Stochastic approach to determination of suspended sediment concentration in tidal rivers by artificial neural network and genetic algorithm
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Notice bibliographique
Résumé
Because of the interaction between tidal and fluvial flows in tidal rivers, sampling and measurement of suspended sediment concentration is very complex. Determination of suspended sediment concentration in tidal rivers is a very important problem in some countries such as Canada and United Kingdom (UK) (for example Bay of Fundy in Canada and Bristol Channel in UK). A numerical model cannot show suspended sediment concentration in tidal river accurately. Fluvial flows bring sand and gravel particles from the watershed, while tidal flow brings silt particles from the sea in flood time and returns them to the sea in ebb time. Interaction between tidal and fluvial flows, relation between suspended sediment concentration and return periods of them, correction of suspended sediment distribution coefficient for use in tidal limit of rivers, finding the best method for determination of suspended sediment concentration in tidal limit of rivers and optimization of it are major difficulties and challenges for determination of suspended sediment concentration. For overcoming these challenges in this research, a perceptron artificial neural network is trained and validated by observed data. For training of the artificial neural network (ANN), Levenberg–Marquardt training method is applied. For decreasing of the mean square error (MSE) and increasing of efficiency coefficient, parameters of ANN are optimized by genetic algorithm (GA) method. The GA method optimizes the number of nodes of hidden layers of ANN that is trained by Levenberg–Marquardt training method. Two sets of data are introduced into a network. Inputs of first network are distance from upstream of river, flood return period, and tide return period. These return periods are determined by observed data and governing stochastic distribution on them. Inputs of second network are distance from upstream of river, flood discharge, and ebb height. Output of these networks is suspended sediment concentration. Observed data show that maximum suspended sediment concentration is concerned with ebb that tidal flow and fluvial flow are in one direction. Because of a shortage of observed data especially in extreme conditions, a numerical model was developed. This model was calibrated by observed data. Results of numerical model convert to two regression relations. These relations are functions of distance from the upstream of river, discharge of flood (or flood return period) at upstream, and ebb height (or ebb return period) at downstream. Then the artificial neural network is tested with the remainder of observed data and results of the numerical model. Sensitive analysis shows that distance from the upstream of river and flood discharge are the most effective governing factors on suspended sediment concentration in first and second network, respectively. For the case study, the Karun River in south west of Iran is considered. This river is the most important tidal river in Iran.
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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 | 0,000 |
| 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