Prediction of Unsaturated Hydraulic Conductivity of Agricultural Soils Using Artificial Neural Network and c#

2016 ◽  
Vol 5 (4) ◽  
pp. 1-15 ◽  
Author(s):  
M. Al-Sulaiman ◽  
A. Aboukarima
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Hydraulic Conductivity ◽  
Agricultural Soils ◽  
Unsaturated Hydraulic Conductivity ◽  
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.

scholarly journals Predicting Hydraulic Conductivity (k) of Tropical Soils by using Artificial Neural Network (ANN)

2009 ◽  
Vol 1 (1) ◽  
pp. 1-6 ◽  
Author(s):  
Lim D.K.H ◽  
Kolay P.K.
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Hydraulic Conductivity ◽  
Conjugate Gradient ◽  
Field Tests ◽  
Tropical Soils ◽  
Training Algorithms ◽  
Marquardt Algorithm ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Hydraulic conductivity of tropical soils is very complex. Several hydraulic conductivity prediction methods have focused on laboratory and field tests, such as the Constant Head Test, Falling Head Test, Ring Infiltrometer, Instantaneous profile method and Test Basins. In the present study, Artificial Neural Network (ANN) has been used as a tool for predicting the hydraulic conductivity (k) of some tropical soils. ANN is potentially useful in situations where the underlying physical process relationships are not fully understood and well-suited in modeling dynamic systems on a real-time basis. The hydraulic conductivity of tropical soil can be predicted by using ANN, if the physical properties of the soil e.g., moisture content, specific gravity, void ratio etc. are known. This study demonstrates the comparison between the conventional estimation of k by using Shepard's equation for approximating k and the predicted k from ANN. A programme was written by using MATLAB 6.5.1 and eight different training algorithms, namely Resilient Backpropagation (rp), Levenberg-Marquardt algorithm (lm), Conjugate Gradient Polak-Ribiere algorithm (cgp), Scale Conjugate Gradient (scg), BFGS Quasi-Newton (bfg), Conjugate Gradient with Powell/Beale Restarts (cgb), Fletcher-Powell Conjugate Gradient (cgf), and One-step Secant (oss) have been compared to produce the best prediction of k. The result shows that the network trained with Resilient Backpropagation (rp) consistently produces the most accurate results with a value of R = 0.8493 and E2 = 0.7209.

Autofluorescence spectroscopy of oral leukoplakia using an artificial neural network

2000 ◽  
Vol 25 (4) ◽  
pp. 325-325
Author(s):  
J.L.N. Roodenburg ◽  
H.J. Van Staveren ◽  
N.L.P. Van Veen ◽  
O.C. Speelman ◽  
J.M. Nauta ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Oral Leukoplakia ◽  
Autofluorescence Spectroscopy ◽  
Artificial Neural
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