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Prediction of environmental indicators in land leveling using artificial intelligence techniques

  • Isham Alzoubi
  • Mahmoud R. Delavar
  • Farhad Mirzaei
  • Babak Nadjar Arrabi
Research Article
  • 18 Downloads

Abstract

Background

Land leveling is one of the most important steps in soil preparation and cultivation. Although land leveling with machines require considerable amount of energy, it delivers a suitable surface slope with minimal deterioration of the soil and damage to plants and other organisms in the soil. Notwithstanding, researchers during recent years have tried to reduce fossil fuel consumption and its deleterious side effects. The aim of this work was to determine best linear model using artificial neural network (ANN), imperialist competitive algorithm and ANN and regression and adaptive neural fuzzy inference system (ANFIS) in order to predict the environmental indicators for land leveling.

Methods

New techniques such as; ANN, imperialist competitive algorithm and ANN and sensitivity analysis and regression and ANFIS that will lead to a noticeable improvement in the environment. In this research effects of various soil properties such as embankment volume, soil compressibility factor, specific gravity, moisture content, slope, sand percent, and soil swelling index in energy consumption were investigated. The study was consisted of 90 samples were collected from 3 different regions. The grid size was set 20 m in 20 m (20 × 20) from a farmland in Karaj province of Iran.

Results

According to the results of sensitivity analysis, only three parameters; density, soil compressibility factor and, embankment volume index had significant effect on fuel consumption. In comparison with ANN, all ICA-ANN models had higher accuracy in prediction according to their higher R2 value and lower RMSE value. Statistical factors of RMSE and R2 illustrate the superiority of ICA-ANN over other methods by values about 0.02 and 0.99, respectively.

Conclusion

Results extracted and statistical analysis was performed and RMSE as well as coefficient of determination, R2, of the models were determined as a criterion to compare selected models. According to the results, 10–8–3-1, 10–8–2-5-1, 10–5–8-10-1, and 10–6–4-1 MLP network structures were chosen as the best arrangements and were trained using Levenberg-Marquet as NTF. Integrating ANN and imperialist competitive algorithm (ICA-ANN) had better performance in prediction of output parameters in comparison with conventional methods such.

Keywords

Artificial neural network Energy Environmental research Imperialist Competitive Algorithm ANFIS 

Abbreviations

ICAANN

Integrating Artificial Neural Network and Imperialist competitive algorithm

ANN

Artificial Neural Network.

LE

environmental indicators: Labor Energy

FE

environmental indicators: Fuel energy

TMC

Total Machinery Cost

TME

environmental indicators: Total Machinery Energy

Notes

Acknowledgments

We are thankful to our colleagues, the professors of Department of Surveying and Geometrics Engineering, Ph.D. students, and the Department of Surveying and Geometric Eng., Engineering Faculty, University of Tehran, Iran, who provided expertise that greatly assisted the research. The authors declare that there is no conflict of interests.

Authors’ contributions

AI carried out all studies about the work and cultivated the data which were necessary to be analyzed. DM helped in statistical analysis. MF participated in land leveling studies results acquisition. A.N B helped in design and studying of artificial neural network. All authors read manuscript and approved it.

Funding

All parts of this research have been supported by University of Tehran.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no competing interests.

Consent for publication

Not applicable.

Ethics approval and consent to participate

Not applicable.

References

  1. 1.
    Khan F, Khan SU, Sarir MS, Khattak RA. Effect of land leveling on some physico-chemical properties of soil in district dir lower. Shar J Agr. 2007;23:108–14.Google Scholar
  2. 2.
    Kaveh A, Talatahari S. Optimum design of skeletal structures using imperialist competitive algorithm. Comput Struct. 2010;1(88):1220–9.CrossRefGoogle Scholar
  3. 3.
    Cassel D, Wood M, Bunge RP, Classer L. Mitogenicity of brain axolemma membranes and soluble factors for dorsal rool ganglion schwann cells. J Cell Biochem. 1982;18:433–45.CrossRefGoogle Scholar
  4. 4.
    Rallo R, Ferre-Gin J, Arenas A, Giralt F. Neural virtual sensor for the inferential prediction of product quality from process variables. Comput Chem Eng. 2002;26:1735–54.CrossRefGoogle Scholar
  5. 5.
    Diamantopoulou MJ. Artificial neural networks as an alternative tool in pine bark volume estimation. Comput Electron Agric. 2005;2005(48):235–44.CrossRefGoogle Scholar
  6. 6.
    Movagharnejad K, Nikzad M. Modeling of tomato drying using artificial neural network. Comput Electron Agric. 2007;59:78–85.CrossRefGoogle Scholar
  7. 7.
    Nikoo MF, Ramezani M, Hadzima-Nyarko E, Nyarko K, Nikoo M. Flood-routing modeling with neural network optimized by social-based algorithm. Nat Hazards. 2016;82(1):1–24.CrossRefGoogle Scholar
  8. 8.
    Rajabioun R, Atashpaz-Gargari E, Lucas C. Colonial competitive algorithm as a tool for nash equilibrium point achievement. Comput Sci Appl ICCSA Springer Berlin Heidelberg. 2008;2008(83):680–95.Google Scholar
  9. 9.
    Ahmadi MA, Soleimani R, Bahadori A. A computational intelligence scheme for prediction equilibrium water dew point of natural gas in TEG dehydration systems. Fuel. 2014;2014(137):145–54.CrossRefGoogle Scholar
  10. 10.
    Ahmadi MA, Golshadi M. Neural network based swarm concept for prediction asphaltene precipitation due to natural depletion. J Pet Sci Eng. 2012;98:40–9.CrossRefGoogle Scholar
  11. 11.
    Ahmadi MA, Shadizadeh SR. New approach for prediction of asphaltene precipitation due to natural depletion by using evolutionary algorithm concept. Fuel. 2012;102:716–23.CrossRefGoogle Scholar
  12. 12.
    Brye KR, Slaton NA, Norman RJ. Soil physical and biological properties as affected by land leveling in a clayey Aquent. Soil Sci Soc Am J. 2006;70:631–42.CrossRefGoogle Scholar
  13. 13.
    Ahmadi MA, Bahadori A, Shadizadeh SR. A rigorous model to predict the amount of dissolved calcium carbonate concentration throughout oil field brines: side effect of pressure and temperature. Fuel. 2015;139:154–9.CrossRefGoogle Scholar
  14. 14.
    Jat ML, Gupta RK, Rodomiro RS. Diversifying the intensive cereal cropping systems of the Indo-Ganges through Horticulture. Chronica horticulturae. 2006;46:27–31.Google Scholar
  15. 15.
    McFarlane BL, Stump-Allen RCG, Watson DO. Public perceptions of natural disturbance in Canada’s national parks: the case of the mountain pine beetle (Dendroctonus ponderosa Hopkins). Biolo Con. 2006;130:340–8.CrossRefGoogle Scholar
  16. 16.
    Abdechiri M, Faez K, Bahrami H. Adaptive Imperialist Competitive Algorithm (AICA), Cognitive Informatics (ICCI). 9th IEEE international conference. 2010. p. 940–945.Google Scholar
  17. 17.
    Fereydooni M, Mansoori B. Simulation depth of bridge pier scouring using artificial neural network and adaptive neuro–fuzzy inference system. Indian J Fundam Appl Life Sci. 2015;5:2091–5.Google Scholar
  18. 18.
    Shakibai AR, Koochekzadeh S. Modeling and predicting agricultural energy consumption in Iran. Am-Eurasian J Agr Environ Science. 2009;2009(5):308–12.Google Scholar
  19. 19.
    Toro J, Requena I, Zamorano M. Environmental impact assessment in Colombia: critical analysis and proposals for improvement. Environ Impact Assess. 2010;30(4):247–61.CrossRefGoogle Scholar
  20. 20.
    Abdi B, Mozafari H, Ayob A, Kohandel R. Imperialist competitive algorithm and its application in optimization of laminated composite structures. Eur J ScI R. 2011;55(2):174–87.Google Scholar
  21. 21.
    Nikoo M, Ramezani F, Nyarko MH, Nyarko EK, Nikoo M. Flood-routing modeling with neural network optimized by social-based algorithm. Nat Hazards. 2016;82:1–24.CrossRefGoogle Scholar
  22. 22.
    Ahmadi MA, Ahmadi MR, Shadizadeh SR. Evolving artificial neural network and imperialist competitive algorithm for prediction permeability of the reservoir. Appl Soft Comput. 2013;23(13):1085–98.CrossRefGoogle Scholar
  23. 23.
    Lei K, Qiu Y, He Y. A new adaptive well-chosen inertia weight strategy to automatically harmonize global and local search ability in particle swarm optimization. Systems and Control in Aerospace and Astronautics, 2006. ISSCAA 2006. 1st International Symposium on, IEEE. 2006.Google Scholar
  24. 24.
    Moghaddam K, Far T. Laser land levelling as a strategy for environmental management: the case of Iran. Pollution. 2015;1(2):203–15.Google Scholar
  25. 25.
    Azadeh A, Ghaderi SF, Sohrabkhani S. Annual electricity consumption forecasting by neural network in high energy consuming industrial sectors. Energy Convers Manag. 2008;49:2272–8.CrossRefGoogle Scholar
  26. 26.
    Tiryaki B. Predicting intact rock strength for mechanical excavation using multivariate statistics, artificial neural networks, and regression trees. Eng Geol. 2008;2008(99):51–60.CrossRefGoogle Scholar
  27. 27.
    Talatahari S, Kavehand A, Sheikholeslami R. Chaotic imperialist competitive algorithm for optimum design of truss structures. Str Multidiscip O incl. 2012;46:355–67.CrossRefGoogle Scholar
  28. 28.
    Ebrahimzadeh A, Addeh J, Rahmani Z. Control chart pattern recognition using K-MICA clustering and neural networks. ISA Trans. 2012;51:111–9.CrossRefGoogle Scholar
  29. 29.
    Marto A, Hajihassani M, Armaghani D, Mohamad ED, Makhtar AM. A novel approach for blast-induced flyrock prediction based on imperialist competitive algorithm and artificial neural network. Hindawi Publishing Corp Scientific World J. 2014;15:1–11.Google Scholar
  30. 30.
    Taghavifar H, Mardani A, Taghavifar L. A hybridized artificial neural network and imperialist competitive algorithm optimization approach for prediction of soil compaction in soil bin facility. Measurement. 2014;46:2288–99.CrossRefGoogle Scholar
  31. 31.
    Toro J, Requena I, Zambrano M. Environmental impact assessment in Colombia: critical analysis and proposals for improvement. Environ Impact Asses. 2010;2010(30):247–61.CrossRefGoogle Scholar
  32. 32.
    Mohammadi A, Rafiee S, Keyhani A, Emam-Djomeh Z. Modelling of kiwifruit (cv. Hayward) slices drying using artificial neural network. 4th International conference on energy efficiency and agricultural engineering. Rousse, Bulgaria, 2009;1–3:397–404.Google Scholar
  33. 33.
    Okasha EM, Abdelraouf RE, Abode MAA. Effect of land leveling and water applied methods on yield and irrigation water use efficiency of maize (Zea mays L.) grown under clay soil conditions. World Appl Sci J. 2013;27:183–90.Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Isham Alzoubi
    • 1
  • Mahmoud R. Delavar
    • 1
  • Farhad Mirzaei
    • 2
  • Babak Nadjar Arrabi
    • 3
  1. 1.Department of Surveying and Geometric Engineering, Engineering FacultyUniversity of TehranTehranIran
  2. 2.College of Agriculture and Natural ResourcesUniversity of TehranTehranIran
  3. 3.School of Electrical and Computer Engineering, College of EngineeringUniversity of TehranTehranIran

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