Distributed River Sensing for Timely Multi-Source Flood Forecasting
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Solution Overview
Problem
Existing flood forecasting systems for small and medium-sized rivers lack accuracy and timeliness due to insufficient data collection and transmission, especially in areas without data, leading to rapid and destructive floods.
Innovation Solution
A flood forecasting system utilizing multi-source fusion precipitation information and real-time perception information, comprising a flood multi-information embedding platform with optical fibers and rotation blades, which are fixed in river channels to collect and analyze flood data through a distributed sensing network, allowing for real-time monitoring and forecasting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional flood monitoring systems are used in small and medium-sized rivers, then the system structure is simple, but the forecasting accuracy and timeliness deteriorate due to insufficient data collection
Solution Approach 1:
The system divides the river monitoring into multiple embedding platforms distributed along the river channel. Each platform independently collects local precipitation and flood data, then transmits to a central processing system. This segmentation enables comprehensive data collection across the river basin while maintaining modular, manageable system components that can be deployed incrementally in small and medium-sized rivers.
Solution Approach 2:
The embedding platform integrates multiple functions into a single device: precipitation collection through collectors, flood level sensing through sensors, data transmission through communication modules, and power management. This multi-functional design improves forecasting accuracy by collecting diverse data types while controlling overall system complexity through functional integration at each platform node.
2Loss of time
If data collection infrastructure is expanded to cover more areas, then the forecasting timeliness improves, but the cost and complexity of the system increases
Solution Approach 1:
The embedding platforms are designed to be self-contained units with integrated power supplies, sensors, and communication capabilities that can operate autonomously in remote river locations. Each platform self-manages data collection, processing, and transmission without requiring extensive external infrastructure, enabling rapid deployment across multiple locations to improve forecasting timeliness while limiting infrastructure complexity.
Solution Approach 2:
The system replaces traditional mechanical data transmission methods (physical data carriers, manual reporting) with electronic sensing and wireless communication technologies. Sensors automatically detect flood parameters and transmit data electronically to the forecasting center, dramatically improving timeliness while reducing the need for physical infrastructure such as roads, bridges, and manual data collection teams.
3Reliability
If traditional flood forecasting methods are used in data-scarce areas, then the implementation cost is low, but the forecasting reliability deteriorates
Solution Approach 1:
The system performs preliminary data collection and processing at distributed embedding platforms before transmitting to the forecasting center. Precipitation collectors and sensors continuously gather data and pre-process it locally, ensuring that even in data-scarce areas, a baseline of reliable data is available. This preliminary action at the field level improves forecasting reliability by ensuring data quality before transmission, overcoming the limitation of scarce data in remote river basins.
Data Source
AI summary
Disclosed is a flood forecasting system driven by multi-source fusion precipitation information and real-time perception information, which comprises a flood multi-information embedding platform, the flood multi-information embedding platform is fixed in a river channel via a fixing device, the flood multi-information embedding platform is provided with a plurality of large-flow through holes and a plurality of small-flow through holes which are symmetrically distributed left and right, a large sleeve is mounted on the large-flow through hole, a small sleeve is mounted on the small-flow through hole, a rotatable rotation blade is fixedly mounted in the large sleeve, the flood multi-information embedding platform is further provided with a blind hole, and a cylinder is mounted in the blind hole.