Untangling hybrid hydrological models with explainable artificial intelligence
Hydrological models are valuable tools for developing streamflow predictions in unmonitored catchments to increase our understanding of hydrological processes. A recent effort has been made in the development of hybrid (conceptual/machine learning) models that can preserve some of the hydrological p...
Guardado en:
| Autores principales: | , , |
|---|---|
| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
IWA Publishing
2021
|
| Materias: | |
| Acceso en línea: | https://doaj.org/article/1fa6d8abbab3463a85b7d216edde8b9f |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| id |
oai:doaj.org-article:1fa6d8abbab3463a85b7d216edde8b9f |
|---|---|
| record_format |
dspace |
| spelling |
oai:doaj.org-article:1fa6d8abbab3463a85b7d216edde8b9f2021-11-08T07:59:15ZUntangling hybrid hydrological models with explainable artificial intelligence2616-651810.2166/h2oj.2021.066https://doaj.org/article/1fa6d8abbab3463a85b7d216edde8b9f2021-01-01T00:00:00Zhttp://doi.org/10.2166/h2oj.2021.066https://doaj.org/toc/2616-6518Hydrological models are valuable tools for developing streamflow predictions in unmonitored catchments to increase our understanding of hydrological processes. A recent effort has been made in the development of hybrid (conceptual/machine learning) models that can preserve some of the hydrological processes represented by conceptual models and can improve streamflow predictions. However, these studies have not explored how the data-driven component of hybrid models resolved runoff routing. In this study, explainable artificial intelligence (XAI) techniques are used to turn a ‘black-box’ model into a ‘glass box’ model. The hybrid models reduced the root-mean-square error of the simulated streamflow values by approximately 27, 50, and 24% for stations 17120000, 27380000, and 33680000, respectively, relative to the traditional method. XAI techniques helped unveil the importance of accounting for soil moisture in hydrological models. Differing from purely data-driven hydrological models, the inclusion of the production storage in the proposed hybrid model, which is responsible for estimating the water balance, reduced the short- and long-term dependencies of input variables for streamflow prediction. In addition, soil moisture controlled water percolation, which was the main predictor of streamflow. This finding is because soil moisture controls the underlying mechanisms of groundwater flow into river streams. Highlights A machine learning model is coupled to the GR4J hydrological model.; The hybrid hydrological model consists of a single soil moisture accounting storage.; The performance improvement is significant under low-flow conditions.; Explainable artificial intelligence techniques are used for the global and local interpretation of the data-driven component of the model.;Daniel AlthoffHelizani Couto BazameJessica Garcia NascimentoIWA Publishingarticlegr4jindividual conditional explanationslimepartial dependence profilesregression treesRiver, lake, and water-supply engineering (General)TC401-506Water supply for domestic and industrial purposesTD201-500ENH2Open Journal, Vol 4, Iss 1, Pp 13-28 (2021) |
| institution |
DOAJ |
| collection |
DOAJ |
| language |
EN |
| topic |
gr4j individual conditional explanations lime partial dependence profiles regression trees River, lake, and water-supply engineering (General) TC401-506 Water supply for domestic and industrial purposes TD201-500 |
| spellingShingle |
gr4j individual conditional explanations lime partial dependence profiles regression trees River, lake, and water-supply engineering (General) TC401-506 Water supply for domestic and industrial purposes TD201-500 Daniel Althoff Helizani Couto Bazame Jessica Garcia Nascimento Untangling hybrid hydrological models with explainable artificial intelligence |
| description |
Hydrological models are valuable tools for developing streamflow predictions in unmonitored catchments to increase our understanding of hydrological processes. A recent effort has been made in the development of hybrid (conceptual/machine learning) models that can preserve some of the hydrological processes represented by conceptual models and can improve streamflow predictions. However, these studies have not explored how the data-driven component of hybrid models resolved runoff routing. In this study, explainable artificial intelligence (XAI) techniques are used to turn a ‘black-box’ model into a ‘glass box’ model. The hybrid models reduced the root-mean-square error of the simulated streamflow values by approximately 27, 50, and 24% for stations 17120000, 27380000, and 33680000, respectively, relative to the traditional method. XAI techniques helped unveil the importance of accounting for soil moisture in hydrological models. Differing from purely data-driven hydrological models, the inclusion of the production storage in the proposed hybrid model, which is responsible for estimating the water balance, reduced the short- and long-term dependencies of input variables for streamflow prediction. In addition, soil moisture controlled water percolation, which was the main predictor of streamflow. This finding is because soil moisture controls the underlying mechanisms of groundwater flow into river streams. Highlights
A machine learning model is coupled to the GR4J hydrological model.;
The hybrid hydrological model consists of a single soil moisture accounting storage.;
The performance improvement is significant under low-flow conditions.;
Explainable artificial intelligence techniques are used for the global and local interpretation of the data-driven component of the model.; |
| format |
article |
| author |
Daniel Althoff Helizani Couto Bazame Jessica Garcia Nascimento |
| author_facet |
Daniel Althoff Helizani Couto Bazame Jessica Garcia Nascimento |
| author_sort |
Daniel Althoff |
| title |
Untangling hybrid hydrological models with explainable artificial intelligence |
| title_short |
Untangling hybrid hydrological models with explainable artificial intelligence |
| title_full |
Untangling hybrid hydrological models with explainable artificial intelligence |
| title_fullStr |
Untangling hybrid hydrological models with explainable artificial intelligence |
| title_full_unstemmed |
Untangling hybrid hydrological models with explainable artificial intelligence |
| title_sort |
untangling hybrid hydrological models with explainable artificial intelligence |
| publisher |
IWA Publishing |
| publishDate |
2021 |
| url |
https://doaj.org/article/1fa6d8abbab3463a85b7d216edde8b9f |
| work_keys_str_mv |
AT danielalthoff untanglinghybridhydrologicalmodelswithexplainableartificialintelligence AT helizanicoutobazame untanglinghybridhydrologicalmodelswithexplainableartificialintelligence AT jessicagarcianascimento untanglinghybridhydrologicalmodelswithexplainableartificialintelligence |
| _version_ |
1718442784500744192 |