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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

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Published on: December 9, 2012

A robust multi-objective bargaining methodology for inter-basin water resource allocation: a case study.

Omid Nasiri-Gheidari¹, Safar Marofi², Farzaneh Adabi³

¹Department of Water Science Engineering, Bu-Ali Sina University, Hamedan, Iran.

Environmental Science and Pollution Research International

|November 15, 2017

Summary

This summary is machine-generated.

This study introduces a robust method for water allocation, balancing economic income and environmental pollution under uncertainty. Increased uncertainty reduces allocation and requires more negotiation rounds, highlighting the method's reliability for stakeholders.

Keywords:

Conflict analysis Inter-basin water allocation Robust optimization Uncertainty Water quality

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Area of Science:

Environmental Science
Water Resource Management
Game Theory

Background:

Water allocation decisions often involve conflicting economic and environmental objectives.
Uncertainty in hydrological and demand data complicates optimal water resource management.
Existing models may not adequately capture stakeholder interactions and uncertainty.

Purpose of the Study:

To develop a novel methodology for balancing economic income and environmental pollution in water allocation under uncertainty.
To enhance a deterministic multi-objective bargaining methodology (DMOBM) into a robust multi-objective bargaining methodology (RMOBM).
To provide a framework for incorporating stakeholder behavior in water allocation decisions.

Main Methods:

Constructed a deterministic multi-objective bargaining methodology (DMOBM) with economic income (EI) and environmental pollution (EP) as objectives.
Enhanced DMOBM to a robust multi-objective bargaining methodology (RMOBM) to handle uncertainties.
Applied the developed model to a large-scale inter-basin water transfer case study.

Main Results:

Nash equilibrium yielded a narrow solution range, with convergence time increasing with uncertainty.
Higher uncertainty levels resulted in reduced water allocation for the receiving basin.
Economic income was less sensitive to uncertain parameters than the environmental objective function.

Conclusions:

The developed RMOBM provides a reliable framework for water allocation under uncertainty.
The methodology effectively incorporates the behavior of different stakeholders in decision-making.
The approach offers a robust solution for managing complex water resource systems facing variability.