A water resource bearing condition evaluation method and system, and a storage medium
By constructing a nested framework and Monte Carlo simulation, dynamically adjusting the threshold range, and combining the interaction model and decision tree algorithm, the spatial hierarchy and temporal variation of water resource carrying capacity assessment are solved, enabling refined water resource management and sustainable development recommendations.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- 河南省水文水资源测报中心
- Filing Date
- 2026-04-29
- Publication Date
- 2026-07-17
AI Technical Summary
Existing water resource carrying capacity assessment methods are insufficient to reflect the water resource transfer relationships between different spatial levels, cannot accurately identify the overload status and risk evolution trends in local areas, and lack dynamic feedback mechanisms, which affects the scientific nature of water resource allocation and ecological protection decisions.
A nested framework is constructed to analyze the mutual influencing factors. The Monte Carlo simulation algorithm is used to simulate the resource change sequence and dynamically adjust the threshold range. Resource allocation suggestions are generated through the decision tree algorithm. Multi-level carrying capacity classification is performed by combining the mutual influence model and the decision tree algorithm.
It enables dynamic and detailed assessment of water resource carrying capacity, improves the accuracy of assessment results and the practicality of regulation recommendations, and can adapt to climate change and supply and demand changes, providing differentiated resource allocation recommendations.
Smart Images
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