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Predictive modelling of soils’ hydraulic conductivity using artificial neural network and multiple linear regression

Williams, CG and Ojuri, OO (2021) Predictive modelling of soils’ hydraulic conductivity using artificial neural network and multiple linear regression. SN Applied Sciences, 3 (2). ISSN 2523-3963

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Abstract

As a result of heterogeneity nature of soils and variation in its hydraulic conductivity over several orders of magnitude for various soil types from fine-grained to coarse-grained soils, predictive methods to estimate hydraulic conductivity of soils from properties considered more easily obtainable have now been given an appropriate consideration. This study evaluates the performance of artificial neural network (ANN) being one of the popular computational intelligence techniques in predicting hydraulic conductivity of wide range of soil types and compared with the traditional multiple linear regression (MLR). ANN and MLR models were developed using six input variables. Results revealed that only three input variables were statistically significant in MLR model development. Performance evaluations of the developed models using determination coefficient and mean square error show that the prediction capability of ANN is far better than MLR. In addition, comparative study with available existing models shows that the developed ANN and MLR in this study performed relatively better.

Item Type: Article
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
T Technology > TC Hydraulic engineering. Ocean engineering
Divisions: Civil Engineering & Built Environment
Publisher: Springer Science and Business Media LLC
Date Deposited: 21 Jul 2021 09:36
Last Modified: 21 Jul 2021 09:36
DOI or Identification number: 10.1007/s42452-020-03974-7
URI: https://researchonline.ljmu.ac.uk/id/eprint/15309

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