The Use of Recurrent Neural Networks (S-RNN, LSTM, GRU) For Flood Forecasting Based on Data Extracted from Classical Hydraulic Modeling DOI Creative Commons

Andrei Mihai Rugină

Modelling in Civil Environmental Engineering, Journal Year: 2023, Volume and Issue: 18(3), P. 1 - 18

Published: Sept. 1, 2023

Abstract Floods are natural disasters that have a significant impact on everyday human life, both through material losses and loss of life. In the context climate change, these events may be more frequent or dangerous. For real-time flood forecasting, fast methods for determining hydrographs along watercourses needed. Classic hydraulic modeling software provides satisfactory results, but in many cases calculation time can high. Another approach, different from classical is use neural networks forecasting hydrographs. Thus, present study aims to analyze three types recurrent networks, including SRNN, RNN-LSTM, RNN-GRU. each network type, flow level resulting were provided as input training data. Using deep learning environment, based previous calibration validation 2 historical modeled. The obtained extremely close those recorded, while running tens times smaller.

Language: Английский

Intensity-Duration-Frequency Curves and Precipitation Frequency Analysis for the Ogun-Osun River Basin Using Annual Maxima Approach DOI
Habeeb Adedotun Alabi, Temitayo Abayomi Ewemoje, Benard Juma

et al.

Published: Jan. 1, 2025

Language: Английский

Citations

0
Accounting for historical data uncertainty in flood frequency analysis: the Upper Rhine River DOI Creative Commons
M. Lang, Jérôme Le Coz, Benjamin Renard

et al.

Journal of Hydrology, Journal Year: 2025, Volume and Issue: unknown, P. 133480 - 133480

Published: May 1, 2025

Language: Английский

Citations

0
Improving the Consistency of Hydrologic Event Identification DOI Creative Commons
Mohammad Masoud Mohammadpour Khoie, Danlu Guo, Conrad Wasko

et al.

Environmental Modelling & Software, Journal Year: 2025, Volume and Issue: unknown, P. 106521 - 106521

Published: May 1, 2025

Language: Английский

Citations

0
Delineation of flood risk terrains and rainfall visualisation in the North Western part of Ghana DOI
Benjamin Wullobayi Dekongmen, Amos T. Kabo‐bah, Geophrey K. Anornu

et al.

Modeling Earth Systems and Environment, Journal Year: 2024, Volume and Issue: 10(3), P. 4567 - 4594

Published: May 16, 2024

Language: Английский

Citations

2
Impact of catchment and climate attributes on flood generating processes and their effect on flood statistics DOI Creative Commons
Svenja Fischer, Markus Pahlow, Shailesh Kumar Singh

et al.

Journal of Hydrology, Journal Year: 2024, Volume and Issue: 646, P. 132361 - 132361

Published: Nov. 16, 2024

Language: Английский

Citations

1
Shifting cold regions streamflow regimes in North America affect flood frequency analysis DOI
Donald H. Burn, Paul H. Whitfield

Hydrological Sciences Journal, Journal Year: 2024, Volume and Issue: 70(1), P. 51 - 70

Published: Oct. 28, 2024

Over threshold flood events in seventy years of data from 202 reference hydrometric stations Canada and the United States were separated into nival, mixed, pluvial types. While Mann-Kendall trend tests showed few significant trends magnitude, changes type fraction found over time, with annual mean temperature, precipitation. Nival decreased frequency seventy-year period 16% sites, while mixed increased (5%, 12%). These indicate a shift nival towards more dominated systems. Fewer analysis against four climate indices. Flood using combined distribution approach three types resulted larger magnitude design flow estimates (median increase 20 – 30 %) comparison results considering to be single population.

Language: Английский

Citations

0
The Use of Recurrent Neural Networks (S-RNN, LSTM, GRU) For Flood Forecasting Based on Data Extracted from Classical Hydraulic Modeling DOI Creative Commons

Andrei Mihai Rugină

Modelling in Civil Environmental Engineering, Journal Year: 2023, Volume and Issue: 18(3), P. 1 - 18

Published: Sept. 1, 2023

Abstract Floods are natural disasters that have a significant impact on everyday human life, both through material losses and loss of life. In the context climate change, these events may be more frequent or dangerous. For real-time flood forecasting, fast methods for determining hydrographs along watercourses needed. Classic hydraulic modeling software provides satisfactory results, but in many cases calculation time can high. Another approach, different from classical is use neural networks forecasting hydrographs. Thus, present study aims to analyze three types recurrent networks, including SRNN, RNN-LSTM, RNN-GRU. each network type, flow level resulting were provided as input training data. Using deep learning environment, based previous calibration validation 2 historical modeled. The obtained extremely close those recorded, while running tens times smaller.

Language: Английский

Citations

0