PREDICTING ENGINEERING FACTORS RELATED TO SOIL AMENDMENTS USING NEURAL NETWORKS

Boulos, D.M.; EL Awady, M.N.; Tawfic, A.; El Attar, M.Z.

doi:10.21608/mjae.2010.105113

PREDICTING ENGINEERING FACTORS RELATED TO SOIL AMENDMENTS USING NEURAL NETWORKS

Document Type : Original Article

Authors

¹ Assist. Res., Soil Phys., Desert Res. Center, Egypt.

² Prof.,Emt., Agric. Eng.; Fac. of Agric. Ain Shams Univ., Egypt.

³ Prof.,Emt.; Soil Phys., Desert Res. Center, Egypt.

⁴ Assist. Prof. Agric. Eng.; Fac. of Agric. Ain Shams Univ., Egypt.

10.21608/mjae.2010.105113

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.

Main Subjects

Agricultural Irrigation and Drainage Engineering

References

Aboukarima, A.M.(2007). Draft models of chisel plow based on simulation using artificial neural networks. Misr J. Ag. Eng., 24(1): 42-61.

Akbarzadeh A.; R.T. Mehrjardi; H. Rouhipour; M. Gorji and H.G. Refahi (2009). Estimating of soil erosion covered with rolled erosion control systems using rainfall simulator (Neuro-fuzzy and Artificial Neural Network Approaches). J. of Ap. Sci. Res., 5(5): 505-514.

El Awady, M.N.; A.G. El-Kabany; M.H. Kabeel and A.M. Aboukarima (2003). Relative importance of variables affecting chisel plow performance using neural networks. The 11^th Conf. of Misr Soc. of Ag. Eng.: 395-407.

El Awady, M.N.; A.G. El-Kabany; M.H. Kabeel and A.M. Aboukarima (2004). Predicting unit draft of tillage implements using statistical models and neural networks. The 12^th Conf. of Misr Soc. of Ag. Eng.: 139-249.

Boulos, D.M. (2006). A study on some physical factors affecting improved irrigation of desertic soil in Ras Sidr. M. Sc. Th., Fac. of Agric., Ain Shams Univ., Egypt: 30-32.

El Kommos, F. and Y. Noor El-Din (1990). The effect of irrigation frequency under varying soil amendments application on wheat and broad beans production in sandy soils. Eg. J. Soil Sci., 30(3): 403-414.

Sorour, H.M. (2006). Prediction of wheat deterioration during storage period using artificial neural network. Misr J. Ag. Eng., 23(3): 637-657.

Tawfik, A.; S.Y. Metwally; M.A. Afifi and M.A. El-Toni (1992). Effect of soil conditioners on availability of soil moisture, soil strength and plant growth. Eg. J. Soil. Sci., 32(2): 181-195.