Mottaghian, H., Mohammadi, J., Karimi, A. (2016). Comparison of artificial neural network and regression pedotransfer functions for prediction of soil saturated hydraulic conductivity. , 29(1), 41-50. doi: 10.22092/wmej.2016.112222
Hamid Reza Mottaghian; Jahanghard Mohammadi; Ahmad Karimi. "Comparison of artificial neural network and regression pedotransfer functions for prediction of soil saturated hydraulic conductivity". , 29, 1, 2016, 41-50. doi: 10.22092/wmej.2016.112222
Mottaghian, H., Mohammadi, J., Karimi, A. (2016). 'Comparison of artificial neural network and regression pedotransfer functions for prediction of soil saturated hydraulic conductivity', , 29(1), pp. 41-50. doi: 10.22092/wmej.2016.112222
Mottaghian, H., Mohammadi, J., Karimi, A. Comparison of artificial neural network and regression pedotransfer functions for prediction of soil saturated hydraulic conductivity. , 2016; 29(1): 41-50. doi: 10.22092/wmej.2016.112222

Comparison of artificial neural network and regression pedotransfer functions for prediction of soil saturated hydraulic conductivity

پژوهش های آبخیزداری
Article 5, Volume 29, Issue 1 - Serial Number 110, April 2016, Pages 41-50    PDF (550.64 K)
Document Type: Research
DOI: 10.22092/wmej.2016.112222
Authors
Hamid Reza Mottaghian* 1; Jahanghard Mohammadi2; Ahmad Karimi3
1PhD student, Department of Soil Science, Faculty of Agriculture, Shahrekord University
2Associate Professor, Department of Soil Science, Faculty of Agriculture, Shahrekord University
3Assistant Professor, Department of Soil Science, Faculty of Agriculture, Shahrekord University
Abstract
Soil saturated hydraulic conductivity (Ks) is among the most important soil hydraulic-physical properties that required for soil-water modeling. Due to high cost and time- consuming nature of Ks measurement, estimating Ks from basic, inexpensive and easily measured physical and chemical soil properties is becoming increasingly important. In the last two decades, the development of estimation methods called pedotransfer functions that use cheap auxiliary variables has been a sharpening focus of soil research. This study was conducted (i) to develop different pedotransfer functions and (ii) to evaluate and compare statistical regression and neural network based pedotransfer functions for estimating Ks in a sub- catchment of Zayanderood River, located in Chaharmahal-va-Backtiari province. The data set was divided in to subsets for modeling (n=86) and validation (n=25). Root-mean-square error (RMSE), mean error (ME) and percentage of relative improvement (RI) were used as the validation indices. The artificial neural network-based models provided more reliable estimation than the statistical regression-based pedotransfer functions.
Keywords
Auxiliary variables; Pedotransfer functions; Validation indices
References
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