Device and method for monitoring and early warning of flood damages based on multi-resource integration

Abstract

The invention discloses a device and method for monitoring and early warning of flood damages based on multi-resource integration. The device comprises a hydrological actual state module, a flow forecast module, a weather forecast module, a radar analysis module, a forecast integration module and a module for product display and early warning release. The method comprises the steps that (1) the hydrological actual state module reads and monitors monitoring point data; (2) the weather forecast module reads input data and parameters to carry out weather mode forecasting; (3) the flow forecast module carries out flow forecasting according to the weather mode forecast and the monitoring point data provided by the hydrological actual state module; (4) the radar analysis module reaches basic data of a radar and estimates precipitation and optical-flow extrapolation; (5) the forecast integration module integrates and outputs information of the flow forecast module and the radar analysis module; and (6) the module for the production display and the early warning release completes the product display and the early warning release. The device and the method consider accuracy of radar extrapolation forecasting in forecasting of the position of a precipitation system and also consider capacity of numerical mode forecasting, so that limitations of the single method are avoided.

Description

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AI Technical Summary

Benefits of technology

This technology helps improve both predictability and precision when detecting potential hazards caused by rain or snow storms on an area's surface (such as near water). It uses data from multiple sources like radars that capture different aspects related to rainfall patterns over time to make better predictions about future events based upon these observations.

Problems solved by technology

Technological Problem: Current hydraulic simulation techniques are limited due to their lack of accurate predictive results from real-time observation or simulations with high precision. This limitation makes it difficult to use traditional methodology like statistical analysis (such as water level) alone without overestimating risk factors associated with large areas affected during an event.

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