Flood Event Identification Using First-Order Difference Sequences
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Solution Overview
Problem
Conventional flood event selection methods are inefficient and subjective, particularly when dealing with large datasets, as they rely on manual empirical selections, leading to inaccurate and time-consuming processes in river basin flood forecasting and hydrological model parameter calibration.
Innovation Solution
A flood event identification method that uses first-order difference sequences to automatically determine peak occurrence times and start/end times by applying specific conditions to continuous difference values, followed by smoothing and threshold-based screening to accurately identify flood events and distinguish between single and multi-peak floods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual empirical selection methods are used for flood event identification, then flexibility in judgment can be applied, but the selection process becomes inefficient and subjective when dealing with large datasets
Solution Approach 1:
The patent replaces manual empirical selection (mechanical human judgment) with an automated computational system that uses first-order difference sequences and threshold comparisons to identify flood events, thereby eliminating subjectivity and improving processing efficiency while maintaining identification accuracy
Solution Approach 2:
The system enables self-service automation where the computational algorithm independently processes runoff time sequence data to identify flood events without requiring manual intervention, allowing the system to handle large datasets efficiently while maintaining consistent identification criteria
2Productivity
If automated methods are introduced to improve processing efficiency, then subjectivity is reduced, but the complexity of the identification system increases
Solution Approach 1:
The patent segments the flood identification process into distinct computational steps: calculating first-order difference sequences, identifying peak occurrence times through threshold comparisons, and determining start/end times based on continuous difference values. This segmentation simplifies the overall system complexity while maintaining high processing efficiency
Solution Approach 2:
The system transforms the original runoff time sequence data into first-order difference sequences, changing the parameter representation to make flood event identification more straightforward through automated threshold-based detection, thereby improving efficiency without requiring complex analytical models
3Measurement precision
If manual selection is used to ensure accurate flood event identification, then subjective judgment can be applied, but the process becomes time-consuming for large amounts of data
Solution Approach 1:
The patent replaces time-consuming manual analysis with automated computational processing that rapidly calculates first-order difference sequences and identifies peak occurrence times through systematic threshold comparisons, maintaining accuracy while dramatically reducing the time required for flood event identification
Solution Approach 2:
The system performs preliminary computational preparation by pre-calculating first-order difference sequences from the runoff data, which enables rapid and accurate identification of flood events without requiring time-consuming manual analysis of the original raw data
Data Source
AI summary
Disclosed in the present disclosure are a flood event identification method and apparatus, an electronic device, and a readable storage medium. The method includes: obtaining runoff time sequence data; obtaining initial peak occurrence time by using N continuous first-order difference values in a first-order difference sequence of the runoff time sequence data; obtaining initial start and end time by using M continuous first-order difference values in the first-order difference sequence; and screening out determined peak occurrence time from the initial peak occurrence time, and screening out start and end time corresponding to the peak occurrence time from the initial start and end time. With the technical solution provided by the present disclosure, flood events can be automatically selected with high efficiency and accuracy.