Technical note: An approach for handling multiple temporal frequencies with different input dimensions using a single LSTM cell
<p>Long short-term memory (LSTM) networks have demonstrated state-of-the-art performance for rainfall-runoff hydrological modelling. However, most studies focus on predictions at a daily scale, limiting the benefits of sub-daily (e.g. hourly) predictions in applications like flood forecasting....
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2025-03-01
|
| Series: | Hydrology and Earth System Sciences |
| Online Access: | https://hess.copernicus.org/articles/29/1749/2025/hess-29-1749-2025.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850206050122203136 |
|---|---|
| author | E. Acuña Espinoza F. Kratzert D. Klotz D. Klotz M. Gauch M. Álvarez Chaves R. Loritz U. Ehret |
| author_facet | E. Acuña Espinoza F. Kratzert D. Klotz D. Klotz M. Gauch M. Álvarez Chaves R. Loritz U. Ehret |
| author_sort | E. Acuña Espinoza |
| collection | DOAJ |
| description | <p>Long short-term memory (LSTM) networks have demonstrated state-of-the-art performance for rainfall-runoff hydrological modelling. However, most studies focus on predictions at a daily scale, limiting the benefits of sub-daily (e.g. hourly) predictions in applications like flood forecasting. Moreover, training an LSTM network exclusively on sub-daily data is computationally expensive and may lead to model learning difficulties due to the extended sequence lengths. In this study, we introduce a new architecture, multi-frequency LSTM (MF-LSTM), designed to use input of various temporal frequencies to produce sub-daily (e.g. hourly) predictions at a moderate computational cost. Building on two existing methods previously proposed by the co-authors of this study, MF-LSTM processes older inputs at coarser temporal resolutions than more recent ones. MF-LSTM gives the possibility of handling different temporal frequencies, with different numbers of input dimensions, in a single LSTM cell, enhancing the generality and simplicity of use. Our experiments, conducted on 516 basins from the CAMELS-US dataset, demonstrate that MF-LSTM retains state-of-the-art performance while offering a simpler design. Moreover, the MF-LSTM architecture reported a 5 times reduction in processing time compared to models trained exclusively on hourly data.</p> |
| format | Article |
| id | doaj-art-63dc8d3d52f94368bc8e328512840e06 |
| institution | OA Journals |
| issn | 1027-5606 1607-7938 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Hydrology and Earth System Sciences |
| spelling | doaj-art-63dc8d3d52f94368bc8e328512840e062025-08-20T02:10:57ZengCopernicus PublicationsHydrology and Earth System Sciences1027-56061607-79382025-03-01291749175810.5194/hess-29-1749-2025Technical note: An approach for handling multiple temporal frequencies with different input dimensions using a single LSTM cellE. Acuña Espinoza0F. Kratzert1D. Klotz2D. Klotz3M. Gauch4M. Álvarez Chaves5R. Loritz6U. Ehret7Institute of Water and Environment, Karlsruhe Institute of Technology (KIT), Karlsruhe, GermanyGoogle Research, Vienna, AustriaGoogle Research, Vienna, AustriaMachine Learning in Earth Science, Interdisciplinary Transformation University Austria, Linz, AustriaGoogle Research, Zurich, SwitzerlandStuttgart Center for Simulation Science, Statistical Model-Data Integration, University of Stuttgart, Stuttgart, GermanyInstitute of Water and Environment, Karlsruhe Institute of Technology (KIT), Karlsruhe, GermanyInstitute of Water and Environment, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany<p>Long short-term memory (LSTM) networks have demonstrated state-of-the-art performance for rainfall-runoff hydrological modelling. However, most studies focus on predictions at a daily scale, limiting the benefits of sub-daily (e.g. hourly) predictions in applications like flood forecasting. Moreover, training an LSTM network exclusively on sub-daily data is computationally expensive and may lead to model learning difficulties due to the extended sequence lengths. In this study, we introduce a new architecture, multi-frequency LSTM (MF-LSTM), designed to use input of various temporal frequencies to produce sub-daily (e.g. hourly) predictions at a moderate computational cost. Building on two existing methods previously proposed by the co-authors of this study, MF-LSTM processes older inputs at coarser temporal resolutions than more recent ones. MF-LSTM gives the possibility of handling different temporal frequencies, with different numbers of input dimensions, in a single LSTM cell, enhancing the generality and simplicity of use. Our experiments, conducted on 516 basins from the CAMELS-US dataset, demonstrate that MF-LSTM retains state-of-the-art performance while offering a simpler design. Moreover, the MF-LSTM architecture reported a 5 times reduction in processing time compared to models trained exclusively on hourly data.</p>https://hess.copernicus.org/articles/29/1749/2025/hess-29-1749-2025.pdf |
| spellingShingle | E. Acuña Espinoza F. Kratzert D. Klotz D. Klotz M. Gauch M. Álvarez Chaves R. Loritz U. Ehret Technical note: An approach for handling multiple temporal frequencies with different input dimensions using a single LSTM cell Hydrology and Earth System Sciences |
| title | Technical note: An approach for handling multiple temporal frequencies with different input dimensions using a single LSTM cell |
| title_full | Technical note: An approach for handling multiple temporal frequencies with different input dimensions using a single LSTM cell |
| title_fullStr | Technical note: An approach for handling multiple temporal frequencies with different input dimensions using a single LSTM cell |
| title_full_unstemmed | Technical note: An approach for handling multiple temporal frequencies with different input dimensions using a single LSTM cell |
| title_short | Technical note: An approach for handling multiple temporal frequencies with different input dimensions using a single LSTM cell |
| title_sort | technical note an approach for handling multiple temporal frequencies with different input dimensions using a single lstm cell |
| url | https://hess.copernicus.org/articles/29/1749/2025/hess-29-1749-2025.pdf |
| work_keys_str_mv | AT eacunaespinoza technicalnoteanapproachforhandlingmultipletemporalfrequencieswithdifferentinputdimensionsusingasinglelstmcell AT fkratzert technicalnoteanapproachforhandlingmultipletemporalfrequencieswithdifferentinputdimensionsusingasinglelstmcell AT dklotz technicalnoteanapproachforhandlingmultipletemporalfrequencieswithdifferentinputdimensionsusingasinglelstmcell AT dklotz technicalnoteanapproachforhandlingmultipletemporalfrequencieswithdifferentinputdimensionsusingasinglelstmcell AT mgauch technicalnoteanapproachforhandlingmultipletemporalfrequencieswithdifferentinputdimensionsusingasinglelstmcell AT malvarezchaves technicalnoteanapproachforhandlingmultipletemporalfrequencieswithdifferentinputdimensionsusingasinglelstmcell AT rloritz technicalnoteanapproachforhandlingmultipletemporalfrequencieswithdifferentinputdimensionsusingasinglelstmcell AT uehret technicalnoteanapproachforhandlingmultipletemporalfrequencieswithdifferentinputdimensionsusingasinglelstmcell |