Estimating the Soil Saturated Hydraulic Conductivity in Ardabil Plain Soils Using Artificial Neural Networks and Regression Models

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Amirabedi Hamed; Asghari Shokrollah; Mesri Gandoshmin Tarahhom; Balandeh Naser; Johari Ebrahim

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Journal: APPLIED SOIL RESEARCH Year:2020 | Volume:7 | Issue:4 Page(s): 124-136

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Title

Estimating the Soil Saturated Hydraulic Conductivity in Ardabil Plain Soils Using Artificial Neural Networks and Regression Models

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Falling headQ2

Independent ParametersQ2

Pedotransfer functionQ2

Stepwise RegressionsQ2

Abstract

Soil saturated hydraulic conductivity (Ks) can be estimated from surrogate data such as soil texture, bulk density and organic carbon and CaCO3 contents using regression (Reg-PTFs) and artificial neural networks (ANN-PTFs) Pedotransfer function (PTF). Saturated hydraulic conductivity was measured by Falling head method in 100 soil samples that obtained from Ardabil plain, Iran. After performing physicals and chemicals analysis on soil samples, the data were divided into two sets of training (80 samples) and validation data (20 samples). Regression models were created by SPSS software, stepwise method and neural networks models were created by Neurosolution software. Statistics criteria such as coefficient of determination (R2), root mean square deviation (RMSE) and Akaike information Criterion (AIC) were determined. Input variables in the best regression models were sand, silt and bulk density. The best neural network models were obtained from the input variables that include bulk density, geometric mean and standard deviation of soil particle size distribution. The values for R2 and RMSE in training and testing data set for the Reg-PTF were 0. 53, 0. 074 and 0. 51, 0. 052 and for the ANN-PTF they were 0. 84, 0. 04 and 0. 73, 0. 06, respectively. In this research all independent variables such as bulk density, particle density, CaCO3, geometric mean and standard deviation of the particle size distribution included as inputs for development of Reg-PTFs and ANN-PTFs. The amount of R2 and RMSE for training and testing data set equal 0. 87, 0. 036 and 0. 58, 0. 076, respectively. Results showed that the ANN-PTF (R2= 0. 84) performs better than the Reg-PTF (R2= 0. 53) in this case. It was also found that when all independent variables were used as inputs in the neural ANN-PTF the values of R2 and RMSE (0. 87 and 0. 036) have been improved in the training data set...

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APA: Copy

Amirabedi, Hamed, Asghari, Shokrollah, Mesri Gandoshmin, Tarahhom, Balandeh, Naser, & Johari, Ebrahim. (2020). Estimating the Soil Saturated Hydraulic Conductivity in Ardabil Plain Soils Using Artificial Neural Networks and Regression Models. APPLIED SOIL RESEARCH, 7(4 ), 124-136. SID. https://sid.ir/paper/391038/en

Vancouver: Copy

Amirabedi Hamed, Asghari Shokrollah, Mesri Gandoshmin Tarahhom, Balandeh Naser, Johari Ebrahim. Estimating the Soil Saturated Hydraulic Conductivity in Ardabil Plain Soils Using Artificial Neural Networks and Regression Models. APPLIED SOIL RESEARCH[Internet]. 2020;7(4 ):124-136. Available from: https://sid.ir/paper/391038/en

IEEE: Copy

Hamed Amirabedi, Shokrollah Asghari, Tarahhom Mesri Gandoshmin, Naser Balandeh, and Ebrahim Johari, “Estimating the Soil Saturated Hydraulic Conductivity in Ardabil Plain Soils Using Artificial Neural Networks and Regression Models,” APPLIED SOIL RESEARCH, vol. 7, no. 4 , pp. 124–136, 2020, [Online]. Available: https://sid.ir/paper/391038/en

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