From virtual environment to real observations: short-term hydrological forecasts with an Artificial Neural Network model.
Notice bibliographique
Résumé
Machine learning model approaches for hydrological forecasts are nowadays common in research. Artificial Neural Network (ANN) is one of the most popular due to its good performance on watersheds with different hydrologic regimes and over several timescales. A short-term (1 to 7 days ahead) forecast model was explored to predict streamflow. This study focused on the summer season defined from May to October. Cross-validation was done over a period of 16 years, each time keeping a single year as a validation set. The ANN model was parameterized with a single hidden layer of 6 neurons. It was developed in a virtual environment based on datasets generated by the physically based distributed hydrological model Hydrotel (Fortin et al., 2012). In a preliminary analysis, several combinations of inputs were assessed, the best combining precipitation and temperature with surface soil moisture and antecedent streamflow. Different spatial discretizations were compared. A semi-distributed discretization was selected to facilitate transferring the ANN model from a virtual environment to real observations such as remote sensing soil moisture products or ground station time series. Four watersheds were under study: the Au Saumon and Magog watersheds located in south Québec (Canada); the Androscoggin watershed in Maine (USA); and the Susquehanna watershed located in New-York and Pennsylvania (USA). All but the Susquehanna watershed are mainly forested, while the latter has a 57% forest cover. To evaluate whether a model with a data-driven structure can mimic a deterministic model, ANN and Hydrotel simulated flows were compared. Results confirm that the ANN model can reproduce streamflow output from Hydrotel with confidence. Soil moisture observation stations were deployed in the Au Saumon and Magog watersheds during the summers 2018 to 2021. Meteorological data were extracted from the ERA5-Land reanalysis dataset. As the period of availability of observed data is short, the ANN model was trained in a virtual environment. Two validations were done: one in the virtual environment and one using real soil moisture observations and flows. The number and locations of the soil moisture probes slightly differed during each of the four summers. Therefore, four models were trained depending on the number of probes and their location. Results highlight that location of the soil moisture probes has a large influence on the ANN streamflow outputs and identifies more representative sub-regions of the watershed. The use of remote sensing data as inputs of the ANN model is promising. Soil moisture datasets from SMOS and SMAP missions are available for the four watersheds under study, although downscaling approaches should be applied to bring the spatial resolution of those products at the watershed scale. One other future lead could be the development of a semi-distributed ANN model in virtual environment based on a restricted selection of hydrological units based on physiographic characteristics. The future L-band NiSAR product could be relevant for this purpose, having a finer spatial resolution compared to SMAP and SMOS and a better penetration of the signal in forested areas than C-band SAR satellites such as Sentinel-1 and the Radarsat Constellation Mission.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».