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Erschienen in: Water Resources Management 6/2024

29.02.2024

A Rapid Forecast Method for the Process of Flash Flood Based on Hydrodynamic Model and KNN Algorithm

verfasst von: Nie Zhou, Jingming Hou, Hua Chen, Guangzhao Chen, Bingyi Liu

Erschienen in: Water Resources Management | Ausgabe 6/2024

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Abstract

Using hydrodynamic models to carry out early warning and flash floods forecasting is an essential measure for loss reduction. Nevertheless, many current hydrodynamic models lack the necessary forecasting timeliness. To address this limitation, a method combining a hydrodynamic model with the K nearest neighbours (KNN) algorithm is proposed to facilitate the rapid prediction of flash flood processes. With the rainfall sequence as the input data and the simulation results of the hydrodynamic model as the target data, the rapid forecast of water depth, water velocity and discharge are achieved. Then the Baogai Temple basin is utilized as a case study, and the rapid forecast model (RFM) is established and subjected to verification for reliability and timeliness. The results demonstrate that the established model exhibits remarkable accuracy, with 99% of the test data effectively limiting the error of accumulated inundation extent within 20%. Furthermore, the Nash-Sutcliffe efficiency (NSE) for cross-sectional discharge achieves a value of 0.98. In 75% of rainfall scenarios, both the maximum average water depth and velocity errors for the cross-sections are effectively confined to 7.5% and 10%, respectively. The model also boasts a substantial improvement in computational efficiency, enabling it to complete the prediction of the flooding process for the next 10 h within 25s. This enhancement offers valuable lead time for emergency decision-making and highlights its extensive application potential in managing flash floods.

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Literatur
Zurück zum Zitat Shahabi H, Shirzadi A, Ghaderi K et al (2020) Flood detection and susceptibility mapping using sentinel-1 remote sensing data and a machine learning approach: hybrid intelligence of bagging ensemble based on k-nearest neighbor classifier. Remote Sens 12(2). https://doi.org/10.3390/rs12020266 Shahabi H, Shirzadi A, Ghaderi K et al (2020) Flood detection and susceptibility mapping using sentinel-1 remote sensing data and a machine learning approach: hybrid intelligence of bagging ensemble based on k-nearest neighbor classifier. Remote Sens 12(2). https://​doi.​org/​10.​3390/​rs12020266
Zurück zum Zitat Shao YM, Shao DN (2014) The new generation of rainstorm intensity formula in Chinese cities. China Architecture & Building Press, Beijing Shao YM, Shao DN (2014) The new generation of rainstorm intensity formula in Chinese cities. China Architecture & Building Press, Beijing
Metadaten
Titel
A Rapid Forecast Method for the Process of Flash Flood Based on Hydrodynamic Model and KNN Algorithm
verfasst von
Nie Zhou
Jingming Hou
Hua Chen
Guangzhao Chen
Bingyi Liu
Publikationsdatum
29.02.2024
Verlag
Springer Netherlands
Erschienen in
Water Resources Management / Ausgabe 6/2024
Print ISSN: 0920-4741
Elektronische ISSN: 1573-1650
DOI
https://doi.org/10.1007/s11269-023-03664-0

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