PREDICTION OF CATION EXCHANGE CAPACITY USING ARTIFICIAL NEURAL NETWORK AND MULTIVARIATE REGRESSION IN KHEZRABAD REGION

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TAGHIZADEH MEHRJARDI R.; MAHMOODI SH.; HEIDARI A.; AKBARZADEH A.

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Journal: JOURNAL OF RESEARCH IN AGRICULTURAL SCIENCE Year:2009 | Volume:5 | Issue:1 Page(s): 1-11

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Title

PREDICTION OF CATION EXCHANGE CAPACITY USING ARTIFICIAL NEURAL NETWORK AND MULTIVARIATE REGRESSION IN KHEZRABAD REGION

Author(s)

TAGHIZADEH MEHRJARDI R. | MAHMOODI SH. | HEIDARI A. | AKBARZADEH A. | Issue Writer Certificate

Keywords

ARTIFICIAL NEURAL NETWORKQ2

PEDOTRANSFER FUNCTIONSQ3

MULTIVARIATE REGRESSIONQ3

ARIDISOLSQ3

CATION EXCHANGE CAPACITYQ3

Abstract

Design and analysis of land-use management scenarios requires detail of a soil data bank including CEC data. Although CEC can be measured directly, its measurement is especially difficult and expensive in the ARIDISOLS of Iran because of the large amounts of calcium carbonate. PEDOTRANSFER FUNCTIONS (PTFs) provide alternative methods by estimating CEC from more readily available soil data.Soil samples were taken from 12 pedons in Khesrabad, Yazd Province.Measured soil variables includ texture (determined by Bouyoucos hydrometer method), organic carbon (determined with using Walkely and Black rapid titration) and CEC (determined with or by using Bower method). Then, the ARTIFICIAL NEURAL NETWORK (ANN), MULTIVARIATE REGRESSION (MR) and several published PTFs were applied to predict CEC, using measurable characteristics of clay, sand, silt and organic carbon. The results showed that ANN method gave the best result followed by MR method and finally the PTFs. Regarding the inputs and coefficients of PTFs, other regression based models had different performance. Among these models, none of them had absolute performance. In conclusion, the result of this study showed that training has a massive effect to increase accuracy of model in one region.

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APA: Copy

TAGHIZADEH MEHRJARDI, R., MAHMOODI, SH., HEIDARI, A., & AKBARZADEH, A.. (2009). PREDICTION OF CATION EXCHANGE CAPACITY USING ARTIFICIAL NEURAL NETWORK AND MULTIVARIATE REGRESSION IN KHEZRABAD REGION. JOURNAL OF RESEARCH IN AGRICULTURAL SCIENCE, 5(1), 1-11. SID. https://sid.ir/paper/115258/en

Vancouver: Copy

TAGHIZADEH MEHRJARDI R., MAHMOODI SH., HEIDARI A., AKBARZADEH A.. PREDICTION OF CATION EXCHANGE CAPACITY USING ARTIFICIAL NEURAL NETWORK AND MULTIVARIATE REGRESSION IN KHEZRABAD REGION. JOURNAL OF RESEARCH IN AGRICULTURAL SCIENCE[Internet]. 2009;5(1):1-11. Available from: https://sid.ir/paper/115258/en

IEEE: Copy

R. TAGHIZADEH MEHRJARDI, SH. MAHMOODI, A. HEIDARI, and A. AKBARZADEH, “PREDICTION OF CATION EXCHANGE CAPACITY USING ARTIFICIAL NEURAL NETWORK AND MULTIVARIATE REGRESSION IN KHEZRABAD REGION,” JOURNAL OF RESEARCH IN AGRICULTURAL SCIENCE, vol. 5, no. 1, pp. 1–11, 2009, [Online]. Available: https://sid.ir/paper/115258/en

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