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  11. A Novel Early Stage Drip Irrigation System Cost Estimation Model Based On Management And Environmental Variables

A novel early stage drip irrigation system cost estimation model based on management and environmental variables

Masoud Pourgholam-Amiji1, Khaled Ahmadaali2, Abdolmajid Liaghat1

  • 1Department of Irrigation and Reclamation Engineering, Faculty of Agriculture, College of Agriculture and Natural Resources, University of Tehran, P. O. Box 4111, Karaj, 31587-77871, Iran.

Scientific Reports
|February 3, 2025

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View abstract on PubMed

Summary
This summary is machine-generated.

This study developed machine learning models to accurately estimate early-stage drip irrigation system costs. The best models, Support Vector Machine (SVM) and Artificial Neural Network (ANN), utilize environmental and management features for precise cost prediction.

Area of Science:

  • Agricultural Engineering
  • Environmental Science
  • Data Science

Background:

  • Pressurized irrigation systems, particularly drip irrigation, are crucial for water management.
  • Accurate early-stage cost estimation for these systems is complex and underexplored.
  • Understanding cost drivers is essential for project planning and investment.

Purpose of the Study:

  • To develop and validate machine learning models for early-stage cost estimation of drip irrigation systems.
  • To identify key environmental and management features influencing system costs.
  • To compare the performance of various feature selection and machine learning algorithms.

Main Methods:

  • A database of 515 drip irrigation projects was compiled, including 39 environmental and management features.
Keywords:
Cost ModelingFeature SelectionLocalized IrrigationMachine Learning

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  • Feature selection algorithms (e.g., LCA, FOA, Wrapper) were employed to identify significant cost-influencing factors.
  • Machine learning models, including Support Vector Machine (SVM) and Artificial Neural Network (ANN), were trained and tested.
  • Model performance was evaluated using metrics such as R-squared (R²) and Root Mean Squared Error (RMSE).

    • Main Results:

    • The LCA and FOA feature selection algorithms demonstrated excellent estimation performance (R² ≈ 0.94, RMSE ≈ 0.002).
    • For readily available features, these algorithms achieved R² of 0.95 and RMSE of 0.0006.
    • The SVM model (RBF Kernel) proved most effective for overall cost estimation (R² ≈ 0.89-0.92).
    • The ANN model (MLP) performed best for readily available features (R² ≈ 0.88-0.91).

    Conclusions:

    • Machine learning models, particularly SVM and ANN, can accurately predict early-stage drip irrigation costs.
    • Environmental and management features are significant predictors of system costs.
    • The developed models provide a valuable tool for accurate cost estimation in irrigation projects.
    Readily Available Features