The comparison of multiple linear regression and artificial neural network methods for analyzing the effect of internal and external factors on the performance of micro and small enterprises

2015 ◽  
pp. 339-347
Author(s):  
Adi Kuswanto
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
External Factors ◽  
Small Enterprises ◽  
Network Methods ◽  
Micro And Small Enterprises ◽  
Artificial Neural ◽  
Internal And External Factors

Spatial modeling of soil salinity using multiple linear regression, ordinary kriging and artificial neural network methods

2016 ◽  
Vol 63 (2) ◽  
pp. 151-160 ◽  
Author(s):  
Mahmoud Shahabi ◽  
Ali Asghar Jafarzadeh ◽  
Mohammad Reza Neyshabouri ◽  
Mohammad Ali Ghorbani ◽  
Khalil Valizadeh Kamran
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Soil Salinity ◽  
Spatial Modeling ◽  
Ordinary Kriging ◽  
Network Methods ◽  
Artificial Neural

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

2021 ◽  
Vol 3 (2) ◽  
Author(s):  
Charles Gbenga Williams ◽  
Oluwapelumi O. Ojuri
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Hydraulic Conductivity ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Coarse Grained ◽  
Soil Types ◽  
Wide Range ◽  
Artificial Neural ◽  
Input Variables

AbstractAs 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.

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