Aquatic Epidemic Alert System Using Water Flow Data
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Solution Overview
Problem
Current aquatic epidemic alert systems fail to provide sufficient protection to the aquaculture industry as they often target only specific types of risks, cover large areas, have poor time resolution, and rely on historical data, lacking real-time information specific to individual aquaculture sites, which can lead to unnoticed threats and significant yield losses.
Innovation Solution
A method and system that utilize real-time information about detected aquatic epidemics combined with water flow data to determine a threat level for specific locations, providing quantitative forecasts and alerts through a user-defined messaging framework, enabling precise spatial and temporal monitoring and updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If real-time monitoring and threat level determination systems are implemented, then the accuracy and timeliness of epidemic alerts is improved, but the system complexity and computational requirements increase
Solution Approach 1:
The system segments the monitoring process into distinct functional modules: data collection from multiple sources (satellites, sensors, buoys), water flow analysis, epidemic detection algorithms, threat level determination, and alert generation. This modular architecture improves measurement precision while managing system complexity through organized functional divisions.
Solution Approach 2:
The system performs preliminary water flow analysis and epidemic detection before final threat level determination. By pre-processing data and identifying potential threats in advance, the system achieves higher alert accuracy without proportionally increasing operational complexity during critical decision-making periods.
2Manufacturing precision
If comprehensive water flow data analysis is performed for threat level determination, then the spatial and temporal resolution of alerts is improved, but the data processing time and computational resources increase
Solution Approach 1:
The system implements periodic data collection and analysis cycles at optimized intervals rather than continuous processing. This approach maintains high spatial and temporal resolution in alerts while reducing overall computational burden and processing time by focusing intensive analysis only when new data becomes available or thresholds are triggered.
Solution Approach 2:
The system applies partial analysis to full water flow datasets by focusing computational resources on regions and time periods with detected epidemic activity or abnormal conditions. This selective processing approach achieves high resolution where needed while minimizing unnecessary computation in low-risk areas, balancing precision with processing efficiency.
3Reliability
If real-time epidemic detection and alert systems are deployed, then the ability to prevent yield losses is improved, but the operational costs and infrastructure requirements increase
Solution Approach 1:
The system employs multi-functional satellite and sensor platforms that serve both epidemic detection and general oceanographic monitoring purposes. This universal approach improves protection capability against aquatic epidemics while avoiding duplicate infrastructure investments, as the same satellites and sensors provide multiple benefits including weather monitoring, climate research, and marine resource management.
Solution Approach 2:
The system utilizes freely available satellite data, open-source oceanographic models, and publicly funded research databases to reduce infrastructure requirements. By leveraging existing public resources and automated data processing pipelines, the system achieves reliable epidemic detection and protection capability without requiring extensive proprietary infrastructure or continuous operational funding.
Data Source
AI summary
Embodiments for providing aquatic epidemic alerts by a processor are described. Information associated with an aquatic epidemic in a body of water is received. A threat level associated with the aquatic epidemic for a location within the body of water is determined based on the information associated with the aquatic epidemic and water flow data associated with the body of water. An indication of the threat level is generated.