,[43][44][45][46][47][48], has been successfully applied with to model many phenomena in the field of geotechnical engineering [49][50][51]. In this algorithm, learning is performed through the gradient descent on the sum of the squares of the errors for all the training patterns [43,49].…”

Section: Artificial Neural Networkmentioning

confidence: 99%

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The use of neural networks for the prediction of cone penetration resistance of silty sands

Erzin

Ecemis

2016

Neural Comput & Applic

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In this study, an artificial neural network (ANN) model was developed to predict the cone penetration resistance of silty sands. To achieve this, the data sets reported by Ecemis and Karaman, including the results of three high-quality field tests, namely piezocone penetration test, pore pressure dissipation tests, and direct push permeability tests performed at 20 different locations on the northern coast of the Izmir Gulf in Turkey, have been used in the development of the ANN model. The ANN model consisted of three input parameters (relative density, fines content, and horizontal coefficient of consolidation) and a single output parameter (normalized cone penetration resistance). The results obtained from the ANN model were compared with those obtained from the field tests. It is found that the ANN model is efficient in determining the cone penetration resistance of silty sands and yields cone penetration resistance values that are very close to those obtained from the field tests. Additionally, several performance indices such as the determination coefficient, variance account for, mean absolute error, root mean square error, and scaled percent error were computed to examine the performance of the ANN model developed. The performance level attained in the ANN model shows that the ANN model developed in this study can be employed for predicting cone penetration of silty sands quite efficiently.

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Section: Resultsmentioning

confidence: 99%

Section: Artificial Neural Networkmentioning

confidence: 99%

The use of neural networks for the prediction of cone penetration resistance of silty sands

Erzin

Ecemis

2016

Neural Comput & Applic

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“…5, and the root mean square error RMSE, represented by Eq. 6, were also computed to assess the performance of the developed models (Grima and Babuska 1999;Finol et al 2001;Gokceoglu 2002;Erzin 2007;Erzin and Yukselen 2009;Erzin et al 2008Erzin et al , 2010 …”

Section: Resultsmentioning

confidence: 99%

The use of neural networks for CPT-based liquefaction screening

Erzin

Ecemis

2014

Bull Eng Geol Environ

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This study deals with development of two different artificial neural network (ANN) models: one for predicting cone penetration resistance and the other for predicting liquefaction resistance. For this purpose, cone penetration numerical simulations and cyclic triaxial tests conducted on Ottawa sand-silt mixes at different fines content were used. Results obtained from ANN models were compared with simulation and experimental results and found close to them. In addition, the performance indices such as coefficient of determination, root mean square error, mean absolute error, and variance were used to check the prediction capacity of the ANN models developed. Both ANN models have shown a high prediction performance based on the performance indices. It has been demonstrated that the ANN models developed in this study can be employed for predicting cone penetration and liquefaction resistances of sand-silt mixes quite efficiently.

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“…In the agricultural sciences, several studies have used ANNs (Zhang, Bai, & Liu, 2007;Alvarez, 2009;Gago, Martínez-Núñez, Landín, & Gallego, 2010;Erzin, Rao, Patel, Gumaste, & Singh, 2010;Soares, Pasqual, Lacerda, Silva, & Donato, 2013;Safa, Samarasinghe, & Nejat, 2015;Alves et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

<b>Estimating soybean yields with artificial neural networks

et al. 2018

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ABSTRACT. The complexity of the statistical models used to estimate the productivity of many crops, including soybeans, restricts the use of this practice, but an alternative is the use of artificial neural networks (ANNs). This study aimed to estimate soybean productivity based on growth habit, sowing density and agronomic characteristics using an ANN multilayer perceptron (MLP). Agronomic data from experiments conducted during the 2013/2014 soybean harvest in Anápolis, Goiás State, B razil, were used to conduct this study after being normalized to an ANN-compatible range. Then, several ANNs were trained to choose the best-performing one. After training the network, a performance analysis was conducted to select the ANN with a performance most appropriate for the problem, and the selected network had a 98% success rate with training data and a 72% data validation accuracy. The application of the MLP to the data used in the experiment shows that it is possible to estimate soybean productivity based on agronomic characteristics, growth habit and population density through AI. Estimativa da produtividade de soja com redes neurais artificiais RESUMO. Para estimar a produtividade de muitas culturas, incluído a soja, são utilizados modelos estatísticos complexos, que torna restrito o acesso a essa prática. Uma alternativa a estes modelos é a utilização de sistemas computacionais empregando Redes Neurais Artificiais (RNA). Este trabalho teve por objetivo estimar a produtividade da soja baseada nos hábitos de crescimento, densidade de semeadura e características agronômicas usando RNA do tipo Multilayer Perceptron (MLP). Para isto foram utilizados dados agronômicos da cultura da soja obtidos em experimento conduzido na safra 2013/2014 em Anápolis, Estado de Goiás, Brasil, cujos dados foram normalizados em intervalo compatível para trabalho com RNA e em seguida feito o treinamento de várias RNAs para a escolha da RNA com melhor performance. Após o treinamento das redes, foi realizada a análise de performance para seleção da RNA com a performance mais adequada ao problema. A RNA selecionada apresentou um índice de acerto de 98% com os dados do treinamento e um acerto de 72% com dados de validação. A aplicação das RNAs do tipo MLP nos dados do experimento conduzido demonstram que é possível estimar a produtividade da soja baseando-se nas características agronômicas, hábito de crescimento e densidade populacional por meio da IA. Palavras-chave:Glycine max (L.) Merrill; características agronômicas; modelagem; MLP; perceptron.

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Artificial neural network models for predicting electrical resistivity of soils from their thermal resistivity

Cited by 67 publications

References 48 publications

The use of neural networks for the prediction of cone penetration resistance of silty sands

The use of neural networks for the prediction of cone penetration resistance of silty sands

The use of neural networks for CPT-based liquefaction screening

<b>Estimating soybean yields with artificial neural networks

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