Determining optimal combination of input variables using gamma test for absorbable potassium modeling in the fuzzy-neural system (case study: Mian-Kangi region Zabol)

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Delarami Amin; Gholamalizadeh Ahmad; SHABANI ASMA

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Journal: JOURNAL OF CROP SCIENCE RESEARCH IN ARID REGIONS Year:2020 | Volume:2 | Issue:1 Page(s): 67-74

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Title

Determining optimal combination of input variables using gamma test for absorbable potassium modeling in the fuzzy-neural system (case study: Mian-Kangi region Zabol)

Author(s)

Delarami Amin | Gholamalizadeh Ahmad | SHABANI ASMA | Issue Writer Certificate

Keywords

Available potassium

Fast measured properties

Fuzzy neural

Gamma test

M test

Abstract

One of the important and complex steps for nonlinear modeling is pre-processing of input data in order to select the appropriate combination of them in the model. The Gamma test was used to select the optimal combination of input variables for Available potassium modeling in this study. The M test was used for determining the optimal number of data needed for modeling. Eight input variables were used for modeling. Modeling the Available potassium was done by the number of optimum points and selected variables with subtractive clustering in the Fuzzy neural system. The results showed that six variables (clay percentage, silt, organic matter, electrical conductivity, saturation moisture and pH) are the optimal combination of variables in modeling the Available potassium in Mian-Kangi region. Also, 112 of measured data (60%) were considered as suitable data for the modeling training section using the M test results. The results indicated that the M method has better accuracy and speed than the trial and error method for finding the appropriate number of input data in training section. The results of modeling also indicated that the Fuzzy neural method has high capability and performance in estimating the amount of Available potassium in the soil of Mian-Kangi region (R2 = 0. 90 and RMSE = 4. 27). Also, organic carbon percentage was the most important input for modeling and predicting the amount of Available potassium.

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APA: Copy

Delarami, Amin, Gholamalizadeh, Ahmad, & SHABANI, ASMA. (2020). Determining optimal combination of input variables using gamma test for absorbable potassium modeling in the fuzzy-neural system (case study: Mian-Kangi region Zabol). JOURNAL OF CROP SCIENCE RESEARCH IN ARID REGIONS, 2(1 ), 67-74. SID. https://sid.ir/paper/373217/en

Vancouver: Copy

Delarami Amin, Gholamalizadeh Ahmad, SHABANI ASMA. Determining optimal combination of input variables using gamma test for absorbable potassium modeling in the fuzzy-neural system (case study: Mian-Kangi region Zabol). JOURNAL OF CROP SCIENCE RESEARCH IN ARID REGIONS[Internet]. 2020;2(1 ):67-74. Available from: https://sid.ir/paper/373217/en

IEEE: Copy

Amin Delarami, Ahmad Gholamalizadeh, and ASMA SHABANI, “Determining optimal combination of input variables using gamma test for absorbable potassium modeling in the fuzzy-neural system (case study: Mian-Kangi region Zabol),” JOURNAL OF CROP SCIENCE RESEARCH IN ARID REGIONS, vol. 2, no. 1 , pp. 67–74, 2020, [Online]. Available: https://sid.ir/paper/373217/en

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