Evaluation of LSTM vs. conceptual models for hourly rainfall runoff simulations with varied training period lengths
Abstract Accurate high-resolution runoff predictions are essential for effective flood mitigation and water planning. In hydrology, conceptual models are preferred for their simplicity, despite their limited capacity for accurate predictions. Deep-learning applications have recently shown promise fo...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-05-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-96577-4 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849312101866143744 |
|---|---|
| author | Mohamed M. Fathi Md Abdullah Al Mehedi Virginia Smith Anjali M. Fernandes Michael T. Hren Dennis O. Terry |
| author_facet | Mohamed M. Fathi Md Abdullah Al Mehedi Virginia Smith Anjali M. Fernandes Michael T. Hren Dennis O. Terry |
| author_sort | Mohamed M. Fathi |
| collection | DOAJ |
| description | Abstract Accurate high-resolution runoff predictions are essential for effective flood mitigation and water planning. In hydrology, conceptual models are preferred for their simplicity, despite their limited capacity for accurate predictions. Deep-learning applications have recently shown promise for runoff predictions; however, they usually require longer input data sequences, especially for high-temporal resolution simulations, thus leading to increased model complexity. To address these challenges, this study evaluates the robustness of two novel approaches using Long Short-Term Memory (LSTM) models. The first model integrates the outputs of a simple conceptual model with LSTM capabilities, while the second model is a stand-alone model that combines coarse and fine temporal inputs to capture both long and short dependencies. To ensure accuracy and reliability, we utilized a century-long meteorological dataset generated from a sophisticated physics-based model, eliminating any influence of measurement errors. The training phase employed multiple sub-periods ranging from 7- to 50-year, with a separate 50-year subset for validation. Our findings highlight the consistent improvement of both LSTM models with increasing training dataset lengths, while conceptual models show no notable enhancement beyond 15 years of training data. Both LSTM models demonstrate superior performance in capturing the reference flow duration curve, offering a promising pathway for more computationally efficient models for runoff predictions. |
| format | Article |
| id | doaj-art-4c87e38915a042f9bab0ca53bc7f9563 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-4c87e38915a042f9bab0ca53bc7f95632025-08-20T03:53:12ZengNature PortfolioScientific Reports2045-23222025-05-0115111210.1038/s41598-025-96577-4Evaluation of LSTM vs. conceptual models for hourly rainfall runoff simulations with varied training period lengthsMohamed M. Fathi0Md Abdullah Al Mehedi1Virginia Smith2Anjali M. Fernandes3Michael T. Hren4Dennis O. Terry5Department of Civil Engineering, Faculty of Engineering, Fayoum UniversityDepartment of Civil and Environmental Engineering, Villanova UniversityDepartment of Civil and Environmental Engineering, Villanova UniversityDepartment of Earth and Environmental Sciences, Denison UniversityDepartment of Earth Sciences, University of ConnecticutDepartment of Earth and Environmental Science, Temple UniversityAbstract Accurate high-resolution runoff predictions are essential for effective flood mitigation and water planning. In hydrology, conceptual models are preferred for their simplicity, despite their limited capacity for accurate predictions. Deep-learning applications have recently shown promise for runoff predictions; however, they usually require longer input data sequences, especially for high-temporal resolution simulations, thus leading to increased model complexity. To address these challenges, this study evaluates the robustness of two novel approaches using Long Short-Term Memory (LSTM) models. The first model integrates the outputs of a simple conceptual model with LSTM capabilities, while the second model is a stand-alone model that combines coarse and fine temporal inputs to capture both long and short dependencies. To ensure accuracy and reliability, we utilized a century-long meteorological dataset generated from a sophisticated physics-based model, eliminating any influence of measurement errors. The training phase employed multiple sub-periods ranging from 7- to 50-year, with a separate 50-year subset for validation. Our findings highlight the consistent improvement of both LSTM models with increasing training dataset lengths, while conceptual models show no notable enhancement beyond 15 years of training data. Both LSTM models demonstrate superior performance in capturing the reference flow duration curve, offering a promising pathway for more computationally efficient models for runoff predictions.https://doi.org/10.1038/s41598-025-96577-4Length of calibration datasetGR4H and GR5H modelsOne-step ahead predictionEfficient LSTM modelsHybrid ML and conceptual modelsLonger training dataset |
| spellingShingle | Mohamed M. Fathi Md Abdullah Al Mehedi Virginia Smith Anjali M. Fernandes Michael T. Hren Dennis O. Terry Evaluation of LSTM vs. conceptual models for hourly rainfall runoff simulations with varied training period lengths Scientific Reports Length of calibration dataset GR4H and GR5H models One-step ahead prediction Efficient LSTM models Hybrid ML and conceptual models Longer training dataset |
| title | Evaluation of LSTM vs. conceptual models for hourly rainfall runoff simulations with varied training period lengths |
| title_full | Evaluation of LSTM vs. conceptual models for hourly rainfall runoff simulations with varied training period lengths |
| title_fullStr | Evaluation of LSTM vs. conceptual models for hourly rainfall runoff simulations with varied training period lengths |
| title_full_unstemmed | Evaluation of LSTM vs. conceptual models for hourly rainfall runoff simulations with varied training period lengths |
| title_short | Evaluation of LSTM vs. conceptual models for hourly rainfall runoff simulations with varied training period lengths |
| title_sort | evaluation of lstm vs conceptual models for hourly rainfall runoff simulations with varied training period lengths |
| topic | Length of calibration dataset GR4H and GR5H models One-step ahead prediction Efficient LSTM models Hybrid ML and conceptual models Longer training dataset |
| url | https://doi.org/10.1038/s41598-025-96577-4 |
| work_keys_str_mv | AT mohamedmfathi evaluationoflstmvsconceptualmodelsforhourlyrainfallrunoffsimulationswithvariedtrainingperiodlengths AT mdabdullahalmehedi evaluationoflstmvsconceptualmodelsforhourlyrainfallrunoffsimulationswithvariedtrainingperiodlengths AT virginiasmith evaluationoflstmvsconceptualmodelsforhourlyrainfallrunoffsimulationswithvariedtrainingperiodlengths AT anjalimfernandes evaluationoflstmvsconceptualmodelsforhourlyrainfallrunoffsimulationswithvariedtrainingperiodlengths AT michaelthren evaluationoflstmvsconceptualmodelsforhourlyrainfallrunoffsimulationswithvariedtrainingperiodlengths AT dennisoterry evaluationoflstmvsconceptualmodelsforhourlyrainfallrunoffsimulationswithvariedtrainingperiodlengths |