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105113

PREDICTING ENGINEERING FACTORS RELATED TO SOIL AMENDMENTS USING NEURAL NETWORKS

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Last updated: 26 Dec 2024

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Agricultural Irrigation and Drainage Engineering

Abstract

Artificial Neural Network model is used to predict engineering factors [bulk density, hydraulic conductivity, infiltration rate, soil penetration resistance and available water], under three different soil amendments [Bitumen Emulsion, Polyacrylamide and Organic Manure]. Multilayer feedforward ANN with 11 input and 5 output neurons was trained using a backpropagation learning algorithm. The data needed to train and test the ANN model was obtained from previous literatures. The inputs were soil amendments [Bitumen Emulsion (BE), Polyacrylamide (PAM) and Organic Manure (OM)], soil texture [Sand (S), Silt (Si) and Clay (C)], engineering factors [Initial bulk density (IBd), Initial hydraulic conductivity (IKa), Initial infiltration rate (IIr), Initial Soil penetration resistance (ISp) and  Initial available water (IAW)]. The outputs were final Bd, Ka, Ir, Sp and AW.The result of Artificial Neural Network model showed that the variations between measured and predicted engineering factors were very small. A field experiment was carried out in the Agricultural Experiment Station of the Desert Research Center at Ras Sidr (رأس سدر), South Sinai Governorate. The experiment studied the effect of three soil amendments on some engineering factors, productivity of sorghum yield and water use efficiency. The results showed that the soil amendments improved the engineering factors in general. Optimum values for productivity and water use efficiency were obtained by applying Organic Manure 23128 Mg/fed and 26.6 kg/m3 respectively. Least values were obtained by applying Polyacrylamide 15559 Mg/fed and 13.14 kg/m3 respectively.

DOI

10.21608/mjae.2010.105113

Authors

First Name

D.M.

Last Name

Boulos

MiddleName

-

Affiliation

Assist. Res., Soil Phys., Desert Res. Center, Egypt.

Email

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City

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Orcid

-

First Name

M.N.

Last Name

EL Awady

MiddleName

-

Affiliation

Prof.,Emt., Agric. Eng.; Fac. of Agric. Ain Shams Univ., Egypt.

Email

-

City

-

Orcid

-

First Name

A.

Last Name

Tawfic

MiddleName

-

Affiliation

Prof.,Emt.; Soil Phys., Desert Res. Center, Egypt.

Email

-

City

-

Orcid

-

First Name

M.Z.

Last Name

El Attar

MiddleName

-

Affiliation

Assist. Prof. Agric. Eng.; Fac. of Agric. Ain Shams Univ., Egypt.

Email

-

City

-

Orcid

-

Volume

27

Article Issue

4

Related Issue

15841

Issue Date

2010-10-01

Receive Date

2020-07-26

Publish Date

2010-10-01

Page Start

1,721

Page End

1,735

Print ISSN

1687-384X

Online ISSN

2636-3062

Article File

MJAE_Volume 27_Issue 4_Pages 1721-1735.pdf

PDF . 1.0MB

Link

https://mjae.journals.ekb.eg/article_105113.html

Detail API

https://mjae.journals.ekb.eg/service?article_code=105113

Order

44

Type

Original Article

Type Code

1,326

Publication Type

Journal

Publication Title

Misr Journal of Agricultural Engineering

Publication Link

https://mjae.journals.ekb.eg/

MainTitle

PREDICTING ENGINEERING FACTORS RELATED TO SOIL AMENDMENTS USING NEURAL NETWORKS

Details

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Article

Created At

23 Jan 2023