Real-time rainfall and runoff prediction by integrating BC-MODWT and automatically-tuned DNNs: Comparing different deep learning models

Journal of Hydrology, Journal Year: 2024, Volume and Issue: 631, P. 130804 - 130804

Published: Jan. 26, 2024

Language: Английский

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A novel additive regression model for streamflow forecasting in German rivers

Results in Engineering, Journal Year: 2024, Volume and Issue: 22, P. 102104 - 102104

Published: April 10, 2024

Forecasting streamflows, essential for flood mitigation and the efficient management of water resources drinking, agriculture hydroelectric power generation, presents a formidable challenge in most real-world scenarios. In this study, two models, first based on Additive Regression Radial Basis Function Neural Networks (AR-RBF) second stacking with Pace Multilayer Perceptron Random Forest (MLP-RF-PR), were compared prediction short-term (1–3 days ahead) medium-term (7 daily streamflow rates three different rivers Germany: Elbe River at Wittenberge, Leine Herrenhausen, Saale Hof The lagged values rate, precipitation temperature considered modeling. Moreover, Bayesian Optimization (BO) algorithm was used to assess optimal number hyperparameters. Both models showed accurate predictions forecasting, R2 1-day ahead ranging from 0.939 0.998 AR-RBF 0.930 0.996 MLP-RF-PR, while MAPE ranged 2.02 % 8.99 2.14 9.68 when exogeneous variables included. As forecast horizon increased, reduction forecasting accuracy observed. However, both could still predict overall flow pattern, even 7-day-ahead predictions, 0.772 0.871 0.703 0.840 10.60 20.45 10.44 19.65 MLP-RF-PR. Overall, outcomes study suggest that MLP-RF-PR can be reliable tools short- rate prediction, requiring short parameters optimized, making them easy implement reducing calculation time required.

Language: Английский

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Comparison of strategies for multistep-ahead lake water level forecasting using deep learning models

Journal of Cleaner Production, Journal Year: 2024, Volume and Issue: 444, P. 141228 - 141228

Published: Feb. 13, 2024

Language: Английский

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An optimized NARX-based model for predicting thermal dynamics and heatwaves in rivers

The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 926, P. 171954 - 171954

Published: March 26, 2024

Language: Английский

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Advanced streamflow forecasting for Central European Rivers: The Cutting-Edge Kolmogorov-Arnold networks compared to Transformers

Journal of Hydrology, Journal Year: 2024, Volume and Issue: unknown, P. 132175 - 132175

Published: Oct. 1, 2024

Language: Английский

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