Exploratory analysis of water quality in a small urbanized watershed using deep learning
Notice bibliographique
Résumé
Water is a life-sustaining resource for living organisms inside and outside water bodies. Natural waters serve as municipal and industrial water supplies, sources for agricultural irrigation, homes for aquatic ecosystems, recreation, and other essential uses. The quality of water determines its use. Therefore, it must be monitored, managed, and reported to help stakeholders in decision-making that can protect watershed ecosystems and improve measures to mitigate factors adversely affecting water bodies. Water quality is represented by a set of parameters that describe specific characteristics or properties of water. These parameters are determined by measuring water's physical and chemical characteristics and concentration levels of various substances in a water column with subsequent sample analysis in laboratories. This results in low frequencies of observations for water quality parameters compared to hydrometric and meteorological data. Frequencies of observation adopted by many water quality monitoring systems vary between 4 and 12 samples per year, suggesting applying modelling techniques to support decision-making. \nThe study aims to develop a data-driven computational tool for water quality modelling in a small, highly urbanized watershed of the Don River, Ontario, Canada. The study focuses on major ions, namely, cations: calcium (Ca2+), magnesium (Mg2+), sodium (Na+), and potassium (K+), and anions such as bicarbonate (HCO3-), carbonate (CO32-), chloride (Cl-), and sulphate (SO42-). These parameters are not affected significantly by the aquatic ecosystem. The hydrological and meteorological processes mainly determine their dynamics. The study uses data from different monitoring systems belonging to the Toronto and Region Conservation Authority (TRCA) and Environment and Climate Change Canada (ECCC). It consists of water quality parameters and hydrometric and meteorological characteristics observed in the watershed over 57 years. Concentrations of selected water quality parameters are modelled using deep neural networks. The data pre-processing framework for cleansing and integrating data observed at different frequencies from different locations is developed. The framework is applied for the comparative analysis of neural networks of various configurations.\nTwo sets of computational experiments were conducted. In the first set of experiments, integrated data from all monitoring stations in the watershed was fed into the deep learning algorithms to train a neural network to predict the concentration of major ions for the upcoming month (t+1). The second set of experiments uses upstream environmental parameters to train the model and predict the major ion concentrations in the lower subwatershed. The study investigates the performance of developed models in accurately predicting ion concentrations and provides insights into the relationship between environmental factors and water quality in the investigated watershed. The findings have practical applications for water resource management and pollution prevention efforts.
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Comment cette classification a été obtenuedéplier
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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| 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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, 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 ».