COMPARISON OF K-NEAREST NEIGHBOR AND ARTIFICIAL NEURAL NETWORK METHODS FOR PREDICTING CATION EXCHANGE CAPACITY OF SOIL

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ZOLFAGHARI A.A.; TIRGAR SOLTANI M.T.; AFSHARI BADRLOO T.; SARMADIAN F.

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Journal: Journal of Soil Management and Sustainable Production Year:2013 | Volume:3 | Issue:1 Page(s): 77-94

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Title

COMPARISON OF K-NEAREST NEIGHBOR AND ARTIFICIAL NEURAL NETWORK METHODS FOR PREDICTING CATION EXCHANGE CAPACITY OF SOIL

Author(s)

ZOLFAGHARI A.A. | TIRGAR SOLTANI M.T. | AFSHARI BADRLOO T. | SARMADIAN F. | Issue Writer Certificate

Keywords

NONPARAMETRIC MODELSQ2

CATION EXCHANGE CAPACITYQ2

K-NEAREST NEIGHBORQ2

ARTIFICIAL NEURAL NETWORK MODELQ2

Abstract

Cation exchange capacity (CEC) measurement is a very expensive and timeconsuming method in large scale assessments. It can be an appropriate approach to predict CEC from readily available properties via developing NONPARAMETRIC MODELS. In the present study, a nonparametric technique has been used for estimating CEC and compared with the most common NONPARAMETRIC MODELS which is based on artificial neural networks (ANN).683 soils were selected from central Iran that 120 of them were used as target data and the others (563) were the reference data set. The parameters, clay, silt, sand and organic carbon content were the input independent variables (readily available properties) and the CEC was as an output dependent variable in this work. The results showed that the maximum error (MaxE) in K-NN and ANN techniques were 4.81 and 5.26 cmol+/kg, respectively. Root mean squared error (RMSE) for the K-NN and ANN were 1.51 and 1.53, respectively. This indicated that both methods are able to properly and equally predict CEC. The positive values of mean error (ME) showed that both models tended to underestimate CEC values in samples. The results analysis also showed that the efficiency of models (EF=0.88) were high by the estimation of CEC values in target soils.

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APA: Copy

ZOLFAGHARI, A.A., TIRGAR SOLTANI, M.T., AFSHARI BADRLOO, T., & SARMADIAN, F.. (2013). COMPARISON OF K-NEAREST NEIGHBOR AND ARTIFICIAL NEURAL NETWORK METHODS FOR PREDICTING CATION EXCHANGE CAPACITY OF SOIL. ELECTRONIC JOURNAL OF SOIL MANAGEMENT AND SUSTAINABLE PRODUCTION, 3(1), 77-94. SID. https://sid.ir/paper/209852/en

Vancouver: Copy

ZOLFAGHARI A.A., TIRGAR SOLTANI M.T., AFSHARI BADRLOO T., SARMADIAN F.. COMPARISON OF K-NEAREST NEIGHBOR AND ARTIFICIAL NEURAL NETWORK METHODS FOR PREDICTING CATION EXCHANGE CAPACITY OF SOIL. ELECTRONIC JOURNAL OF SOIL MANAGEMENT AND SUSTAINABLE PRODUCTION[Internet]. 2013;3(1):77-94. Available from: https://sid.ir/paper/209852/en

IEEE: Copy

A.A. ZOLFAGHARI, M.T. TIRGAR SOLTANI, T. AFSHARI BADRLOO, and F. SARMADIAN, “COMPARISON OF K-NEAREST NEIGHBOR AND ARTIFICIAL NEURAL NETWORK METHODS FOR PREDICTING CATION EXCHANGE CAPACITY OF SOIL,” ELECTRONIC JOURNAL OF SOIL MANAGEMENT AND SUSTAINABLE PRODUCTION, vol. 3, no. 1, pp. 77–94, 2013, [Online]. Available: https://sid.ir/paper/209852/en

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