Adaptive Water Quality Prediction Using Dynamic Fluid Model Parameters
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Solution Overview
Problem
Fluid dynamic models used for water quality prediction often fail to accurately simulate water quality indicators due to non-adaptive water body parameters that do not account for changes in the water environment over time, leading to inaccurate results.
Innovation Solution
A method that estimates a value range of water body parameters using measured data from two sets of time-spatial points and determines an optimal parameter value by comparing measured and simulated data, employing a deep learning model and genetic algorithm to refine the fluid dynamic model's parameters adaptively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If non-adaptive water body parameters are used in fluid dynamic models, then the model structure remains simple and easy to operate, but the prediction accuracy deteriorates due to inability to account for environmental changes over time
Solution Approach 1:
The patent transforms static water body parameters into dynamic parameters that adapt to environmental changes. The system automatically updates parameters such as bottom roughness, wind friction, and thermal exchange coefficients based on real-time environmental conditions, enabling the fluid dynamic model to maintain high prediction accuracy while responding to temporal and spatial variations in the water environment
Solution Approach 2:
The patent implements a feedback mechanism where measured water quality data and environmental parameters are continuously fed back into the system to adjust and optimize model parameters. This closed-loop approach allows the model to learn from actual observations and improve its predictions by correcting parameter values based on discrepancies between simulated and measured data
2Measurement precision
If water body parameters are updated adaptively to reflect environmental changes, then prediction accuracy improves, but the complexity of parameter estimation and model calibration increases
Solution Approach 1:
The patent introduces an intermediary system comprising data processing modules and optimization algorithms that bridge the gap between raw environmental measurements and model parameters. This intermediary layer automatically processes measured data from sensors and environmental monitoring systems, transforming it into optimized parameter values through mathematical optimization, thereby reducing the direct difficulty of parameter estimation
Solution Approach 2:
The system implements self-service through automated parameter calibration where the model uses its own performance metrics to guide parameter optimization. The system automatically identifies which parameters need adjustment, searches for optimal values using optimization algorithms, and updates parameters without requiring extensive manual intervention, thereby reducing the overall difficulty of parameter estimation
Data Source
AI summary
This disclosure provides a computer-implemented method. The method may comprise estimating a value range of a water body parameter based on measured data for a water quality indicator of a first set of time-spatial points and measured data for the water quality indicator of a second set of time-spatial points; and determining an optimal value of the water body parameter from the estimated value range by comparing the measured data for the water quality indicator of the second set and simulated data for the water quality indicator of the second set, wherein the simulated data for the water quality indicator of the second set is obtained based on a fluid dynamic model using the measured data for the water quality indicator of the first set as an input of the fluid dynamic model and using a value in the estimated value range as a parameter of the fluid dynamic model.