RETRACTED: Application of adaptive neuro-fuzzy technique to determine the most relevant variables for daily soil temperature prediction at different depths

Mohammadi, Kasra; Shamshirband, Shahaboddin; Kamsin, Amirrudin; Yusof, Raja Jamilah Raja

doi:10.1016/j.catena.2016.06.008

Cited by 2 publications

(1 citation statement)

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“…The estimated correlations from some of these studies are listed in Table 1. Moreover, due to the power of fuzzy theory and the intelligent neural networks, and because of the wide range of their abilities in geotechnical engineering, they are used as the effective approaches for predicting geomaterial, mechanical, or physical properties (Ferentinou and Fakir 2017;Hajihassani et al 2015;Mohammadi et al 2016;Romo and Garcıá 2003;Sonmez et al 2006;Zahmatkesh et al 2017). Tsiambaos and Sabatakakis (2004) have shown their interest in driving a relationship between UCS and point load index for different sedimentary rocks using statistical analysis of a set of experimental data.…”

Section: Introductionmentioning

confidence: 99%

Development of a new empirical model and adaptive neuro-fuzzy inference systems in predicting unconfined compressive strength of weathered granite grade III

Moosavi

Mohammadi

2021

Bull Eng Geol Environ

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The present paper has developed a new multivariate linear regression and adaptive neuro-fuzzy inference processing to predict UCS for weathered granite grade III based on simple input data from point load index, Schmidt rebound hardness, and P wave velocity. Data from 85 rock core samples of this granite type have been selected. By using multivariate regression analysis, three models with two independent variables and one model with three independent variables have been developed. Furthermore, another model has been obtained by using a neuro-fuzzy logic analysis. The root means square error, RMSE, coefficient of determination, R 2 , and the mean absolute percentage error, MAPE, were used as the evaluation criteria of the accuracy of the models. The results have indicated that the regression-based and neuro-fuzzy models are effective, but the accuracy of the neurofuzzy model is in good agreement with the realistic data from the direct test.

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