Evaluation of Water Resources Carrying Capacity and Analysis of Influencing Factors in China’s Major Grain-Producing Areas Based on Machine Learning

Abstract

Evaluating regional water resource carrying capacity (WRCC) helps alleviate regional water supply–demand conflicts. This study constructed a 17-indicator system for evaluating WRCC in Major Grain-Producing Areas (MGPAs) based on the “production–living–ecology” functional perspective. It employed a combined Entropy Weight–Root Mean Square Deviation–CRITIC weighting approach with a BP neural network model to conduct a comprehensive assessment of WRCC across 13 MGPAs from 2004 to 2023. The results demonstrated the following: (1) Both MGPAs and the national level exhibit a “ecology dominance–living secondary–production weakness” pattern in functional weighting. (2) WRCC in MGPAs is characterized by agricultural production dominance, basic domestic needs as the core, and localized ecological protection as the focus—significantly differing from the national pattern of industrial-driven, economically interconnected, and trans-regional ecological concerns. (3) Spatiotemporally, WRCC levels across the 13 provinces have consistently increased, with a spatial distribution characterized by “higher in the north, lower in the south.” These findings reveal that water resource management in MGPAs requires strategies distinct from national approaches, emphasizing agricultural water conservation and efficiency alongside localized ecological protection. This provides precise policy tools and scientific decision support for implementing water-based production quotas and coordinating food security with water resource security in these regions.