COMPARING NONPARAMETRIC K-NEAREST NEIGHBOR TECHNIQUE WITH ANN MODEL FOR PREDICTING SOIL SATURATED HYDRAULIC CONDUCTIVITY

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KHASHEI SIUKI A.; JALALI MOAKHAR V.R.; NOFERESTI A.M.; RAMAZANI Y.

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Journal: Journal of Soil Management and Sustainable Production Year:2015 | Volume:5 | Issue:3 Page(s): 81-95

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Title

COMPARING NONPARAMETRIC K-NEAREST NEIGHBOR TECHNIQUE WITH ANN MODEL FOR PREDICTING SOIL SATURATED HYDRAULIC CONDUCTIVITY

Author(s)

KHASHEI SIUKI A. | JALALI MOAKHAR V.R. | NOFERESTI A.M. | RAMAZANI Y. | Issue Writer Certificate

Keywords

ARTIFICIAL NEURAL NETWORKQ2

K-NEAREST NEIGHBOR TECHNIQUEQ2

SOIL SATURATED HYDRAULIC CONDUCTIVITYQ2

Abstract

Background and Objectives: SOIL SATURATED HYDRAULIC CONDUCTIVITY is the most important physical parameter, but its measurement often is difficult because of practical and/or costrelated reasons. In this research, expert system approaches with one type of the nonparametric lazy learning algorithms, a k-nearest neighbor (k-NN) algorithm, was compared and tested to estimate saturated hydraulic conductivity (Ks) from other easily available soil properties.Materials and Methods: In this research 151 soil samples were collected from farmlands around Bojnourd and saturated hydraulic conductivity (Ks) was estimated from other soil properties including soil textural fractions, EC, pH, SP, OC, TNV, rs and rb. To evaluate ARTIFICIAL NEURAL NETWORK systems all the data were divided into 3 parts, 50% for training, 25% were allocated for the test and other data for validation. Design of MLP models (Multilayer Perceptron) was performed by sigmoid functions and hidden layer. ANN for all models was selected with Levenberg-Marcoardet algorithm to training as a hidden layer threshold logsig function for hidden layer and tansig for output layer.Results: Results showed that the accuracy of the parameter estimation, using parametric method of ARTIFICIAL NEURAL NETWORK to compare with k-nearest neighbors for terms of all the parameters (with r=0.97, EF=0.946, RMSE=8.798, ME=28.446 and CRM=-0.144) compared to other methods input models was acceptable.Conclusion: The results showed that different techniques have estimated, hydraulic conductivity coefficient partially. The non-parametric method k-nearest neighbor, focus on the estimation of the regression line 1: 1 has been studied more than the other methods. The best result was for ARTIFICIAL NEURAL NETWORK method with all information bank. Performance index knearest neighbor method (EF=57 to 71%), is powerful indication of this technique for estimation of soil hydraulic conductivity based on other easily available parameters, including particle size distribution, soil saturation extract, electrical conductivity (EC), saturation percentage of soil (SP), the organic carbon (OC), total of neutralizing value (TNV), bulk and apparent specific density of soil. ANN method can be used to estimate saturated hydraulic conductivity especially when new data set available.

Cites

ESTIMATION OF SATURATED HYDRAULIC CONDUCTIVITY IN SOME SOILS OF ILAM PROVINCE USING ARTIFICIAL NEURAL NETWORKS AND REGRESSION METHODS

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APA: Copy

KHASHEI SIUKI, A., JALALI MOAKHAR, V.R., NOFERESTI, A.M., & RAMAZANI, Y.. (2015). COMPARING NONPARAMETRIC K-NEAREST NEIGHBOR TECHNIQUE WITH ANN MODEL FOR PREDICTING SOIL SATURATED HYDRAULIC CONDUCTIVITY. ELECTRONIC JOURNAL OF SOIL MANAGEMENT AND SUSTAINABLE PRODUCTION, 5(3), 81-95. SID. https://sid.ir/paper/209798/en

Vancouver: Copy

KHASHEI SIUKI A., JALALI MOAKHAR V.R., NOFERESTI A.M., RAMAZANI Y.. COMPARING NONPARAMETRIC K-NEAREST NEIGHBOR TECHNIQUE WITH ANN MODEL FOR PREDICTING SOIL SATURATED HYDRAULIC CONDUCTIVITY. ELECTRONIC JOURNAL OF SOIL MANAGEMENT AND SUSTAINABLE PRODUCTION[Internet]. 2015;5(3):81-95. Available from: https://sid.ir/paper/209798/en

IEEE: Copy

A. KHASHEI SIUKI, V.R. JALALI MOAKHAR, A.M. NOFERESTI, and Y. RAMAZANI, “COMPARING NONPARAMETRIC K-NEAREST NEIGHBOR TECHNIQUE WITH ANN MODEL FOR PREDICTING SOIL SATURATED HYDRAULIC CONDUCTIVITY,” ELECTRONIC JOURNAL OF SOIL MANAGEMENT AND SUSTAINABLE PRODUCTION, vol. 5, no. 3, pp. 81–95, 2015, [Online]. Available: https://sid.ir/paper/209798/en

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