Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

A Multiobjective Mathematical Model for Optimal Irrigation Water Allocation

Nahid Sultana, M. M. Rizvi, G. M. Wali Ullah

TL;DR

This paper develops a multiobjective optimization framework for irrigation water allocation that balances economic returns with environmental flow protection. By formulating two single-objective models, $NB_{\max}$ and $EFD_{\min}$, and a multiobjective extension, it expands the decision space to jointly optimize crop area and environmental releases, evaluated on the Muhuri Irrigation Project. The study demonstrates clear economic–ecological trade-offs, with a scalarization-based approach delivering nearly $10^3$ Pareto points in seconds and outperforming prior efforts in both coverage and speed. The framework offers a practical, transferable decision-support tool for regulated irrigation systems under scarcity, while outlining enhancements such as seasonality, uncertainty, and richer hydrologic dynamics for future work.

Abstract

Sustainable irrigation and food security increasingly depend on efficient water resource management in the face of growing climatic and economic constraints. In this study, we develop two single objective optimization models for irrigation planning, one that maximizes net benefit and the other that minimizes environmental flow deficiency, and compare their performance with established models reported in previous studies. We then extend the analysis to a multiobjective programming formulation solved through scalarization and genetic approaches to evaluate trade-offs. Numerical experiments on the Muhuri Irrigation Project reveal three outcomes: (i) a complete scenario view with profits ranging from $ \$ 0.2 \times 10^9$ to $ \$ 1.497\times 10^9$ and environmental flow deficits between 0 and 1200 GL, where the 1200 GL represents the theoretical annual maximum under a 100 GL uniform monthly target; (ii) explicit trade-offs showing higher profits correspond to greater ecological shortfalls; and (iii) an integration based approach producing nearly 1000 Pareto optimal solutions within seconds, greatly outperforming earlier studies.

A Multiobjective Mathematical Model for Optimal Irrigation Water Allocation

TL;DR

This paper develops a multiobjective optimization framework for irrigation water allocation that balances economic returns with environmental flow protection. By formulating two single-objective models, and , and a multiobjective extension, it expands the decision space to jointly optimize crop area and environmental releases, evaluated on the Muhuri Irrigation Project. The study demonstrates clear economic–ecological trade-offs, with a scalarization-based approach delivering nearly Pareto points in seconds and outperforming prior efforts in both coverage and speed. The framework offers a practical, transferable decision-support tool for regulated irrigation systems under scarcity, while outlining enhancements such as seasonality, uncertainty, and richer hydrologic dynamics for future work.

Abstract

Sustainable irrigation and food security increasingly depend on efficient water resource management in the face of growing climatic and economic constraints. In this study, we develop two single objective optimization models for irrigation planning, one that maximizes net benefit and the other that minimizes environmental flow deficiency, and compare their performance with established models reported in previous studies. We then extend the analysis to a multiobjective programming formulation solved through scalarization and genetic approaches to evaluate trade-offs. Numerical experiments on the Muhuri Irrigation Project reveal three outcomes: (i) a complete scenario view with profits ranging from 0.2 \times 10^9 \ and environmental flow deficits between 0 and 1200 GL, where the 1200 GL represents the theoretical annual maximum under a 100 GL uniform monthly target; (ii) explicit trade-offs showing higher profits correspond to greater ecological shortfalls; and (iii) an integration based approach producing nearly 1000 Pareto optimal solutions within seconds, greatly outperforming earlier studies.

Paper Structure

This paper contains 12 sections, 10 equations, 1 figure, 4 tables.

Table of Contents

  1. Introduction
  2. Decision and Managerial Insights
  3. Literature Review
  4. Problem Statement
  5. Formulations of the Mathematical Models and Solution Approach
  6. Numerical Experiments, Results and Sensitivity Analysis
  7. Multiobjective Optimization Problems
  8. Conclusion
  9. Funding and/or Conflicts of interests/Competing interests
  10. Appendix
  11. Solution procedure
  12. Algorithm

Figures (1)

  • Figure 1: Pareto fronts generated by the 'gamultiobj', 'paretosearch', and 'weighted-constraint' ($P_w^k$) methods.