The evaluation and diagnosis of water resources carrying capacity are crucial for optimizing regional water resources management. This study focuses on the Yishusi River Basin over the period 2008–2017, using a combined single-element and multi-element approach to assess the region’s water resources carrying capacity. Subjective weights were derived from a fuzzy analytic hierarchy process (FAHP) informed by a panel of 30 anonymous peer experts, and objective weights were derived from a fuzzy entropy weight method (FEWM) applied to the 2007–2017 odd-year official records. An improved Accelerated Genetic Algorithm (AGA) was used to determine both subjective and objective weights, which are then integrated using the Principle of Minimum Entropy Production (PMEP). We therefore develop the FAHP-AGA-FEWM-PMEP diagnostic model, which identifies the main driving indicators of water resources carrying capacity in the region. The framework is applied to screen and diagnose the key driving indicators of each element of water resources carrying capacity, and its stability is verified by comparison with single-method weighting and by parameter sensitivity analysis. The results show that, in terms of water resource carrying capacity evaluation, the Yishusi River Basin experienced severe overload in 2017 with respect to water volume. Henan was critically overloaded, Jiangsu was severely overloaded, and Anhui and Shandong were not overloaded. Regarding the water resource carrying capacity diagnosis, key diagnostic driving indicators for the “regional available water” capacity representation include large and medium-sized reservoir storage, surface water resources, inter-basin water transfer, non-conventional water utilization, and groundwater resources. For the “regional water consumption” load representation, the key diagnostic driving indicators are water consumption per 10,000 CNY of GDP, ecological environment water consumption, water consumption per 10,000 CNY of industrial added value, per capita GDP, the proportion of water used by the secondary industry, and irrigation water consumption per unit area.
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Li et al. (2026) studied this question.
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