An interval multi-objective fuzzy-interval credibility-constrained nonlinear programming model for balancing...

Area of Science:

• Environmental Science
• Water Resource Management
• Operations Research

Background:

• Uncertainty in water resource management poses challenges for agricultural and ecological needs.
• Existing models often struggle to address dual uncertainties (interval and fuzzy parameters) in water allocation.
• Spatial variability of ecological vegetation water requirements requires integrated assessment.

Purpose of the Study:

• To develop an interval multi-objective fuzzy-interval credibility-constrained nonlinear programming (IMFICNP) model for optimizing water allocation.
• To integrate spatial ecological vegetation water requirement (SEWR) estimation into the water allocation framework.
• To handle uncertainties and conflicting objectives in agricultural and ecological water management.

Main Methods:

• Utilized remote sensing (RS) and geographic information system (GIS) for spatial SEWR estimation.
• Formulated the IMFICNP model combining interval parameter programming, multi-objective programming, and fuzzy-interval credibility-constrained programming.
• Incorporated interval quadratic crop water production functions (IQCWPFs) to model crop yield response to irrigation.

Main Results:

• The IMFICNP model successfully generated optimal water allocation schemes under varying credibility levels.
• Higher credibility levels led to reduced water allocation and system benefits.
• SEWR effectively captured spatial heterogeneity, with water allocation influenced by planting area and economic value.

Conclusions:

• The IMFICNP model effectively balances conflicting objectives in water allocation under uncertainty.
• The approach provides a robust framework for managing agricultural and ecological water resources sustainably.
• Results offer a valuable basis for sustainable development in arid and semi-arid irrigation districts.