Similarity discrimination and extrapolation prediction methods of heavy rain and flood
- Article
- Figures
- Metrics
- Preview PDF
- Reference
- Related
- Cited by
- Materials
Abstract:
Flood forecasting and prediction are integral components of non-structural flood management measures. Methods for flood forecasting and prediction can generally be classified into two categories: process-driven approaches (hydrological models) and data-driven approaches. Traditionally, the focus has been on process-driven approaches, but with the accumulation of hydrological data and advancements in big data analytics, data-driven approaches have gained increasing attention. In particular, the application of artificial intelligence technology in the water industry has led to the emergence of hydrological data mining-based forecasting and prediction methods as a research hotspot. Conducting hydrological knowledge mining and prediction based on the principle of similarity has become an important research direction, offering a new technical means to uncover hidden patterns within rainfall, floods, and watershed surface information. This approach also promotes the automation and intelligence of water resources data processing, assisting in improving the accuracy of flood forecasting and prediction, thereby facilitating the modernization and precision of the water industry.In theory, the longer the series of hydrological data, the more torrential rain-induced flood knowledge can be extracted. However, hydrological data series in a changing environment often exhibit inconsistencies, which affect the accuracy of flood forecasting and prediction based on torrential rain-induced flood knowledge. Currently, research on historical similar torrential rain-induced flood knowledge considering inconsistencies in guiding real-time flood forecasting is relatively limited. In this context, a methodology based on the knowledge of torrential rain-induced floods for real-time flood forecasting and prediction is proposed. The proposed method focuses on historical records of typical torrential rain-induced floods and extracts rainfall feature indicators, such as the position of the rainstorm center, antecedent precipitation, total average rainfall, and rainfall processes. Multiple feature indicators are simultaneously assessed for their similarity using criteria such as Euclidean distance. By inferring historical typical floods based on similarity knowledge and incorporating the "rainfall-peak flow" or "rainfall-runoff" relationship before and after the change, a combined "peak-flow" correction approach is applied to ensure consistency. Real-time rolling extrapolation is then performed to estimate future flood processes, forming a comprehensive "multi-feature indicator extraction-historical torrential rain-induced flood similarity determination-real-time flood correction and extrapolation" technique.The application results at the Mengyin Station on the Yi River demonstrate the effectiveness of the proposed methodology. For any given torrential rain-induced flood event, the most similar historical flood event can be accurately identified through multiple feature indicators, ensuring the theoretical correctness of the technique. By considering the most identified similar flood event and applying suitable corrections to ensure consistency, the extrapolation and prediction of future flood processes significantly improve the accuracy of real-time flood forecasting compared to the direct application of similar flood processes.In summary, the suggested methodology, grounded in torrential rain-induced flood knowledge, introduces an effective avenue for real-time flood forecasting and prediction. By extracting multiple feature indicators, evaluating their similarity, and incorporating correction and extrapolation steps, it enables accurate identification of similar historical flood events and enhances the precision of real-time flood forecasting. This study contributes to the progression of flood management and establishes the groundwork for further research aimed at enhancing flood forecasting accuracy and propelling the modernization of the water industry.
Keywords:
Project Supported:
Clc Number:
Mobile website
® 2024 All Rights Reserved
冀公网安备13010502002656号
冀公网安备13010502002656号