Distributed Water Allocation Using Genetic Algorithms
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Solution Overview
Problem
Current water resource allocation methods fail to adequately consider the dynamic interactions between natural and artificial water cycles, leading to inefficient allocation and poor feasibility in meeting individual unit benefits, especially due to neglecting spatial differences and dynamic changes in runoff and water quality.
Innovation Solution
A distributed water resource allocation method that divides a region into allocation units based on digital elevation data, land use, and administrative regions, determining spatial topological relationships between water users and sources, and using genetic algorithms to optimize water allocation while ensuring maximum coordination and minimum pollutant discharge.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If lumped allocation models are used to simplify water resource allocation, then the overall system optimization is improved, but the spatial differences and local water resource issues are neglected
Solution Approach 1:
The patent divides the basin into multiple sub-basins and further segments them into allocation units based on physiographic conditions, land use, and administrative regions. This segmentation allows the model to capture spatial differences while maintaining overall system optimization, resolving the contradiction between global efficiency and local precision.
Solution Approach 2:
The patent applies different allocation strategies and parameters to different allocation units based on their specific characteristics (land use type, soil type, slope, water resource zoning). This local quality approach ensures that each region receives appropriate water allocation considering its unique conditions, thereby improving spatial resolution accuracy without sacrificing overall system optimization.
2Device complexity
If monthly allocation patterns are adopted to simplify calculations, then the computational complexity is reduced, but the dynamic changes in runoff and water quality cannot be responded to
Solution Approach 1:
The patent transitions from static monthly allocation to dynamic daily allocation, allowing the model to respond to daily variations in runoff, water quality, and water demands. The genetic algorithm optimizes allocation daily based on current conditions, achieving adaptability to dynamic changes while managing computational complexity through efficient algorithm design.
Solution Approach 2:
The patent incorporates feedback mechanisms where water quality measurements and runoff data from previous periods inform allocation decisions in subsequent periods. This feedback loop enables the system to adapt to dynamic changes in real-time, adjusting allocation strategies based on actual system performance and environmental conditions.
3Device complexity
If single-objective optimization is used to simplify the allocation process, then the computational burden is reduced, but the coordination among socio-economic, water, and environment subsystems is insufficient
Solution Approach 1:
The patent transforms the multi-objective optimization problem into a multi-parameter optimization framework where multiple objectives (socio-economic benefits, water allocation efficiency, environmental protection) are simultaneously optimized. The genetic algorithm handles multiple objectives by evaluating solutions based on multiple criteria, achieving coordinated development across all subsystems while managing computational burden through efficient parameter optimization.
Data Source
AI summary
A distributed water resource allocation method and which includes: acquiring a sub-basin in a setting region based on data of a digital elevation model, and dividing the sub-basin into a plurality of allocation units by applying a multi-attribute overlaying method; determining a spatial topological relationship between water users and water sources in the various allocation units according to a water supply priority of the water source and a water use priority of the water user; acquiring water demand data in an administrative region, and calculating a water supply amount of each water source as well as a daily amount of water allocation, water consumption, water discharge and pollutant discharge of each water user in the administrative region; constructing objective functions and constraint conditions; and resolving the objective functions based on the constraint conditions and by adopting a genetic algorithm, to obtain water resource allocation data.