Study on Water Resource Carrying Capacity and Crop Structure Optimization Based on Gray Relational Analysis

Area of Science:

• Agricultural Water Management
• Hydrology
• Machine Learning Applications

Background:

• Mixed-cropping areas in the Inner Mongolia Yellow River Basin face critical water resource challenges, including insufficient irrigation quotas and groundwater over-extraction.
• Existing water resource utilization faces conflicts due to demand-supply imbalances and inefficient practices.

Purpose of the Study:

• To assess the 2023 water resource carrying capacity in the region using gray relational analysis.
• To develop a machine learning-based crop structure optimization model to minimize water use and maximize economic benefits.

Main Methods:

• Gray relational analysis was employed, evaluating five key factors: water resource utilization efficiency, irrigation rate, degree of development and utilization, supply modulus, and demand modulus.
• A machine learning model was developed to optimize crop structures for water conservation and economic gain.

Main Results:

• Groundwater resources are critically depleted, with many areas exhibiting low water carrying capacity and significant supply-demand conflicts.
• Unreasonable planting structures and excessive irrigation quotas contribute to substantial water waste.

Conclusions:

• Recommended crop structure adjustments include reducing food crops and increasing economic and forage crops.
• Optimized structures can achieve significant annual water savings, increased yield, and enhanced economic benefits, promoting rational water resource allocation.