Neural networks modelling of stream nitrogen using remote sensing information: model development and application
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Notice bibliographique
Résumé
In remotely located forest watersheds, monitoring nitrogen (N) in streams often is not feasible because of the high costs and site inaccessibility. Therefore, modelling tools that can predict N in unmonitored watersheds are urgently needed to support management decisions for these watersheds. Recently, remote sensing (RS) has become a cost-efficient way to evaluate watershed characteristics and obtain model input variables. This study was to develop an artificial neural network (ANN) modelling tool relying solely on public domain climate data and satellite data without ground-based measurements. ANN was successfully applied to simulate N compositions in streams at studied watersheds by using easily accessible input variables, relevant time-lagged inputs and inputs reflecting seasonal cycles. This study was the first effort to take the consideration of vegetation dynamics into N modelling by using RS-derived enhanced vegetation index (EVI) that was capable of describing the differences of vegetation canopy and vegetation dynamics among watersheds. As a further study to demonstrate the applicability of the ANN models to unmonitored watersheds, the calibrated ANN models were used to predict N in other different watersheds (unmonitored watersheds in this perspective) without further calibration. A watershed similarity index was found to show high correlation with the transferability of the models and can potentially guide transferring the trained models into similar unmonitored watersheds. Finally, a framework to incorporate water quantity/quality modelling into forestry management was proposed to demonstrate the application of the developed models to support decision making. The major components of the framework include watershed delineation and classification, database and model development, and scenario-based analysis. The results of scenario analysis can be used to translate vegetation cut into values of EVI that can be fed to the models to predict changes in water quality (e.g. N) in response to harvesting scenarios. The results from this research demonstrated the applicability of ANNs for stream N modelling using easily accessible data, the effectiveness of RS-derived EVI in N model construction, and the transferability of the ANN models. The presented models have high potential to be used to predict N in streams in the real-world and serve forestry management.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| 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 | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 découle