Informed Neural Networks for Flood Forecasting With Limited Amount of Training Data
Abstract This study introduces a novel method called Informed Neural Networks (INNs), developed to enhance flood forecasting accuracy, particularly under limited data conditions. Accurate flood forecasts are crucial for timely evacuations, especially as heavy rainfall increasingly threatens areas pr...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2025-03-01
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| Series: | Water Resources Research |
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| Online Access: | https://doi.org/10.1029/2023WR036380 |
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| _version_ | 1849687885467353088 |
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| author | K. Komiya H. Kiyotake R. Nakada M. Fujishima K. Mori |
| author_facet | K. Komiya H. Kiyotake R. Nakada M. Fujishima K. Mori |
| author_sort | K. Komiya |
| collection | DOAJ |
| description | Abstract This study introduces a novel method called Informed Neural Networks (INNs), developed to enhance flood forecasting accuracy, particularly under limited data conditions. Accurate flood forecasts are crucial for timely evacuations, especially as heavy rainfall increasingly threatens areas previously unaffected by flooding. Traditional methods often require extensive data and frequent updates, making them costly and challenging to maintain. INNs address these challenges by enabling accurate predictions under limited data conditions. We propose an INN architecture for rivers in regions like Japan, where floods are predominantly caused by rainfall. We applied the INN to both rainfall‐dominated and non‐rainfall‐dominated floods to evaluate its effectiveness and limitations. Our experiments show that the INN effectively integrates domain knowledge, maintains performance, and achieves lower prediction errors than ANN in data‐scarce scenarios. These findings highlight the potential of INNs as a promising approach for future flood forecasting, particularly in data‐limited environments. |
| format | Article |
| id | doaj-art-ad7e596392c54d6ba0a230d4e4a08121 |
| institution | DOAJ |
| issn | 0043-1397 1944-7973 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Wiley |
| record_format | Article |
| series | Water Resources Research |
| spelling | doaj-art-ad7e596392c54d6ba0a230d4e4a081212025-08-20T03:22:12ZengWileyWater Resources Research0043-13971944-79732025-03-01613n/an/a10.1029/2023WR036380Informed Neural Networks for Flood Forecasting With Limited Amount of Training DataK. Komiya0H. Kiyotake1R. Nakada2M. Fujishima3K. Mori4Digitial Twin Computing Research Center Tokyo JapanDigitial Twin Computing Research Center Tokyo JapanDigitial Twin Computing Research Center Tokyo JapanDigitial Twin Computing Research Center Tokyo JapanDigitial Twin Computing Research Center Tokyo JapanAbstract This study introduces a novel method called Informed Neural Networks (INNs), developed to enhance flood forecasting accuracy, particularly under limited data conditions. Accurate flood forecasts are crucial for timely evacuations, especially as heavy rainfall increasingly threatens areas previously unaffected by flooding. Traditional methods often require extensive data and frequent updates, making them costly and challenging to maintain. INNs address these challenges by enabling accurate predictions under limited data conditions. We propose an INN architecture for rivers in regions like Japan, where floods are predominantly caused by rainfall. We applied the INN to both rainfall‐dominated and non‐rainfall‐dominated floods to evaluate its effectiveness and limitations. Our experiments show that the INN effectively integrates domain knowledge, maintains performance, and achieves lower prediction errors than ANN in data‐scarce scenarios. These findings highlight the potential of INNs as a promising approach for future flood forecasting, particularly in data‐limited environments.https://doi.org/10.1029/2023WR036380flood forecastingmachine learningartificial neural networksdomain‐specific knowledge |
| spellingShingle | K. Komiya H. Kiyotake R. Nakada M. Fujishima K. Mori Informed Neural Networks for Flood Forecasting With Limited Amount of Training Data Water Resources Research flood forecasting machine learning artificial neural networks domain‐specific knowledge |
| title | Informed Neural Networks for Flood Forecasting With Limited Amount of Training Data |
| title_full | Informed Neural Networks for Flood Forecasting With Limited Amount of Training Data |
| title_fullStr | Informed Neural Networks for Flood Forecasting With Limited Amount of Training Data |
| title_full_unstemmed | Informed Neural Networks for Flood Forecasting With Limited Amount of Training Data |
| title_short | Informed Neural Networks for Flood Forecasting With Limited Amount of Training Data |
| title_sort | informed neural networks for flood forecasting with limited amount of training data |
| topic | flood forecasting machine learning artificial neural networks domain‐specific knowledge |
| url | https://doi.org/10.1029/2023WR036380 |
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