Fractional Ice Cover Prediction With Thermodynamics and Machine Learning
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Solution Overview
Problem
Current hydrodynamic models struggle to accurately simulate hydrodynamics during periods of partial ice cover due to the lack of precise knowledge of the spatial extent of ice cover, which affects water circulation and nutrient exchange.
Innovation Solution
A combined approach using a thermodynamics module and a machine learning module to predict ice cover, with feedback loops and in-situ observations to refine predictions, applying weightings and thresholds to optimize ice cover predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a hydrodynamic model is used to simulate water movement, then the simulation can capture water circulation patterns, but the accuracy deteriorates during periods of partial ice cover due to lack of precise ice cover knowledge
Solution Approach 1:
The system uses in-situ ice cover observations to continuously update and refine the hydrodynamic model's ice cover representation. This feedback loop allows the model to correct its ice cover knowledge based on actual measurements, thereby maintaining simulation accuracy during partial ice cover periods.
Solution Approach 2:
The patent introduces an intermediary data assimilation process that bridges the gap between sparse in-situ observations and the continuous hydrodynamic model simulation. This intermediary mechanism allows the model to incorporate limited observation data into its ice cover representation, improving accuracy without requiring complete spatial coverage.
2Measurement precision
If in-situ observations are used to improve ice cover prediction, then prediction accuracy improves, but the system complexity increases due to integration of multiple data sources and models
Solution Approach 1:
The system divides the ice cover prediction problem into separate functional modules: a hydrodynamic model for water circulation, a machine learning module for ice cover prediction, and an data assimilation module for integrating observations. This segmentation allows each component to be developed and optimized independently while working together through standardized interfaces.
Solution Approach 2:
The patent creates a unified framework where the same computational infrastructure serves multiple purposes: simulating hydrodynamics, predicting ice cover from satellite data, processing in-situ observations, and updating model parameters. This multi-functionality reduces overall system complexity by eliminating the need for separate dedicated systems for each function.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Improves the accuracy of fractional ice cover prediction, enhancing the simulation of hydrodynamics, nutrient exchange, and biological processes by integrating satellite data, thermodynamics, and in-situ observations.
Implementation Method 1
generating a first ice cover prediction with a thermodynamics module
Implementation Method 2
generating a second ice cover prediction with a machine learning module
Implementation Method 3
The first ice cover prediction is combined with the second ice cover prediction to generate a combined ice cover prediction
Data Source
AI summary
A computer implemented method of predicting ice coverage on a body of water includes generating a first ice cover prediction with a thermodynamics module and generating a second ice cover prediction with a machine learning module. The first ice cover prediction is combined with the second ice cover prediction to generate a combined ice cover prediction. Error statistics are computed based on a comparison of the combined ice cover prediction with an ice coverage observation and the combined ice cover prediction is updated based on the error statistics.