A New Custom Deep Learning Model Coupled with a Flood Index for Multi-Step-Ahead Flood Forecasting
Guardat en:
| Publicat a: | Hydrology vol. 12, no. 5 (2025), p. 104 |
|---|---|
| Autor principal: | |
| Altres autors: | , , , |
| Publicat: |
MDPI AG
|
| Matèries: | |
| Accés en línia: | Citation/Abstract Full Text + Graphics Full Text - PDF |
| Etiquetes: |
Sense etiquetes, Sigues el primer a etiquetar aquest registre!
|
MARC
| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3211981646 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2306-5338 | ||
| 024 | 7 | |a 10.3390/hydrology12050104 |2 doi | |
| 035 | |a 3211981646 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 100 | 1 | |a Shen, Jianming |u College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China | |
| 245 | 1 | |a A New Custom Deep Learning Model Coupled with a Flood Index for Multi-Step-Ahead Flood Forecasting | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Accurate and prompt flood forecasting is essential for effective decision making in flood control to help minimize or prevent flood damage. We propose a new custom deep learning model, IF-CNN-GRU, for multi-step-ahead flood forecasting that incorporates the flood index (<inline-formula>IF</inline-formula>) to improve the prediction accuracy. The model integrates convolutional neural networks (CNNs) and gated recurrent neural networks (GRUs) to analyze the spatiotemporal characteristics of hydrological data, while using a custom recursive neural network that adjusts the neural unit output at each moment based on the flood index. The IF-CNN-GRU model was applied to forecast floods with a lead time of 1–5 d at the Baihe hydrological station in the middle reaches of the Han River, China, accompanied by an in-depth investigation of model uncertainty. The results showed that incorporating the flood index <inline-formula>IF</inline-formula> improved the forecast precision by up to 20%. The analysis of uncertainty revealed that the contributions of modeling factors, such as the datasets, model structures, and their interactions, varied across the forecast periods. The interaction factors contributed 17–36% of the uncertainty, while the contribution of the datasets increased with the forecast period (32–53%) and that of the model structure decreased (32–28%). The experiment also demonstrated that data samples play a critical role in improving the flood forecasting accuracy, offering actionable insights to reduce the predictive uncertainty and providing a scientific basis for flood early warning systems and water resource management. | |
| 651 | 4 | |a China | |
| 651 | 4 | |a Han River | |
| 653 | |a Damage prevention | ||
| 653 | |a Warning systems | ||
| 653 | |a Resource management | ||
| 653 | |a Early warning systems | ||
| 653 | |a Artificial neural networks | ||
| 653 | |a Flood control | ||
| 653 | |a Water depth | ||
| 653 | |a Flood damage | ||
| 653 | |a Flood forecasting | ||
| 653 | |a Machine learning | ||
| 653 | |a Uncertainty | ||
| 653 | |a Damage | ||
| 653 | |a Datasets | ||
| 653 | |a Water resources management | ||
| 653 | |a Accuracy | ||
| 653 | |a Recurrent neural networks | ||
| 653 | |a Stream flow | ||
| 653 | |a Water resources | ||
| 653 | |a Decision making | ||
| 653 | |a Flood predictions | ||
| 653 | |a Hydrologic data | ||
| 653 | |a Flood management | ||
| 653 | |a Deep learning | ||
| 653 | |a Forecasting | ||
| 653 | |a Floods | ||
| 653 | |a Hydrology | ||
| 653 | |a Lead time | ||
| 653 | |a Neural networks | ||
| 700 | 1 | |a Yang Moyuan |u Beijing Water Science and Technology Institute, Beijing 100048, China | |
| 700 | 1 | |a Zhang, Juan |u Beijing Water Science and Technology Institute, Beijing 100048, China | |
| 700 | 1 | |a Chen, Nan |u Beijing Water Science and Technology Institute, Beijing 100048, China | |
| 700 | 1 | |a Li, Binghua |u Beijing Water Science and Technology Institute, Beijing 100048, China | |
| 773 | 0 | |t Hydrology |g vol. 12, no. 5 (2025), p. 104 | |
| 786 | 0 | |d ProQuest |t Agriculture Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3211981646/abstract/embedded/ZKJTFFSVAI7CB62C?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3211981646/fulltextwithgraphics/embedded/ZKJTFFSVAI7CB62C?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3211981646/fulltextPDF/embedded/ZKJTFFSVAI7CB62C?source=fedsrch |