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Prediction of environmental indicators in land leveling using artificial intelligence techniques

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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.

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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

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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.

Funding

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

Author information

Authors and Affiliations

  1. Department of Surveying and Geometric Engineering, Engineering Faculty, University of Tehran, Tehran, Iran

    Isham Alzoubi & Mahmoud R. Delavar

  2. College of Agriculture and Natural Resources, University of Tehran, Tehran, Iran

    Farhad Mirzaei

  3. School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran

    Babak Nadjar Arrabi

Authors
  1. Isham Alzoubi
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  2. Mahmoud R. Delavar
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  3. Farhad Mirzaei
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  4. Babak Nadjar Arrabi
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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.

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Correspondence to Isham Alzoubi.

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Alzoubi, I., Delavar, M.R., Mirzaei, F. et al. Prediction of environmental indicators in land leveling using artificial intelligence techniques. J Environ Health Sci Engineer 16, 65–80 (2018). https://doi.org/10.1007/s40201-018-0297-3

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  • DOI: https://doi.org/10.1007/s40201-018-0297-3

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