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Prediction of environmental indicators in land leveling using artificial intelligence techniques.

Isham Alzoubi1, Mahmoud R Delavar1, Farhad Mirzaei2

  • 11Department of Surveying and Geometric Engineering, Engineering Faculty, University of Tehran, Tehran, Iran.

Journal of Environmental Health Science & Engineering
|September 28, 2018
PubMed
Summary

Imperialist Competitive Algorithm-Artificial Neural Network (ICA-ANN) models accurately predict environmental indicators for land leveling, outperforming traditional methods. This approach optimizes fuel consumption and minimizes environmental impact during soil preparation.

Keywords:
ANFISArtificial neural networkEnergyEnvironmental researchImperialist Competitive Algorithm

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Area of Science:

  • Agricultural Engineering
  • Environmental Science
  • Computational Intelligence

Background:

  • Land leveling is crucial for soil preparation, but machine-based methods are energy-intensive.
  • Reducing fossil fuel consumption in land leveling is a key environmental concern.
  • Optimizing land leveling practices can minimize soil degradation and harm to soil organisms.

Purpose of the Study:

  • To develop and compare predictive models for environmental indicators in land leveling.
  • To identify the most effective modeling technique for optimizing energy consumption.
  • To investigate the influence of soil properties on fuel usage during land leveling.

Main Methods:

  • Utilized Artificial Neural Network (ANN), Imperialist Competitive Algorithm (ICA) combined with ANN (ICA-ANN), and Adaptive Neural Fuzzy Inference System (ANFIS).
  • Investigated the impact of soil properties including embankment volume, compressibility, specific gravity, moisture content, slope, sand percentage, and swelling index.
  • Collected 90 soil samples from three regions in Karaj province, Iran, with a grid size of 20m x 20m.

Main Results:

  • Sensitivity analysis revealed density, soil compressibility factor, and embankment volume index as significant factors influencing fuel consumption.
  • ICA-ANN models demonstrated superior prediction accuracy over standard ANN, evidenced by higher R-squared (R²) and lower Root Mean Square Error (RMSE) values.
  • ICA-ANN models achieved R² values of approximately 0.99 and RMSE values around 0.02, indicating high predictive performance.

Conclusions:

  • ICA-ANN integration offers enhanced prediction capabilities for land leveling environmental indicators compared to conventional methods.
  • Specific Multi-Layer Perceptron (MLP) network structures (e.g., 10-8-3-1) were identified as optimal.
  • The study highlights the potential of computational intelligence for optimizing agricultural practices and reducing environmental impact.